Description:FIELD OF THE INVENTION
[0001] This present disclosure generally relates to electrical motors, and more specifically to systems and methods for sustained high-performance operation in electric motors with real-time adaptive control and continuous energy recovery.

BACKGROUND OF THE INVENTION
[0002] Electric vehicles (EVs) rely heavily on the performance and efficiency of electric motors to meet the demands of modern transportation. Achieving sustained high-performance operation in EV motors, particularly under peak load conditions, has posed significant challenges. Existing methods often struggle to maintain performance and reliability, leading to issues such as overheating, increased wear and tear, and reduced operational lifespan. These challenges may be exacerbated by the dynamic and variable nature of load demands in real-world driving scenarios.

[0003] Conventional systems primarily rely on mechanical optimizations, such as adjusting air gaps, modifying motor components, and altering magnetic flux densities to enhance performance. These methods, while beneficial to some extent, often fall short in addressing the core issues associated with heat generation and efficiency. For instance, mechanical redesigns can increase the complexity and cost of the motor, while providing only superficial mitigation of heat-related problems. Additionally, traditional energy recovery systems, like regenerative braking, capture energy only during specific phases of operation, such as deceleration, missing opportunities for continuous energy recovery. Electric vehicles often employ multiple motors to meet performance and efficiency demands. Proper load distribution among these motors is necessary for optimal performance and longevity.

[0004] Another significant drawback of conventional systems is their reliance on active thermal management techniques. These systems typically involve external cooling methods, such as adding cooling fins or using cooling fluids, which not only increase the overall complexity and cost but also fail to enhance the intrinsic efficiency of the motor. The inability to dynamically manage heat and load conditions in real-time further limits the performance and reliability of these motors, making them vulnerable to inefficiencies and failures, particularly under high-stress conditions.

[0005] Other drawbacks of existing solutions are as follows:

[0006] Limited Energy Capture: Traditional regenerative braking systems only recover energy during deceleration phases, missing opportunities to harness energy during acceleration or steady-state operation. This results in suboptimal energy utilization and efficiency.

[0007] High Operational Costs and Complexity: Conventional systems often rely on intricate control mechanisms to modulate current frequency and voltage. These systems are costly, prone to inefficiencies, and susceptible to failure, especially under dynamic driving conditions.

[0008] Excessive Heat Generation: Electric vehicle motors generate substantial heat due to friction, resistance, and eddy current losses. Inefficient heat management leads to higher operational temperatures, reducing efficiency and potentially compromising motor longevity.

[0009] Accelerated Wear and Tear: The thermal and mechanical stresses associated with traditional motor designs accelerate wear and tear, leading to reduced operational lifespan and reliability. Frequent maintenance and component replacements are often necessary, increasing operational costs.

[0010] Inefficiencies in Real-Time Load Management: Conventional motors lack sophisticated real-time adaptive control systems, limiting their ability to dynamically manage varying load conditions. This results in suboptimal performance, particularly under high-stress or peak load scenarios, and hinders the overall efficiency and reliability of the motor.

[0011] Limitations and disadvantages of conventional and traditional approaches will become apparent to one of ordinary skill in the art, through comparison of described systems with some aspects of the present invention, as set forth in the remainder of the present application and with reference to the drawings.

SUMMARY OF THE INVENTION
[0012] Aspects of the present disclosure relate to systems and methods to enhance the sustained high-performance operation of electric vehicle motors by integrating real-time adaptive control mechanisms that continuously monitor and adjust motor parameters based on real-time data, optimizing performance, and preventing overheating. This dynamic adjustment capability ensures that the motor operates efficiently across varying load conditions, maintaining optimal performance and extending its operational lifespan.

[0013] The integration of continuous energy recovery, real-time adaptive control, and enhanced peak load handling ensures that the motor can sustain high-performance operation under dynamic conditions, significantly advancing the state-of-the-art in electric motor technology for electric vehicles.

