DESC:TORQUE-BASED SPEED CONTROL SYSTEM FOR AN ELECTRIC VEHICLE
Field of the Invention
The present disclosure generally relates to electric vehicle systems. Further, the present disclosure particularly relates to a system to control a speed of an electric vehicle.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Electric vehicles have witnessed widespread adoption due to their advantages in terms of energy efficiency and environmental sustainability. Such vehicles typically include components such as electric drive units, battery packs, and control units that enable various operational capabilities. Among these, speed control remains a critical parameter influencing driving performance and energy utilization. Speed control strategies generally fall into two broad categories, namely current control-based strategies and torque control-based strategies.
Conventional methods employing current control for speed regulation suffer from performance limitations due to the highly fluctuating nature of electrical current. Variations in current during vehicle operation lead to difficulty in maintaining consistent vehicle speed, thereby affecting drive quality. Moreover, torque control strategies have been employed to provide smoother vehicle operation and to address limitations associated with current-based control. However, commonly known torque control strategies fail to optimize energy consumption effectively, resulting in non-ideal usage of battery power and reduced vehicle range.
A commonly known technique for controlling speed in electric vehicles involves the use of proportional-integral-derivative controllers. Such controllers are utilised due to the straightforward implementation and tuning processes. However, parameter tuning in proportional-integral-derivative controllers becomes increasingly difficult under varying road conditions and load variations. Further, such controllers fail to handle the nonlinearities present in electric vehicle dynamics. The limitations in dynamic response and adaptability of such controllers result in non-optimal energy usage and poor torque response during real-world driving scenarios.
Another known technique used in electric vehicle speed control is based on model predictive control strategies. Such strategies utilize predictive models to estimate future behaviour and compute control inputs that minimize cost functions. The benefit of model predictive control lies in its capability to handle constraints associated with torque, speed and other variables. However, the practical implementation of such strategies requires computationally intensive resources. Further, model accuracy plays a significant role in the performance of model predictive control techniques. Deviations in model parameters due to external disturbances or component aging lead to inefficiencies in speed regulation and increased battery consumption.
Fuzzy logic controllers have also been implemented in various electric vehicle control applications. Such controllers emulate human-like reasoning to deal with system uncertainties and nonlinearities. Despite offering robustness in handling variable driving conditions, fuzzy logic controllers require extensive rule definition and membership function tuning. The performance of such controllers is heavily reliant on the quality of the control rules defined, which introduces subjectivity and non-repeatability in the control design process. Development and testing of such controllers require considerable effort and engineering time.
Other speed control strategies have been developed using various optimization-based techniques and hybrid controllers. However, each of such strategies introduces certain limitations. Some strategies depend on offline training or pre-characterised models, which lack adaptability to real-time changes in driving conditions. Some techniques involve complex tuning and calibration requirements which reduce scalability and increase implementation cost. Further, integration of such techniques into existing electric vehicle control units often demands hardware modification or software restructuring.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for achieving optimal speed control in electric vehicles while considering torque constraints.
Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
An objective of the present disclosure is to enable speed control in electric vehicles through torque-based regulation while considering physical constraints and varying operational conditions. Another objective of the present disclosure is to utilize real-time computation and feedback-based adjustment of torque to achieve controlled acceleration and deceleration that are responsive to user inputs and environmental resistances.
In an aspect, the present disclosure provides a system to control a speed of an electric vehicle including a speed sensing unit operatively coupled to a traction motor and a motor control unit such that the speed sensing unit detects an actual motor speed. The system also includes an input interface and a vehicle control unit in communication with the speed sensing unit and the input interface. The input interface is configured to receive a driving mode, at least one of a desired speed associated with a throttle input signal or a braking force input. The vehicle control unit receives the detected actual motor speed, a mass of the electric vehicle, and at least one of the driving mode and the desired speed. Thereafter, the vehicle control unit computes a desired torque value using a discrete-time longitudinal dynamic model as a function of the mass of the electric vehicle, the detected actual motor speed, the braking force input, a rolling resistance force, a driving surface characteristic, an aerodynamic drag force, an inertial resistive force, a surface gradient, the desired speed, and the driving mode. Next, the vehicle control unit applies a constrained optimization protocol on the desired torque value based on at least one of a predefined maximum torque limit, a predefined minimum torque limit, a predefined maximum torque rate, and a predefined minimum torque rate. Consequently, the vehicle control unit generates a demanded torque based on application of the constrained optimization protocol. A motor control unit is in communication with the vehicle control unit and the traction motor. The motor control unit receives the demanded torque from the vehicle control unit and actuates the traction motor to generate the demanded torque to control the speed of the electric vehicle.
Furthermore, such a system enables computation of torque values based on physical models of the vehicle and input conditions. Moreover, such a system enhances control by managing torque within safety and efficiency constraints.
In certain implementations of the disclosure, the vehicle control unit receives the mass of the electric vehicle as an output value from a load detector or as an output value of a mass estimation algorithm. Moreover, such a feature enables adaptability of the torque computation based on real-time vehicle load variations.
In certain other implementations of the disclosure, the vehicle control unit regulates at least one of the predefined maximum torque limit the predefined minimum torque limit the predefined maximum torque rate and the minimum torque rate limit based on at least one of an energy consumption level a motor efficiency a driving condition the detected mass or the detected load. Furthermore, such regulation enables alignment of torque boundaries with operational parameters to optimise performance.
In certain other implementations of the disclosure, the driving mode is selected from eco-mode ride mode and rush mode and each driving mode is associated with a predefined desired target speed. Moreover, such selection enables dynamic adjustment of performance characteristics based on user preference.
In certain other implementations of the disclosure, Further the vehicle control unit adjusts at least one of the predefined torque limit and the predefined torque rate limit based on a deviation between a first acceleration derived from a time-series analysis of an actual motor speed and a second acceleration computed using an initial value of the detected mass and a resultant force inferred from the load detection sensor. Furthermore, such adjustment enables enhanced responsiveness in control strategy by detecting inconsistencies in estimated versus actual performance.
In certain other implementations of the disclosure, the vehicle control unit modulates the desired target speed based on a route profile such that the route profile comprises a road segment curvature an urban density index and a speed zone classification. Moreover, such modulation enables contextual speed control based on environmental constraints.
In certain implementations of the disclosure, the vehicle control unit modifies the desired torque value for a regenerative braking event or a regenerative coasting event by utilizing a reverse polarity coefficient or a reverse polarity map. Furthermore, such modification enables energy recovery operations during deceleration scenarios.
Further the reverse polarity coefficient is scaled based on a state of charge of a battery and/or a back electromotive force of the traction motor. Moreover, such scaling enables safe and effective energy recovery without violating system constraints.
In certain other implementations of the disclosure, the vehicle control unit monitors a cumulative demanded torque profile over a predefined time interval and constraints the demanded torque when the cumulative demanded torque profile exceeds a thermal operating threshold associated with the traction motor. Furthermore, such constraint enables protection of the traction motor from thermal overloads.
Further the motor control unit applies a phase-interleaved torque command to the traction motor such that the phase-interleaved torque command is associated with a sequentially timed actuation of multiple motor phases to minimize the torque harmonics. Moreover, such command application enables smoother torque delivery and reduced vibration during vehicle operation.
Brief Description of the Drawings
The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a system to control a speed of an electric vehicle (EV), in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a method for controlling a speed of an electric vehicle (EV), in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates a decision-based constrained optimization protocol flowchart, in accordance with the embodiments of the present disclosure.
FIG. 4 illustrates a signal flow diagram for vehicle speed regulation using a torque-based control strategy, in accordance with the embodiments of the present disclosure.
Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
As used herein, the term "system" refers to a combination of interconnected components or devices functioning together for a defined operational purpose. A system comprises physical or digital units that interact through mechanical, electrical, or computational interfaces. In transport or control applications, a system may govern variables such as torque, speed, or energy delivery. A system may include control units, processing units, actuators, and sensors operating in coordination. Communication within the system may be enabled through embedded buses including CAN, SPI, or Ethernet. For example, a vehicular control system may incorporate an engine control unit, speed sensor, and a user interface. The system may operate using real-time feedback from sensors to regulate motion or performance. Software embedded within the system may include rule-based control logic or numerical computation frameworks. A system may operate autonomously, semi-autonomously, or based on external triggers. Physical integration of system components may be distributed or centralised based on application and design requirements.