[0014] Aspects of the present disclosure relate to a system and method for achieving sustained high-performance operation in electrical motors, particularly under peak load conditions. The system integrates real-time adaptive control mechanisms that monitor and adjust motor parameters dynamically, ensuring optimal performance and preventing overheating. Enhanced peak load handling capabilities are achieved through improved motor design and control strategies, allowing the motor to consistently operate beyond its nominal ratings without significant degradation. Additionally, the system incorporates a continuous energy recovery and feedback system, utilizing auxiliary windings to capture and regenerate energy by converting otherwise lost energy into electrical power, which is then fed back into one or more batteries of the electric vehicle and/or to the electric motor and/or to power the on-board auxiliary systems of the electric vehicle. This approach ensures that the motor operates efficiently and reliably under varying load demands, extending its operational lifespan and reducing maintenance requirements.

[0015] Compared to existing technologies, the system of the present invention provides significant advancements by integrating continuous energy recovery mechanisms and adaptive control systems that dynamically respond to real-time conditions. Traditional systems often rely on phase-specific energy recovery methods and passive thermal management, which are insufficient for sustained high-load operations. In contrast, this invention offers a holistic solution that addresses both performance and efficiency challenges. The continuous energy recovery system operates throughout all phases of motor operation, ensuring maximized energy utilization. Furthermore, the adaptive control mechanisms optimize load and thermal management in real-time.

[0016] Aspects of the present disclosure relate to the integration of auxiliary windings that continuously capture and convert otherwise lost energy into electrical energy, which is then fed back into one or more batteries of the electric vehicle and/or to the electric motor and/or to power the on-board auxiliary systems of the electric vehicle. This process is managed by intelligent control systems that dynamically adjust the rate and amount of energy feedback based on real-time battery status and motor operation conditions. By maintaining a continuous energy recovery mechanism, this system significantly enhances the overall energy efficiency and extends the operational range of battery-operated equipment, particularly electric vehicles. Additionally, the optimized energy flow management ensures that the motor operates efficiently without compromising battery health or motor performance, resulting in reduced frequency of charging sessions and prolonged battery lifespan.

[0017] Traditional systems typically rely on phase-specific energy recovery mechanisms, such as regenerative braking, which only capture energy during deceleration phases. In contrast, the continuous energy recovery system described in this patent operates throughout all phases of motor operation, offering a significant improvement in energy utilization. Moreover, the intelligent control systems used in the system of the present disclosure provide real-time adjustments to optimize energy feedback. Aspects of the present disclosure provide the following technical advantages or benefits:

[0018] The solution described in the present disclosure enhances the performance and efficiency of electric motors in electric vehicles through the integration of auxiliary windings and intelligent control systems for continuous energy recovery and optimized energy management. Aspects of the present disclosure present disclosure enables continuous energy capture and feedback by utilizing auxiliary windings to capture energy losses during all phases of motor operation and converting this energy into electrical power that is fed back into one or more batteries of the electric vehicle and/or to the electric motor and/or to power the on-board auxiliary systems of the electric vehicle.

[0019] Further aspects of the present disclosure optimize energy utilization and efficiency by employing intelligent control systems that minimize energy wastage and maximize the conversion efficiency of captured energy, reducing dependency on intricate and costly control systems.

[0020] Further aspects of the present disclosure extend the operational lifespan of the motor by reducing the losses (i.e., eddy current losses) and thermal aspects of the motor, such that the motor operates at a reduced temperature to maintain battery health. Further aspects of the present disclosure maximize the use of available energy to reduce the frequency of external charging sessions and promote sustainability through improved energy efficiency of electric vehicles.

[0021] Further aspects of the present disclosure enhance the sustained high-performance operation of electric vehicle motors by integrating real-time adaptive control mechanisms, continuous energy recovery systems, and advanced peak load handling capabilities. The system of the present disclosure aims to address the limitations of conventional systems by providing a comprehensive solution that dynamically adjusts motor parameters to optimize performance and prevent overheating, maximizes energy utilization through continuous energy capture and feedback, and extends the operational lifespan of the motor while reducing maintenance requirements. By achieving these objectives, the invention seeks to offer a robust, efficient, and reliable motor solution that meets the demanding requirements of modern electric vehicle applications.