As used herein, the term "speed sensing unit" refers to a sensing component that detects or calculates the rotational or linear speed of a mechanical element. The speed sensing unit may use sensing techniques including Hall-effect sensing, magnetic pulse counting, inductive sensing, optical encoding, inertial measurement, or satellite-based position sensing system. For example, a Hall-effect sensor may detect changes in magnetic fields as a rotating object passes a fixed sensor. An optical encoder may use a perforated disc and a light receiver to determine shaft speed. An inertial measurement unit (IMU) may calculate speed by integrating acceleration values over time along one or more axes. A Global Positioning System (GPS) receiver may drive vehicle speed by calculating the rate of change of position data obtained from satellite signals. The speed sensing unit may output analog or digital signals corresponding to real-time speed values. In vehicular systems, the speed sensing unit may be installed near rotating shafts, motor housings, wheels, or may include standalone IMU or GPS modules mounted on the vehicle body. Output from the speed sensing unit may be used in feedback loops for speed regulation or torque computation. Signal processing circuits may be associated with the speed sensing unit to convert raw sensor data into interpretable speed parameters. Calibration and fault detection features may also be incorporated.
As used herein, the term "traction motor" refers to an electromechanical device that transforms electrical input into rotational mechanical output used to propel a vehicle. The traction motor may be of the type selected from induction motors, permanent magnet synchronous motors, brushless direct current motors, or switched reluctance motors. Operation of the traction motor involves electrical excitation of stator windings and interaction with the rotor to produce rotational force. Control of the traction motor is achieved by managing voltage, current, and frequency supplied to the windings through an inverter or motor driver. For instance, a three-phase alternating current signal may be modulated to control torque output. The traction motor may also support regenerative operation by converting kinetic energy into electrical energy during deceleration. The mechanical output of the traction motor may be transferred to the vehicle drivetrain, which may include gear systems or differential assemblies. Sensors may be associated with the traction motor to monitor parameters such as rotor position, temperature, and speed.
As used herein, the term "motor control unit" refers to an electronic device or sub-system that manages the operation of an electric motor by processing control inputs and generating corresponding electrical excitation signals. The motor control unit performs tasks such as voltage modulation, phase commutation, and real-time current control. Algorithms executed by the motor control unit may include pulse width modulation, field-oriented control, or direct torque control. The motor control unit receives signals representing demanded torque, speed, or current and applies these to the motor in accordance with system requirements. For example, in a brushless motor application, the motor control unit may adjust the phase and amplitude of current supplied to the stator windings to achieve precise rotor positioning. Communication between the motor control unit and other components such as a vehicle control unit may be established via digital communication protocols. Thermal protection and feedback monitoring mechanisms may also be integrated within the motor control unit.
As used herein, the term "input interface" refers to a hardware or software entity that facilitates the reception of control inputs from a user, environment, or external system (including, but not limited to, an electronic device and/or a smartphone or an application installed over the electronic device and/or the smartphone). The input interface may capture physical signals such as throttle position, brake pedal force, or mode selection and convert them into digital or analog signals for use by processing units. Examples of input interface elements include potentiometers, resistive sensors, digital buttons, touchscreen panels, and wireless input receivers. For instance, a throttle signal may be received as a variable voltage corresponding to pedal depression. The input interface may also receive sensor data such as accelerometer readings or load values. Signal conditioning, filtering, and analog-to-digital conversion may occur within or in conjunction with the input interface. In a vehicle, the input interface acts as the gateway for user or sensor input to influence system control parameters such as speed or torque.
As used herein, the term "vehicle control unit" refers to a computational unit that governs control logic for vehicle parameters based on input from sensors, user interfaces, and other subsystems. The vehicle control unit executes mathematical and logical operations to compute control commands such as torque requests, speed targets, and energy budgets. For example, the vehicle control unit may evaluate a dynamic model of the vehicle and determine the necessary torque output based on detected mass, actual speed, and environmental resistive forces. Inputs to the vehicle control unit may include data from speed sensors, load detectors, and user input signals. Outputs from the vehicle control unit may be transmitted to actuator control devices such as motor control units. Processing within the vehicle control unit may be performed using embedded microcontrollers or digital signal processors executing structured software code. Control cycles may be executed periodically based on sampling intervals. Memory resources within the vehicle control unit may store operational data and calibration values.
As used herein, the term "discrete-time longitudinal dynamic model" refers to a time-stepped mathematical representation of the forward-backward motion characteristics of a vehicle. The discrete-time longitudinal dynamic model expresses forces and resulting accelerations or velocities in a format suitable for computation over finite time intervals. Input parameters to the model may include vehicle mass, road gradient, rolling resistance, aerodynamic drag, and external driving forces. For example, the model may use Newton’s second law to relate the net longitudinal force to vehicle acceleration, taking into account resistive forces and desired speed change. The discrete-time nature of the model allows implementation on digital control systems where continuous equations are approximated using difference equations. The model may be used to calculate desired torque values required to achieve a specific speed profile under defined load and environmental conditions.
As used herein, the term "constrained optimization" refers to a mathematical approach that determines the most suitable solution for a specific control objective while assuring that certain predefined limits or boundaries are not violated. In control applications, constrained optimization is used to compute values such as torque or energy output while observing operational restrictions like maximum or minimum limits, rate of change, or thermal boundaries. For example, when computing a desired torque, the optimization process makes sure that the torque does not exceed the capability of traction motor or the safety threshold of system. Techniques for constrained optimization include linear programming, quadratic programming, and rule-based evaluations. The problem may be defined by an objective function representing the desired system performance and a set of constraints defining allowable operational ranges. The outcome of constrained optimization is a control value that satisfies both the objective and all applicable constraints. Such a process is integral to modern embedded systems requiring real-time decision-making under defined boundaries.
As used herein, the term "generate" refers to the act of producing a control output, signal, or quantity based on processed input data or computed values. In control systems, “generate” may involve forming a torque command or speed instruction from internal computations. For example, after computing the desired torque using a dynamic model and applying constraints, a final torque demand signal is generated for execution by an actuator. Generation may involve mathematical transformation, scaling, discretisation, or formatting of data to match interface specifications. The generated output may be transmitted to hardware controllers or stored in memory for subsequent use. Generation operations may occur continuously or periodically in response to changes in input conditions or control loop iterations. In embedded systems, generate may be implemented through software routines or dedicated digital logic. The generated value serves as a final control reference that drives actuation mechanisms such as motors or valves to achieve the intended system behaviour.
As used herein, the term "receive" refers to the act of acquiring data, signals, or information from another component, device, or communication channel. In an embedded or control system, “receive” operations may include capturing analog signals, polling digital inputs, or reading messages from serial communication buses. For instance, a control unit may receive speed data from a speed sensor or a mode command from an input device. Reception may involve signal processing tasks such as decoding, filtering, or validation of the incoming data. Communication protocols such as CAN, I2C, or UART may facilitate structured data reception. In real-time systems, receive operations are scheduled within control loops or triggered by interrupts. The received data is typically stored in buffers or registers for processing by the system. Accurate and timely receive functionality is essential to maintain synchronisation and responsiveness within a closed-loop control framework. Received information may include sensor readings, command inputs, or status flags from distributed components.
As used herein, the term "compute" refers to the execution of mathematical or logical operations to derive a value, output, or decision based on provided inputs. In vehicular control systems, compute operations are used to calculate torque requirements, predict system responses, or adjust operating points. For example, the vehicle control unit may compute the required propulsion torque using input variables such as speed, load, gradient, and desired acceleration. The computational process may involve difference equations, polynomial evaluations, or rule-based expressions. Compute may be implemented through software code executed by microcontrollers or digital processors. Timing and sequencing of compute steps may be synchronised with control cycles to enable timely decision-making. Computed values may be immediately used for control or stored for diagnostic analysis. Compute operations may also be part of higher-order functions such as state estimation, optimisation, or feedback correction. Mathematical libraries and numerical solvers may support computation of values across varied scenarios.
As used herein, the term "actuate" refers to the process of applying an output command to an electromechanical device to produce a mechanical response such as motion or force. In electric vehicles, actuate typically involves sending control signals to a traction motor to generate a required torque output. For example, a torque command generated by a control unit may be translated into phase voltage signals by the motor control unit to actuate the motor. The actuation process may include converting digital values into pulse-width modulated signals or varying the amplitude and frequency of voltage waveforms. Actuation may be closed-loop, where the output response is continuously monitored, or open-loop, where the control signal is applied based on predefined settings. Parameters influencing actuation include signal resolution, motor characteristics, and power electronics constraints. Timing and accuracy of actuation directly affect system stability and responsiveness. Safety and diagnostic features may be included to detect failures or abnormal conditions during actuation.