[0022] In an example implementation, a system for real-time adaptive control and continuous energy recovery in an electric motor is disclosed, wherein the electric motor comprises a main winding and one or more auxiliary windings in a stator of the electric motor. The system includes an adaptive control unit configured to monitor in real-time sensor data from a plurality of sensors; and adjust at least one of a voltage or a current input to the electric motor based on real-time feedback based on the sensor data, using a Proportional-Integral-Derivative (PID) controller, wherein the real-time feedback is indicative of an error between a desired state of the electric motor and an actual state of the electric motor, a state of the electric motor being associated with an operating parameter of the electric motor.
[0023] In an aspect combinable with the example implementation, the plurality of sensors may include at least one of thermocouples, hall effect sensors, load sensors, temperature sensors, and speed sensors.

[0024] In another aspect combinable with any of the previous aspects, the operating parameter of the electric motor is at least one of a speed of the electric motor and a torque of the electric motor.

[0025] In another aspect combinable with any of the previous aspects, the operating parameter of the electric motor is the speed of the electric motor, wherein the adaptive control unit is configured to maintain the speed of the electric motor by minimizing an error between a desired speed of the electric motor and an actual speed of the electric motor, using the PID controller.

[0026] In another aspect combinable with any of the previous aspects, the PID controller is configured to determine a desired setpoint for the operating parameter of the electric motor based on at least one of a driver’s input, performance requirements, and system conditions.

[0027] In another aspect combinable with any of the previous aspects, the error in the PID controller is a difference between the desired setpoint and an actual value of the operating parameter.

[0028] In another aspect combinable with any of the previous aspects, the error is based on at least one of changes in load, variations in input voltage, external disturbances, temperature fluctuations and mechanical wear.

[0029] In another aspect combinable with any of the previous aspects, the adaptive control unit is configured to predict changes in a load of the electric motor and pre-emptively adjust the operating parameter of the electric motor, using one or more machine learning models.

[0030] In another aspect combinable with any of the previous aspects, the one or more machine learning models are trained based on historical data and real-time data.

[0031] In another aspect combinable with any of the previous aspects, the one or more machine learning models are configured to: analyze past sensor data to predict load requirements of the electric motor; and pre-emptively adjust desired setpoints of the operating parameter for the PID controller based on the predicted load requirements.

[0032] In an example implementation, a method for real-time adaptive control and continuous energy recovery in an electric motor is disclosed, wherein the electric motor comprises a main winding and one or more auxiliary windings in a stator of the electric motor. The method includes: monitoring in real-time sensor data from a plurality of sensors; and adjusting at least one of a voltage or a current input to the electric motor based on real-time feedback based on the sensor data, using a Proportional-Integral-Derivative (PID) controller, wherein the real-time feedback is indicative of an error between a desired state of the electric motor and an actual state of the electric motor, a state of the electric motor being associated with an operating parameter of the electric motor.

[0033] In an aspect combinable with the example implementation, the plurality of sensors may include at least one of thermocouples, hall effect sensors, load sensors, temperature sensors, and speed sensors.

[0034] In another aspect combinable with any of the previous aspects, the operating parameter of the electric motor is at least one of a speed of the electric motor and a torque of the electric motor.

[0035] In another aspect combinable with any of the previous aspects, the operating parameter of the electric motor is the speed of the electric motor, wherein the adjusting comprises maintaining the speed of the electric motor by minimizing an error between a desired speed of the electric motor and an actual speed of the electric motor, using the PID controller.

[0036] In another aspect combinable with any of the previous aspects, the method further comprises determining, using the PID controller, a desired setpoint for the operating parameter of the electric motor based on at least one of a driver’s input, performance requirements, and system conditions.

[0037] In another aspect combinable with any of the previous aspects, the error in the PID controller is a difference between the desired setpoint and an actual value of the operating parameter.

[0038] In another aspect combinable with any of the previous aspects, the error is based on at least one of changes in load, variations in input voltage, external disturbances, temperature fluctuations and mechanical wear.