FIG. 1 illustrates a system (100) to control a speed of an electric vehicle (EV), in accordance with the embodiments of the present disclosure. The system (100) comprises a plurality of functional components including sensing, control, and actuation units configured to operate in coordination for the purpose of determining, regulating, and applying motor torque for vehicle propulsion under varying driving conditions. The system (100) includes a speed sensing unit (102) operatively coupled to a traction motor (104) and a motor control unit (106), such that the speed sensing unit (102) is adapted to detect an actual motor speed. The speed sensing unit (102) may be implemented using a wheel speed sensor, a magnetic sensor, a Hall-effect sensor, an optical encoder, or an inductive proximity sensor disposed in a manner enabling speed sensing unit (102) to monitor the rotational speed of the traction motor (104). The detected actual motor speed may be transmitted to the motor control unit (106) and to other associated control units over a data communication bus. The speed sensing unit (102) includes signal conditioning circuitry to convert raw sensor outputs into filtered, calibrated, and digitised speed data. For example, the speed sensing unit (102) may operate at a sampling rate suitable for high-speed control applications, such as in the range of 1 kHz to 10 kHz. The speed sensing unit (102) is integrated within the motor housing or mounted externally in proximity to the motor shaft or gear assembly. The speed data provided by the speed sensing unit (102) serves as a primary input for dynamic computations carried out by a vehicle control unit (110), described further herein.
In an embodiment, the input interface (108) is configured to receive a driving mode, at least one of a desired speed associated with a throttle input signal or a braking force input. The input interface (108) includes signal acquisition circuitry that receives analog or digital signals from sensors integrated with control elements such as a throttle pedal, rotational throttle grip or a driving mode selector. The throttle input signal is produced by a sensor such as a potentiometer or Hall-effect device mounted on the throttle pedal or the throttle grip, which generates a voltage proportional to the throttle position or the pedal position. The driving mode is selected through a physical switch, rotary knob, or touchscreen interface located on the vehicle dashboard, or alternately through a mobile application operating on an electronic device such as a smartphone. The driving mode options may include eco-mode, ride-mode, or rush-mode, each associated with predefined torque response behavior. The input interface (108) is further configured to receive wireless control input transmitted from the mobile application via Bluetooth, Wi-Fi, or other communication protocols. Through the mobile interface, a user selects the driving mode or enter a desired cruising speed, which is transmitted to the input interface (108) and relayed to the vehicle control unit (110) in real time. The input interface (108) also performs input validation, time-stamping, and buffering prior to transferring the data to the vehicle control unit (110) for use in torque computation and control logic.
In an embodiment, the input interface (108) is further configured to receive a braking force input corresponding to a deceleration request intended for braking-based regenerative control. The braking force input is generated using sensors such as a pressure transducer or force-sensitive resistor positioned within a brake pedal assembly or a hydraulic braking circuit. When a braking event is initiated by the user, the sensor measures the applied force, and the resulting signal is captured by the input interface (108). The braking force input is interpreted by the vehicle control unit (110) to determine the suitability and intensity of regenerative braking. When the force level falls within a predefined regenerative braking threshold, the vehicle control unit (110) calculates a negative torque command corresponding to a regeneration level, and transmits the command to the motor control unit (106). The motor control unit (106) actuates the traction motor (104) to operate in generator mode, thereby recovering kinetic energy and directing it to the battery or energy storage system. The input interface (108) continuously provides real-time braking force input throughout the braking event, allowing adaptive control of regenerative torque in accordance with user-applied force. In some embodiments, the braking force input is also received from a mobile application, where the user can define regenerative braking behavior via preset profiles or input sliders. Such digital inputs are transmitted to the input interface (108) and processed in the same manner as physical sensor signals, allowing the vehicle control unit (110) to manage braking-based regeneration under both manual and remote-control scenarios.
In an embodiment, the system (100) includes a vehicle control unit (110) in communication with the speed sensing unit (102) and the input interface (110), wherein the vehicle control unit (110) is configured to receive the detected actual motor speed, a mass of the EV, and at least one of the driving mode and the desired speed. The vehicle control unit (110) is implemented using a microprocessor, digital signal processor (DSP), field-programmable gate array (FPGA), or application-specific integrated circuit (ASIC) configured to execute real-time control routines. The mass of the EV is determined from a load detector or inferred using a mass estimation algorithm that utilizes vehicle dynamics and sensor feedback. The actual motor speed is received from the speed sensing unit (102), while the driving mode and desired speed are received via the input interface (110). The vehicle control unit (110) includes memory units for program storage and data logging, and implement lookup tables, calibration maps, and numerical models stored in flash or EEPROM. The vehicle control unit (110) operates on a control loop cycle ranging from 1 ms to 10 ms (selected from distinct ranges such as 1 ms to 3 ms, 4 ms to 7 ms and 8 ms to 10 ms) depending on system requirements. The vehicle control unit (110) assures time-coordinated processing of multiple input parameters and implements the primary computational logic for determining torque values necessary for achieving the desired operational behavior of the EV under various physical constraints and operational scenarios.
In an embodiment, the vehicle control unit (110) is further configured to compute a desired torque value using a discrete-time longitudinal dynamic model as a function of the mass of the EV, the detected actual motor speed, the braking force input, a rolling resistance force, a driving surface characteristic, an aerodynamic drag force, an inertial resistive force, a surface gradient, the desired speed, and the driving mode. The discrete-time longitudinal dynamic model is implemented using finite-difference equations derived from Newtonian mechanics, wherein the net longitudinal force equals the product of mass and acceleration. Each resistive force component is computed using standard physical relationships; for example, rolling resistance is determined as the product of a rolling resistance coefficient and vehicle weight, while aerodynamic drag is computed as a function of vehicle frontal area, drag coefficient, and square of velocity. The model takes as inputs the road gradient from an inclinometer or map database, and the driving surface characteristic from a sensor or predefined database. The computed torque value represents the net torque required at the motor shaft to overcome resistive forces and achieve a target acceleration or speed change. The torque computation is updated periodically in synchrony with control loop timing, and numerical stability is assured through the use of bounded finite-precision arithmetic and saturation logic. Intermediate variables and residuals are stored for diagnostic evaluation. The desired torque value serves as an input to subsequent constraint enforcement logic within the vehicle control unit (110).
In an embodiment, the vehicle control unit (110) is further configured to apply a constrained optimization protocol on the desired torque value based on at least one of a predefined maximum torque limit, a predefined minimum torque limit, a predefined maximum torque rate, and a predefined minimum torque rate. The constrained optimization involves limiting the computed desired torque to a range defined by hardware capabilities, thermal limits, or safety margins. The predefined maximum torque limit and minimum torque limit is stored in a calibration table that is indexed based on vehicle speed, temperature, or battery state-of-charge. The torque rate limits restricts how quickly the torque increases or decreases over successive control cycles, thereby preventing sudden jerks or oscillations. The enforcement of constraints is performed through clipping functions, rule-based adjustments, or predictive filters embedded in the control software. For example, the constraints may be dynamically modified based on system states such as motor temperature, road conditions, or user preferences. The constrained optimization process generates an adjusted torque value that satisfies all boundary conditions and transition smoothness requirements. The implementation is software-based using arithmetic operations or hardware-accelerated for reduced computation latency. Logs of constrained versus unconstrained torque values is maintained for analysis and refinement. The resulting constrained torque value is referred to as the demanded torque and is prepared for communication to the motor control unit (106) for actuation purposes.
In an embodiment, the vehicle control unit (110) is further configured to generate a demanded torque based on application of the constrained optimization protocol, wherein the demanded torque reflects a final torque command compliant with dynamic system limits and responsive to driver input. The demanded torque is represented as a digital value in Newton-meters and is formatted according to the communication protocol used between the vehicle control unit (110) and the motor control unit (106). The demanded torque is computed at a fixed control interval and includes time-stamping or indexing for traceability. Generation of the demanded torque includes filtering to smooth out abrupt variations caused by sensor noise or discrete signal transitions. In some embodiments, the demanded torque is further modified to accommodate energy recovery operations, load balancing, or driveability tuning. The generated demanded torque is retained in a register or memory buffer until successfully transmitted to the motor control unit (106). In multi-motor systems, the demanded torque is distributed among multiple traction motors based on configuration and load balancing logic. The demand generation logic is implemented as a distinct task within the vehicle control unit (110) and scheduled with high priority to maintain real-time responsiveness.
In an embodiment, the system (100) further includes a motor control unit (106) in communication with the vehicle control unit (110) and the traction motor (104), wherein the motor control unit (106) is configured to receive the demanded torque from the vehicle control unit (110). The motor control unit (106) receives the demanded torque over a communication link such as a CAN bus, SPI, UART, or Ethernet-based interface. Upon reception, the motor control unit (106) performs integrity checks such as cyclic redundancy check (CRC), timestamp validation, or message framing verification. The motor control unit (106) includes a processing core, memory, and power stage drivers configured to convert digital torque commands into motor actuation signals. The received demanded torque is scaled and translated into current references for field-oriented control (FOC) or space vector modulation (SVM) schemes. Sensor feedback such as rotor position or phase current is used in conjunction with the demanded torque to achieve closed-loop regulation of motor output. The motor control unit (106) applies thermal and electrical protection strategies during torque delivery and limits current or power if operational thresholds are exceeded. Diagnostic codes or acknowledgements are sent back to the vehicle control unit (110) upon successful command execution.
In an embodiment, the motor control unit (106) is further configured to actuate the traction motor (104) to generate the demanded torque to control the speed of the EV. Actuation includes generation of voltage waveforms across motor windings using power electronic components such as insulated-gate bipolar transistors (IGBTs) or metal-oxide-semiconductor field-effect transistors (MOSFETs) under the control of pulse width modulation (PWM) signals. The motor control unit (106) determines the phase voltage, current, and timing required to produce the desired torque from the traction motor (104) based on rotor position, magnetic field alignment, and electrical system constraints. Actuation logic operates within a tight control loop, such as 10–100 µs, to maintain precision in torque delivery. The actual motor torque is monitored using current sensors or estimated from control variables and used to correct deviations from the demanded torque. The resulting torque from the traction motor (104) is transmitted to the vehicle drivetrain, thereby modulating vehicle speed in accordance with driver inputs and system computations. The actuation process is continuously monitored for faults, and corrective actions are initiated in response to detected anomalies such as overcurrent, undervoltage, or loss of rotor synchronization.
In an exemplary aspect, the system (100) to control a speed of an electric vehicle (EV) is deployed in a compact urban commuter EV designed for intra-city operation with frequent stop-and-go traffic. In a scenario, a user selects an “eco-mode” via the input interface (110), intending to prioritise energy conservation and smooth acceleration. Upon receiving the eco-mode input, along with a desired speed based on throttle depression and minimal braking force, the vehicle control unit (110) retrieves the detected actual motor speed from the speed sensing unit (102), and the current vehicle mass obtained through a load detector integrated into the suspension system. The vehicle control unit (110) executes a discrete-time longitudinal dynamic model which incorporates the detected mass, road gradient derived from a GPS-linked inclinometer, surface friction conditions from a preloaded urban terrain map, and rolling resistance and aerodynamic drag estimations based on calibrated vehicle parameters. The model calculates a desired torque sufficient to overcome resistive forces and achieve the target speed in a gradual ramp profile compliant with eco-mode characteristics. The desired torque is then subjected to constrained optimization logic within the vehicle control unit (110), where torque is limited to a predefined maximum threshold suitable for low-energy drive states, and torque rate is restricted to prevent abrupt accelerations. The resulting demanded torque is generated and transmitted to the motor control unit (106), which actuates the traction motor (104) to deliver the required torque output. As a result, the EV accelerates in a controlled, energy-efficient manner, adhering to safety and comfort constraints throughout the operation.
In an embodiment, the vehicle control unit (110) may receive a value representative of the mass of the electric vehicle from a load detector or from a mass estimation algorithm executed by the vehicle control unit (110). The load detector may include one or more sensors positioned at structural elements of the electric vehicle, such as suspension arms or subframe interfaces. Such sensors may be implemented using strain gauges, pressure sensors, or piezoresistive elements. Electrical signals corresponding to local loads may be processed through a calibration function to derive an estimate of the total mass of the electric vehicle, accounting for passengers, luggage, or other payloads. Alternatively, a mass estimation algorithm may be employed, whereby the vehicle control unit (110) infers the effective mass by evaluating longitudinal vehicle dynamics, including a relationship between propulsion torque, acceleration, and resistive forces. In one scenario, when a torque of 200 Nm is applied and an acceleration of 1.6 m/s² is detected under known resistive loading, the inferred mass may be approximately 1250 kg. The mass value derived from either method may be time-stamped and stored for use in torque computation or constraint regulation processes. Such a mass value may be updated on a per-cycle basis or retained for longer time windows depending on system design. The use of either the load detector or the estimation algorithm may be selected based on hardware availability, system configuration, or application-specific accuracy requirements.
In an embodiment, the vehicle control unit (110) may regulate one or more of a predefined maximum torque limit, a predefined minimum torque limit, a predefined maximum torque rate, or a predefined minimum torque rate based on at least one parameter selected from an energy consumption level, a motor efficiency, a driving condition, a detected mass, or a detected load. Such regulation may be executed in real time and may be driven by feedback data or predetermined lookup tables. When an energy consumption level exceeds a calibrated threshold, such as 280 Wh/km, the maximum torque limit may be scaled down proportionally, for instance, from 250 Nm to 180 Nm. Similarly, when motor efficiency decreases due to increased thermal load or reduced voltage supply, torque rate values may be constrained to prevent excessive current draw. In another scenario, when the vehicle is operating under urban driving conditions involving frequent deceleration, the minimum torque rate may be reduced to prevent abrupt torque transitions. Load-dependent adjustments may also be applied, such that higher payloads result in upward adjustments of minimum torque to maintain traction on inclines. Data used for these adjustments may originate from sensors, estimators, or operating condition databases stored in the vehicle control unit (110). Regulatory adjustments may be executed at every control loop iteration and may involve filtering to suppress transients or sensor noise. No limitation is intended on the number or combination of input parameters used to determine the applicable torque or torque rate constraint values.
In an embodiment, the driving mode received at the input interface (108) may be selected from a set of predefined driving modes including an eco-mode, a ride mode, and a rush mode, wherein each mode may correspond to a predefined desired target speed and associated torque control characteristics. In eco-mode, the vehicle control unit (110) may restrict target speed to a lower value, such as 40 km/h, and enforce conservative torque application to prioritize energy conservation. Ride mode may represent a balanced state with moderate acceleration and cruising capabilities, supporting a target speed in the range of 60 km/h with a torque ceiling of approximately 200 Nm. In contrast, rush mode may enable higher acceleration rates and faster cruising speeds, such as 80 km/h, and allow application of peak torque values, for example, 300 Nm, to support rapid acceleration scenarios. Selection of the driving mode may be made manually by the user via a switch, graphical interface, or touchscreen input, or may be determined automatically based on route characteristics, user behavior, or energy availability. Mode selection may be confirmed to the driver through a visual or auditory cue. The associated speed and torque constraints may be retrieved from stored maps in the vehicle control unit (110) and applied during torque computation and constraint filtering processes. The selected driving mode may further affect regenerative braking profiles, response sensitivity, and driveability behavior across all operating phases of the vehicle.
In an embodiment, the vehicle control unit (110) may adjust at least one of the predefined torque limit or the predefined torque rate limit based on a deviation between a first acceleration derived from a time-series analysis of actual motor speed and a second acceleration computed using an initial value of the detected mass and a resultant force inferred from the load detection sensor. The first acceleration may be determined by evaluating motor speed variations across discrete time intervals using numerical differentiation or time-series filtering techniques. The second acceleration may be calculated from the dynamic response model using a measured torque input and the estimated or detected mass of the electric vehicle. The difference between the two computed accelerations may represent a discrepancy in modeled and actual vehicle dynamics, potentially due to inaccurate mass estimation, unaccounted road gradient, or transient resistive forces. When such a deviation exceeds a predefined threshold, the vehicle control unit (110) may respond by adjusting torque constraints to prevent instability or excessive actuator commands. For example, a 15% lower observed acceleration compared to the predicted value may result in a downward correction of the torque rate limit from 250 Nm/s to 180 Nm/s. Such adjustments may be executed using real-time correction factors, and may apply selectively to acceleration or deceleration phases. The comparison and constraint update logic may be embedded in a subroutine within the control software, executed periodically to affirm alignment between modeled and actual dynamics.
In an embodiment, the vehicle control unit (110) may modulate the desired target speed based on a route profile, wherein the route profile may contain data relating to road segment curvature, urban density index, and speed zone classification. Route profile information may be retrieved from a digital map database, a GPS module, or a real-time path planning system. Road segment curvature may be quantified based on radius of curvature or change in heading angle over a fixed distance. In cases where the curvature radius is below a predefined threshold, such as 50 meters, the target speed may be reduced to avoid unsafe lateral acceleration. The urban density index may be computed using known variables including intersection frequency, proximity to crosswalks, pedestrian traffic probability, and road type classification. A high-density index may trigger reductions in the target speed and modifications to acceleration profiles. Speed zone classification data, such as school zones or highway segments, may be extracted from road sign recognition or map annotations. Each data point within the route profile may influence the final target speed value used for torque computation. The vehicle control unit (110) may recalculate target speed dynamically as the route progresses and environmental factors change. Modulated speed values may be constrained by user preferences or global operational limits and may be smoothed to prevent oscillatory behavior in the drive system. Speed modulation based on route profile may support compliance with safety regulations and contribute to consistent vehicle behavior across variable road conditions.
In an embodiment, modulation of the desired target speed by the vehicle control unit (110) may not be limited to route profile data and may further be based on a driver or rider profile associated with the EV. The driver profile may include predefined or learned parameters representing user-specific driving characteristics such as preferred cruising speed, acceleration tendencies, braking patterns, and responsiveness to varying traffic conditions. Such a profile may be retrieved from onboard storage or received wirelessly from a connected application on a smartphone or another electronic device. The profile may be linked to a user account and may store historical data corresponding to multiple drive sessions. Based on the stored profile, the vehicle control unit (110) may adjust the target speed within bounds permitted by system constraints. A profile reflecting conservative driving behavior may lead to reduced target speeds in urban areas or on curved segments, whereas a profile indicating higher performance tolerance may permit comparatively elevated speeds. Modulation derived from the driver profile may be applied concurrently with route profile parameters, and the resulting target speed may be constrained to remain within globally defined safety and system-operating limits. Continuous learning based on real-time usage data may allow refinement of the stored profile without requiring explicit user intervention. The final modulated speed may be used by the vehicle control unit (110) during torque computation and constraint logic operations.
In an exemplary aspect, an EV operating within a mixed urban-suburban environment may utilize the vehicle control unit (110) to modulate the desired target speed dynamically based on both route profile and driver profile data. While navigating through a downtown district with frequent intersections, narrow lanes, and pedestrian activity, the route profile received from a GPS-integrated path planning module identifies a high urban density index and multiple low-radius curves. The vehicle control unit (110) reduces the target speed accordingly to maintain safe lateral acceleration and account for potential obstructions. Concurrently, the vehicle control unit (110) accesses a stored driver profile indicating a conservative driving preference with historically low acceleration rates and frequent use of economy driving mode. Based on this behavioral data, the vehicle control unit (110) applies an additional downward adjustment to the target speed, beyond what is determined by the route profile alone. As the EV transitions from the downtown area to a suburban arterial road with wider lanes and fewer stops, the urban density index decreases, and the route profile supports higher permissible speed. However, the target speed is still moderated slightly in accordance with the same conservative driver profile. Throughout the route, the vehicle control unit (110) recalculates and updates the target speed in real time as new profile data becomes available or conditions change. The modulated speed is then used in the torque computation logic to generate the appropriate demanded torque for the traction motor (104), resulting in controlled, customized, and situationally responsive vehicle operation.
In an embodiment, the vehicle control unit (110) may modify the desired torque value during a regenerative braking event or a regenerative coasting event by applying a reverse polarity coefficient. The reverse polarity coefficient may represent a scaling factor applied to the torque value to reflect the change in power flow direction when the traction motor (104) operates as a generator. During deceleration phases where the driver input corresponds to braking or throttle release, the vehicle control unit (110) may determine that torque reversal is appropriate based on current speed, gradient, and energy recovery availability. The reverse polarity coefficient may not be fixed and may be selected from a stored set of values or calculated dynamically based on operating conditions. By applying the reverse polarity coefficient, the magnitude of the negative torque command may be adjusted to meet both braking demands and energy recovery goals. For example, when the base desired torque is calculated as –60 Nm during a coasting event, and the reverse polarity coefficient is set to 0.85, the modified torque command may be set to –51 Nm. Such modification may be conditional on whether regenerative braking is permitted based on battery charge status, temperature, or inverter status. The vehicle control unit (110) may apply further filtering to the modified torque value to enable smooth transition between propulsion and regenerative states. The reverse polarity coefficient may also be stored in association with driving mode, load condition, or user preference, thereby allowing the vehicle to adapt energy recovery intensity under different scenarios. Alternatively, the vehicle control unit (110) may utilize a Reverse Torque Map in place of the reverse polarity coefficient. The Reverse Torque Map may define regenerative torque values based on multiple input parameters including vehicle speed, brake input, battery state of charge, and driving mode. The map may enable non-linear and context-sensitive adjustment of negative torque during coasting or braking phases. Use of the Reverse Torque Map allows greater adaptability to dynamic conditions, providing smoother control transitions and optimized energy recovery without relying on a single scaling factor.
In an exemplary aspect, an EV descending a moderate downhill gradient at a speed of approximately 55 km/h enters a regenerative coasting phase following release of the throttle pedal by the driver. The vehicle control unit (110) receives input indicating that propulsion torque demand has ceased, and simultaneously evaluates braking input and environmental parameters. A stored route segment profile confirms that the road gradient is sufficient to sustain vehicle momentum without additional propulsion. The vehicle control unit (110) calculates an initial negative torque value of –60 Nm based on the longitudinal dynamics model and the mass and speed of vehicle. Before issuing a regenerative braking command, the vehicle control unit (110) checks the battery state of charge and confirms that regenerative energy recovery is permitted. A reverse polarity coefficient of 0.85, previously stored in association with the active eco-mode and current load condition, is retrieved. The vehicle control unit (110) multiplies the calculated torque by the reverse polarity coefficient, yielding a final commanded regenerative torque of –51 Nm. The scaled torque reflects the need to moderate the intensity of energy recovery in order to maintain driving comfort and prevent abrupt deceleration. The vehicle control unit (110) filters the torque command further to ensure a smooth transition from propulsion to generator mode. The modified torque is then transmitted to the motor control unit (106), which actuates the traction motor (104) in generator mode to deliver the desired regenerative effect. The recovered electrical energy is routed to the battery within thermal and voltage constraints. In other scenarios, the vehicle control unit (110) may consult the Reverse Torque Map to retrieve an appropriate regenerative torque value instead of applying a scaling coefficient. The map-based retrieval may incorporate additional inputs such as brake modulation or gradient severity, enabling refined torque control aligned with vehicle dynamics and driver comfort expectations.
In an embodiment, the reverse polarity coefficient applied by the vehicle control unit (110) may be scaled based on a state of charge (SoC) of a battery and a back electromotive force (EMF) generated by the traction motor (104). The state of charge may be determined from a battery management system based on coulomb counting, voltage curves, or open-circuit voltage estimation. When the SoC exceeds a predefined upper limit, such as 95%, the reverse polarity coefficient may be reduced or set to zero to prevent overcharging of the battery. Conversely, when the SoC is below a predefined threshold, the coefficient may be increased to maximize regenerative energy capture. In parallel, the back EMF generated by the traction motor (104) during regenerative operation may be estimated using motor models or directly sensed using voltage monitoring at the motor terminals. If the back EMF approaches a level that may exceed the DC bus voltage or pose a risk to power electronics, the reverse polarity coefficient may be scaled down accordingly. For instance, in a scenario where the SoC is 85% and the back EMF is calculated at 380V against a bus limit of 400V, the reverse polarity coefficient may be set to 0.7 to assure safe braking without triggering overvoltage faults. The combined SoC and EMF-dependent scaling may result in a dynamically adjusted coefficient, which is applied in real-time within the torque computation logic. Such scaling may affirm system safety, battery protection, and consistent braking behavior.
In an embodiment, the vehicle control unit (110) may monitor a cumulative demanded torque profile over a predefined time interval and may constrain the demanded torque when the cumulative profile exceeds a thermal operating threshold associated with the traction motor (104). The cumulative demanded torque profile may be calculated as a running integral or moving average of the absolute value of demanded torque over time. For example, a cumulative torque profile may be computed using trapezoidal integration across successive control intervals over a 60-second sliding window. When the cumulative value exceeds a calibrated threshold that correlates with motor winding temperature rise, such as 18,000 Nm·s, the vehicle control unit (110) may reduce the allowable maximum torque or apply a derating factor to the current demanded torque. Thermal operating thresholds may be obtained from manufacturer-provided motor models or inferred from thermal sensors placed within the motor housing. In the absence of temperature sensors, estimated temperature models based on electrical power dissipation and ambient cooling conditions may be used. The constraint logic may involve saturation limits, time-dependent scaling, or activation of thermal protection modes. Constrained torque values may be stored separately from nominal demanded torque values to support fault analysis and system diagnostics. Constraint activation may also be accompanied by a user notification or control mode change. The purpose of the control mechanism may include prevention of motor overheating, extension of component life, and improved reliability of the electric propulsion system under sustained high-load operation.
In an embodiment, monitoring of a cumulative demanded torque profile by the vehicle control unit (110) may not be limited to instantaneous values and may extend across a predefined time interval to manage thermal loading on the traction motor (104). The cumulative torque profile may be determined as a moving average or running integral of the absolute demanded torque over time. Trapezoidal integration across consecutive control cycles within a 60-second sliding window may be used for such calculation. Upon detection that the cumulative torque profile exceeds a predefined thermal operating threshold—such as 18,000 Nm·s corresponding to motor temperature rise—the demanded torque may be constrained by the vehicle control unit (110). The thermal operating threshold may not remain fixed and may be dynamically updated as a result of a feedback loop using temperature sensors placed within the motor housing, enabling real-time correlation of thermal load with actual winding temperatures. In the absence of temperature sensors, dynamic threshold adjustment may also be carried out through a thermal deration algorithm implemented to manage motor or battery cooling requirements based on electrical power dissipation and ambient thermal conditions. Constraint logic applied by the vehicle control unit (110) may involve scaling factors, torque saturation limits, or time-dependent deration. Constrained torque values may be maintained separately from the nominal commanded torque for diagnostic reference. The presence of thermal constraints may lead to a mode change or visual indicator for the user. Torque modulation under such conditions may be applied to prevent thermal overload of the traction motor (104) and to support reliable operation under sustained demand.
In an embodiment, the motor control unit (106) may apply a phase-interleaved torque command to the traction motor (104), wherein the phase-interleaved torque command may correspond to a sequentially timed actuation of multiple motor phases to reduce torque harmonics. In multi-phase electric machines, instantaneous torque may vary due to non-ideal current waveforms and spatial displacement of windings. By implementing a phase-interleaving strategy, the motor control unit (106) may distribute the torque demand across motor phases using shifted timing or modulated pulse width commands to achieve smoother net torque output. For example, in a three-phase system operating at a switching frequency of 10 kHz, interleaving may involve advancing or delaying pulse generation for individual phases by approximately 120 electrical degrees. Such timing modifications may reduce the amplitude of low-order harmonics that contribute to acoustic noise, vibration, and torque ripple. The motor control unit (106) may calculate the phase-interleaved signals using digital signal processing routines or look-up tables stored in memory. The strategy may be activated selectively under conditions such as low-speed operation, vehicle launch, or transitions between drive and regeneration modes. Feedback from current sensors may be used to refine the actuation pattern and maintain current balance across phases. The use of phase-interleaved torque commands may not be limited to a specific motor topology and may apply to permanent magnet synchronous machines, induction motors, or switched reluctance machines. Additional filtering or waveform shaping techniques may be used in combination to meet electromagnetic compatibility and drivability requirements.
FIG. 2 illustrates a method (200) for controlling a speed of an electric vehicle (EV), in accordance with the embodiments of the present disclosure. At step 202, an actual motor speed is detected by a speed sensing unit (102), which is operatively coupled to a traction motor (104) and a motor control unit (106). The speed sensing unit (102) may be implemented using one or more sensors such as Hall-effect sensors, optical encoders, or magnetic pickups positioned to monitor the rotational output of the traction motor (104). The actual motor speed detected by the speed sensing unit (102) is transmitted as a feedback signal, which is representative of the current rotational speed of the traction motor (104), to downstream control components for further processing. The detected actual motor speed serves as a primary input to a vehicle control unit (110), enabling closed-loop feedback control within a real-time torque regulation system.
At step 204, a driving mode and at least one of a desired speed associated with a throttle input signal or a braking force input is received by an input interface (110). The input interface (108) may receive user-generated input via accelerator or brake pedals, selector switches, or graphical interfaces. For example, a throttle signal may correspond to a voltage or pulse-width modulated signal representing an intended acceleration, while a braking input may be derived from a force-sensitive resistor or brake pressure sensor. The driving mode received via the input interface (108) may be selected from predefined categories such as eco-mode, ride mode, or rush mode, each representing different propulsion and torque strategies. The input interface (108) may convert the physical or analog signals into digital data and transmit the processed values to the vehicle control unit (110) for further evaluation in subsequent computational steps.
At step 206, the actual motor speed detected by the speed sensing unit (102), a mass of the EV, and at least one of the driving mode and the desired speed is received by the vehicle control unit (110), which is in communication with both the speed sensing unit (102) and the input interface (110). The mass of the EV may be obtained from a load detector or inferred from a mass estimation algorithm executed internally by the vehicle control unit (110). Receipt of the parameters enables the vehicle control unit (110) to compile all necessary input data for dynamic modeling of the propulsion system. The communication between subsystems may occur over digital protocols such as CAN, LIN, or SPI, and the input values may be stored in memory registers for use during the subsequent torque computation phase. The received values collectively represent the current operational state and user intent, forming the basis for torque value determination.
At step 208, a desired torque value is computed by the vehicle control unit (110) using a discrete-time longitudinal dynamic model. The model considers the mass of the EV, the actual motor speed, the braking force input, a rolling resistance force, a driving surface characteristic, an aerodynamic drag force, an inertial resistive force, a surface gradient, the desired speed, and the driving mode. The discrete-time longitudinal dynamic model may be implemented using finite-difference equations that simulate the longitudinal force balance across time steps. For instance, Newton’s second law is applied to determine required net force, and thus torque, to achieve a desired acceleration. Environmental variables such as road slope and rolling resistance may be calculated or retrieved from stored calibration maps. The computation results in a raw desired torque value necessary to meet the target motion characteristics specified by the driver or driving mode under prevailing physical constraints.
At step 210, the desired torque value computed in the preceding step is processed through a constrained optimization protocol applied by the vehicle control unit (110). The constrained optimization protocol incorporates at least one of a predefined maximum torque limit, a predefined minimum torque limit, a predefined maximum torque rate, and a predefined minimum torque rate. The constraints make sure that the desired torque value remains within safe and allowable limits based on hardware ratings, energy efficiency requirements, or driving condition restrictions. For example, if the computed torque exceeds a maximum allowable torque of 250 Nm, the constrained optimization protocol may scale the torque down accordingly. Similarly, torque rate constraints may prevent rapid changes between torque values over successive control cycles. The vehicle control unit (110) may apply the constraints using clamping functions or filtered transition mechanisms, thereby generating an intermediate constrained torque value suitable for use in actuation.
At step 212, a demanded torque is generated by the vehicle control unit (110) based on the application of the constrained optimization protocol. The demanded torque represents the final torque value intended to be applied at the traction motor (104) for achieving the desired speed response. The demanded torque may be expressed in engineering units such as Newton-meters (Nm) and may be transmitted as a command signal from the vehicle control unit (110) to the motor control unit (106). The command may be scheduled for transmission at defined control intervals and may include time stamps or validation data for synchronization. The generation of the demanded torque may also involve signal formatting, quantization, or digital encoding for compatibility with downstream components. The demanded torque serves as the authoritative torque request for real-time actuation of the traction motor (104), reflecting both driver intent and enforced operational constraints.
At step 214, the motor control unit (106), which is in communication with the vehicle control unit (110) and the traction motor (104), receives the demanded torque. The motor control unit (106) may receive the torque command over a dedicated communication channel or via a shared vehicle bus. Upon receiving the demanded torque, the motor control unit (106) may perform verification routines such as checksum validation or range checking before storing the torque command in control memory. The demanded torque may then be converted into phase current references or voltage commands using control strategies such as field-oriented control (FOC) or direct torque control (DTC). Sensor feedback such as rotor position and phase current values may be used to modulate the electrical actuation pattern accordingly. The receipt of the demanded torque initiates the control sequence responsible for producing mechanical output in the traction motor (104).
At step 216, the traction motor (104) is actuated by the motor control unit (106) to generate the demanded torque and thereby control the speed of the electric vehicle. The actuation involves the generation of electrical signals, such as pulse-width modulated waveforms, to drive the motor windings in accordance with the demanded torque received. The motor control unit (106) may use inverter circuits comprising IGBTs or MOSFETs to modulate power delivery across the motor phases. Real-time feedback from position sensors and current sensors may be used to maintain closed-loop torque control. The resulting mechanical torque produced by the traction motor (104) is transmitted to the drivetrain of the electric vehicle, resulting in acceleration, deceleration, or maintenance of speed as determined by the commanded input. The actuation step may be repeated at high frequency to enable continuous and stable propulsion control. Safety mechanisms may be invoked if deviations or faults are detected during the actuation process.
FIG. 3 illustrates a decision-based constrained optimization protocol flowchart, in accordance with the embodiments of the present disclosure. The flowchart depicts a structured method for processing a desired torque input by systematically applying operational constraints and performance feedback to compute a final torque output suitable for implementation in an electric or hybrid vehicle control system. Initially, the desired torque input is received and evaluated against predefined torque boundaries, including minimum and maximum torque limits. If the torque falls outside these boundaries, it is clipped to the nearest permissible value. Subsequently, the torque is examined for conformity with permissible rate-of-change thresholds. If the rate change is not acceptable, a rate limiter is applied to moderate the transition. Following this, the protocol assesses whether the torque output aligns with efficiency requirements and energy consumption constraints by referencing real-time feedback from the motor control system. If inefficiencies or excessive energy consumption are detected, further adjustments are made to the torque output to restore optimal performance conditions. Ultimately, the processed torque, having satisfied all constraints and optimizations, is issued as the final torque command. Such a sequential and conditional processing framework ensures safe, efficient and performance-oriented torque delivery under dynamically varying operational scenarios, while maintaining system reliability and energy efficiency.
FIG. 4 illustrates a signal flow diagram for vehicle speed regulation using a torque-based control strategy, in accordance with the embodiments of the present disclosure. The diagram outlines a communication exchange between a motor control unit (similar to the motor control unit (106) of FIG. 1) and a vehicle control unit (similar to the vehicle control unit (110) of FIG. 1) through a vehicle CAN bus, which facilitates the transfer of operational data required for dynamic speed management. Parameters including speed, total load, motor control unit mode, and vehicle control unit mode are transmitted over the bus and processed by a speed control algorithm embedded within the vehicle control unit (similar to the vehicle control unit (110) of FIG. 1). The algorithm initiates a conditional verification to determine whether the vehicle control unit mode is set to traction. If traction mode is confirmed, subsequent logic branches assess the current motor control unit mode (similar to the motor control unit (106) of FIG. 1) to differentiate among multiple torque execution scenarios. Depending on the motor control unit mode, the control flow proceeds to calculate the vehicle velocity error (ve), which is obtained by comparing the forecasted speed with a target speed derived from a throttle or brake signal received through an input interface (similar to the input interface (108) of FIG. 1). The vehicle velocity error serves as the primary input to a cost function, which is minimized through a convex optimization procedure. The optimization is performed under torque and torque rate constraints, ensuring compliance with operational boundaries while striving for improved speed tracking and efficiency. The resulting output is a reference torque, which is communicated to the motor control unit (similar to the motor control unit (106) of FIG. 1) for actuation of a traction motor (similar to the traction motor (104) of FIG. 1). Upon completion of the torque generation step, the control cycle terminates, enabling continuous real-time adjustment of propulsion torque in response to varying drive conditions.
In an embodiment, speed sensing unit (102) operatively coupled to traction motor (104) and motor control unit (106) enables real-time acquisition of actual motor speed, which supports accurate feedback for torque computation. Said operative coupling allows speed sensing unit (102) to monitor motor shaft rotation without signal loss or delay, enhancing responsiveness of control actions initiated by motor control unit (106). Positioning speed sensing unit (102) in proximity to traction motor (104) reduces latency in speed detection and eliminates signal distortion due to transmission delays. Input interface (108) configured to receive a driving mode, desired speed associated with a throttle input signal, or braking force input enables adaptive torque computation responsive to user intent. Communication of vehicle control unit (110) with both speed sensing unit (102) and input interface (108) makes sure synchronization of feedback and driver commands. Execution of discrete-time longitudinal dynamic model by vehicle control unit (110) allows precise torque prediction considering resistive and inertial components. Application of constrained optimization by vehicle control unit (110) on said torque computation enables control within physical boundaries, reducing thermal and mechanical stress. Generation of a demanded torque based on such constrained torque enhances stability and prevents over actuation. Motor control unit (106) receiving and executing said demanded torque affirms timely delivery of accurate torque output, thereby achieving smoother and more predictable control of electric vehicle speed.
In an embodiment, reception of electric vehicle mass by vehicle control unit (110) from load detector or mass estimation algorithm improves precision of dynamic modeling executed during torque computation. Such mass information, whether sensor-based or model-derived, directly influences computation of inertial and resistive forces. Accurate input mass enables vehicle control unit (110) to calculate required propulsion torque more precisely under changing load conditions, such as passenger or cargo variations. Real-time mass updates prevent underestimation or overestimation of torque, avoiding inefficiencies in energy usage or vehicle response. Integration of load detector into structural chassis elements supports stable measurement of distributed loads, minimizing noise and offset error. Mass estimation logic within vehicle control unit (110) makes sure fallback capability in case of sensor failure or cost-optimized deployments without dedicated load detectors.
In an embodiment, regulation of predefined torque limit and torque rate parameters by vehicle control unit (110) based on inputs such as energy consumption level, motor efficiency, detected mass, or driving condition enables context-aware torque adaptation. Such regulation allows vehicle control unit (110) to dynamically scale down torque delivery when high energy consumption or motor inefficiency is detected, reducing battery drain and thermal buildup. Adjustments based on driving conditions, such as frequent stops in urban zones, allow minimization of oscillations and jerk by limiting torque rate. Load-based scaling affirms propulsion remains adequate without exceeding mechanical or thermal capabilities of drivetrain components. Regulation of both amplitude and rate prevents instability in electric vehicle behavior during rapid load or terrain transitions. Technical advantages include sustained motor performance, improved driving comfort, and extended system reliability under diverse operating environments.
In an embodiment, association of each driving mode—eco-mode, ride mode, and rush mode—with a predefined desired target speed enables vehicle control unit (110) to select torque strategy aligned with user preference or application context. Eco-mode supporting lower target speeds leads to reduction in torque command amplitude, conserving battery energy. Ride mode maintains balanced performance for standard driving scenarios, while rush mode enables quicker acceleration through elevated torque availability. Switching between the modes modifies both desired speed inputs and associated torque constraints in vehicle control unit (110), leading to varied energy usage, response behavior, and regenerative recovery. Advantages include user-selectable control performance, programmable efficiency profiles, and operational flexibility across different driving objectives.
In an embodiment, deviation-based adjustment of predefined torque limit or torque rate limit by vehicle control unit (110) using first acceleration derived from motor speed time-series and second acceleration computed from detected mass and inferred force enables real-time validation and correction of control accuracy. Comparison of estimated acceleration from sensor data with modeled acceleration under current load conditions allows identification of mismatches due to external disturbances or modeling inaccuracies. Vehicle control unit (110) dynamically refines torque limits to account for actual conditions, minimizing control error. Such correction enhances vehicle responsiveness while maintaining safety under uncertain or varying road conditions. The deviation-based adjustment provides improved alignment between commanded and actual dynamics, minimized oscillations, and adaptive torque bounding under varying acceleration profiles.
In an embodiment, modulation of desired target speed by vehicle control unit (110) based on a route profile comprising road segment curvature, urban density index, and speed zone classification enables predictive speed control adapted to environmental context. Curvature-based modulation reduces speed in tight turns, minimizing lateral instability. High urban density inferred from route profile triggers lower speed targets, supporting safe navigation through congested areas. Speed zone classification enables compliance with regulatory limits based on digital map data or geofenced regions. Dynamic speed modulation prior to torque computation allows anticipation of deceleration and reduction in energy usage through smoother transitions. Advantage includes compliance with road safety constraints, energy-optimized speed scheduling, and context-aware propulsion planning.
In an embodiment, application of a reverse polarity coefficient by vehicle control unit (110) during regenerative braking or coasting events allows modulation of negative torque magnitude without reversing torque logic. Such coefficient serves as a scalar applied to base torque computation to achieve safe and controlled regenerative operation. By adjusting the strength of regenerative effect, vehicle control unit (110) makes sure smooth braking behavior and prevents abrupt transitions. Use of reverse polarity coefficient decouples regenerative logic from main drive torque pathway, enabling fine-tuned energy recovery control. The application of a reverse polarity coefficient enables safer braking response, controlled regenerative feedback, and minimized driveline shock during transition between propulsion and regeneration phases.
In an embodiment, scaling of reverse polarity coefficient based on state of charge (SoC) of a battery and back electromotive force (EMF) of traction motor (104) enables vehicle control unit (110) to modulate regenerative braking intensity based on system constraints. When SoC is high, reverse polarity coefficient is reduced to prevent battery overcharging. When back EMF exceeds system voltage thresholds, coefficient is further decreased to avoid inverter overvoltage conditions. Such dual-scaling approach allows vehicle control unit (110) to optimize regenerative braking performance within safe limits. Technical advantage includes improved energy recovery management, prevention of overvoltage conditions, and battery-safe dynamic braking torque generation.
In an embodiment, monitoring of cumulative demanded torque profile by vehicle control unit (110) over a predefined time interval and constraining of torque when such profile exceeds thermal operating threshold of traction motor (104) enables proactive thermal management. Accumulated torque exposure is evaluated to predict motor heating over time. When threshold is exceeded, vehicle control unit (110) reduces torque to mitigate temperature rise and prevent thermal shutdown. Constraining torque based on cumulative profile rather than instantaneous values supports sustained safe operation under prolonged load. Advantages include motor overtemperature prevention, thermal derating during high-load intervals, and extended motor lifespan under heavy usage.
In an embodiment, application of a phase-interleaved torque command by motor control unit (106) to traction motor (104), using sequential timing across motor phases, reduces harmonic content and smooths net torque output. Phase interleaving shifts actuation timing between phases, thereby distributing torque ripple and minimizing peak harmonic contributions. The sequential timing improves drivability by reducing vibrations and acoustic noise. Especially under low-speed or low-load conditions, interleaving prevents resonance effects. Advantages includes reduced torque ripple, smoother torque delivery, improved NVH performance, and enhanced motor current symmetry.
In an embodiment, execution of method (200) for controlling a speed of an electric vehicle using steps including speed detection by speed sensing unit (102), input reception via input interface (110), and subsequent processing by vehicle control unit (110) and motor control unit (106) enables sequential closed-loop control over electric vehicle propulsion. Computation of torque using dynamic models, enforcement of torque constraints, and generation of final demanded torque affirms torque output remains responsive to real-time inputs while complying with safety and efficiency parameters. Technical advantages include deterministic control sequencing, integration of multi-source feedback, and continuous torque management.
In an embodiment, the vehicle control unit (110) calculates a vehicle velocity error (ve) as the difference between the actual motor speed and a desired speed (vd) of the electric vehicle. The calculation involves comparing the real-time motor speed data received from the speed sensing unit (102) with the desired speed signal acquired via the input interface (108). The vehicle velocity error (ve) serves as the basis for computing a demanded torque. Optionally, the efficiency parameter (?) of the system (100) is also considered in the formulation of a cost function to refine the torque decision under operating constraints. The vehicle control unit (110) reduces the vehicle velocity error (ve) and seeks to maximize ? in order to compute the demanded torque, which is subsequently transmitted to the motor control unit (106) as an input reference value. The demanded torque governs the torque generation by the traction motor (104) to reconcile actual and desired motor speed. The discrete-time longitudinal dynamics of the vehicle can be modeled as:

v(k+1) = v(k) - T_s/(2*m)*C_d*A*? * ?v(k)?^2 -T_s/m×m*g*? sin ? +(T_s*M_y)/(m*R_w ) + (T_s*t)/(m*R_w )…(1)

Ts – Sampling time
My – Rolling Moment around Y-axis.
A – Frontal area of the electric vehicle
? – Density of air
m – mass estimated by the mass estimation algorithm
Cd – Drag coefficient
g – coefficient of gravity
t – Torque
? - pitch angle
Rw – Wheel radius

Where ?1/2(C?_d*A*? * ?v(k)?^2) is the drag force, m*g*sin? is the Force due to gradient, M_y/R_w is Rolling Resistance Force, and t is Traction Torque.
If the desired speed is vd, where the desired speed is dependent on the throttle input and the vehicle modes, then the system 100 reduces the vehicle velocity error (ve) between the two values, which can be given by v_e= v(k+1) - v_d.
Further, a cost function (J) can be formulated to reduce the vehicle velocity error (ve),
J =min v_e^2 + max ?.
subject to t_min= t = t_max,
??t?_min= ?t = ??t?_max
where max ? – is the maximum efficiency factor derived for torque associated with a predefined speed slab. The torque demand for the predefined speed slab is determined experimentally. The max ? is assigned a lower weight than the vehicle velocity error (ve) in formulation of the cost function (J). Here, ?t represents the change in torque across control intervals. The term max(?) corresponds to the efficiency factor for torque within a specific speed band, experimentally derived. In the cost function J, a lower weight is assigned to ? compared to ve, such that reducing the velocity error takes precedence in real-time control decisions made by the vehicle control unit (110). The demanded torque resulting from the optimized cost function is communicated to the motor control unit (106), which actuates the traction motor (104) accordingly.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random-access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

,CLAIMS:Claims
I/We Claim:
1. A system (100) to control a speed of an electric vehicle (EV), comprising:
a speed sensing unit (102) operatively coupled to a traction motor (104) and a motor control unit (106), wherein the speed sensing unit (102) detects an actual motor speed;
an input interface (108) configured to receive a driving mode, at least one of a desired speed associated with a throttle input signal or a braking force input; and
a vehicle control unit (110) in communication with the speed sensing unit (102) and the input interface (110), wherein the vehicle control unit (110) is configured to:
receive the detected actual motor speed, a mass of the EV, and at least one of the driving mode and the desired speed;
compute a desired torque value using a discrete-time longitudinal dynamic model as a function of the mass of the EV, the detected actual motor speed, the braking force input, a rolling resistance force, a driving surface characteristic, an aerodynamic drag force, an inertial resistive force, a surface gradient, the desired speed, and the driving mode;
apply a constrained optimization protocol on the desired torque value based on at least one of a predefined maximum torque limit, a predefined minimum torque limit, a predefined maximum torque rate, and a predefined minimum torque rate; and
generate a demanded torque based on application of the constrained optimization protocol; and
a motor control unit (106) in communication with the vehicle control unit (110) and the traction motor (104), the motor control unit (106) configured to:
receive the demanded torque from the vehicle control unit (110); and
actuate the traction motor (104) to generate the demanded torque to control the speed of the EV.
2. The system (100) of claim 1, wherein the vehicle control unit (110) receives the mass of the EV as an output value from a load detector or as an output value of a mass estimation algorithm.
3. The system (100) of claim 1, wherein the vehicle control unit (110) regulates at least one of the predefined maximum torque limit, the predefined minimum torque limit, the predefined maximum torque rate, and the minimum torque rate limit, based on at least one of an energy consumption level, a motor efficiency, a driving condition, the detected mass, or the detected load.
4. The system (100) of claim 1, wherein the driving mode is selected from: an eco-mode, a ride mode, and a rush mode, and wherein each driving mode is associated with a predefined desired target speed.
5. The system (100) of claim 1, wherein the vehicle control unit (110) adjusts at least one of the predefined torque limit and the predefined torque rate limit based on a deviation between a first acceleration derived from a time-series analysis of an actual motor speed and a second acceleration computed using an initial value of the detected mass and a resultant force inferred from the load detection sensor.
6. The system (100) of claim 1, wherein the vehicle control unit (110) modulates the desired target speed based on a route profile, wherein the route profile comprising a road segment curvature, an urban density index, and a speed zone classification.
7. The system (100) of claim 1, wherein the vehicle control unit (110) is adapted to modify the desired torque value for a regenerative braking event or a regenerative coasting event. by utilizing a reverse polarity coefficient.
8. The system (100) of claim 7, wherein the reverse polarity coefficient is scaled based on a state of charge (SoC) of a battery and a back electromotive force (EMF) of the traction motor (104).
9. The system (100) of claim 1, wherein the vehicle control unit (110):
monitors a cumulative demanded torque profile over a predefined time interval; and
constraints the demanded torque when the cumulative demanded torque profile exceeds a thermal operating threshold associated with the traction motor (104).
10. The system (100) of claim 1, wherein the motor control unit (106) applies a phase-interleaved torque command to the traction motor (104), wherein the phase-interleaved torque command is associated with a sequentially timed actuation of multiple motor phases to minimize the torque harmonics.
11. A method (200) for controlling a speed of an electric vehicle (EV), the method (200) comprising:
detecting, by a speed sensing unit (102) an actual motor speed;
receiving, by an input interface (110), a driving mode and at least one of a desired speed associated with a throttle input signal or a braking force input;
receiving, by a vehicle control unit (110) the actual motor speed, a mass of the EV, and at least one of the driving mode and the desired speed;
computing, by the vehicle control unit (110), a desired torque value using a discrete-time longitudinal dynamic model as a function of the mass of the EV, the actual motor speed, the braking force input, a rolling resistance force, a driving surface characteristic, an aerodynamic drag force, an inertial resistive force, a surface gradient, the desired speed, and the driving mode;
applying, by the vehicle control unit (110), a constrained optimization protocol to the desired torque value based on at least one of a predefined maximum torque limit, a predefined minimum torque limit, a predefined maximum torque rate, and a predefined minimum torque rate;
generating, by the vehicle control unit (110), a demanded torque based on the application of the constrained optimization protocol;
receiving, by the motor control unit (106) in communication with the vehicle control unit (110) and the traction motor (104), the demanded torque from the vehicle control unit (110); and
actuating, by the motor control unit (106), the traction motor (104) to generate the demanded torque to control the speed of the EV.

Dated 09 July 2025 Kumar Tushar Srivastava
IN/PA- 3973
Agent for the Applicant