[0039] In another aspect combinable with any of the previous aspects, the method further comprises predicting changes in a load of the electric motor and pre-emptively adjusting the operating parameter of the electric motor, using one or more machine learning models.

[0040] In another aspect combinable with any of the previous aspects, the one or more machine learning models are trained based on historical data and real-time data.

[0041] In another aspect combinable with any of the previous aspects, the method further comprises analyzing, using the one or more machine learning models, past sensor data to predict load requirements of the electric motor; and pre-emptively adjusting, using the one or more machine learning models, desired setpoints of the operating parameter for the PID controller based on the predicted load requirements.

[0042] These and other features and advantages of the present invention may be appreciated from a review of the following detailed description of the present invention, along with the accompanying figures in which like reference numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS
[0043] FIG. 1 is a schematic representation of a system for real-time adaptive control and continuous energy recovery in an electric motor according to an aspect of the present disclosure.

[0044] FIG. 2A is a schematic representation of a stator of an induction motor according to an aspect of the present disclosure.

[0045] FIG. 2B is a diagrammatic representation of a connection configuration between a main winding and an additional auxiliary winding of the stator to achieve high power factor according to an aspect of the present disclosure.

[0046] FIG. 3 is a schematic representation of a rotor of an induction motor according to an aspect of the present disclosure.

[0047] FIG. 4 is a flowchart illustrating a method for real-time adaptive control and continuous energy recovery in an electric motor according to an aspect of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION
[0048] The following described implementations may be found in the disclosed system for real-time adaptive control and continuous energy recovery in an electric motor.

[0049] FIG. 1 is a schematic representation of a system for real-time adaptive control and continuous energy recovery in an electric motor according to an aspect of the present disclosure. Referring to FIG. 1, there is shown a system 100 operating in conjunction with an electric motor 102 which includes a main winding 102M and one or more auxiliary windings 102A. Further referring to FIG. 1, the system 100 includes an adaptive control unit 104 which encompasses a monitoring unit 106 in communication with one or more sensors 108, one or more machine learning models 110, and a Proportional-Integral-Derivative (PID) controller 112.

[0050] The monitoring unit 106 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to monitor in real-time sensor data from one or more sensors 108. Examples of the one or more sensors 108 may include, but are not limited to, the plurality of sensors comprises at least one of thermocouples, hall effect sensors, load sensors, temperature sensors, and speed sensors.

[0051] The adaptive control unit 104 may comprise suitable logic, circuitry, interfaces and/or code that may be operable to adjust at least one of a voltage or a current input to the electric motor based on real-time feedback based on the sensor data, using the PID controller 112. The real-time feedback is indicative of an error between a desired state of the electric motor 102 and an actual state of the electric motor 102. A state of the electric motor 102 is being associated with an operating parameter of the electric motor 102. The operating parameter of the electric motor 102 may include a speed of the electric motor 102 and/or a torque of the electric motor 102.

[0052] In an implementation, considering the operating parameter of the electric motor 102 is the speed of the electric motor 102, the adaptive control unit 104 is configured to maintain the speed of the electric motor 102 by minimizing an error between a desired speed of the electric motor 102 and an actual speed of the electric motor 102, using the PID controller 112.

[0053] The PID controller 112 may comprise suitable logic, circuitry, interfaces and/or code that may be configured to determine a desired setpoint for the operating parameter of the electric motor 102 based on one or more of a driver’s input, performance requirements, and system conditions.

[0054] In some implementations, the error in the PID controller 112 may be a difference between the desired setpoint (e.g., target motor speed) and an actual value (e.g., current motor speed) of the operating parameter. In some implementations, the error may be based on factors such as, but not limited to, changes in load, variations in input voltage, external disturbances, temperature fluctuations and mechanical wear.

[0055] In an implementation, the adaptive control unit 104 is configured to predict changes in a load of the electric motor and pre-emptively adjust the operating parameter of the electric motor 102, using the one or more machine learning models 110. In some implementations, the one or more machine learning models 110 are trained based on historical data and real-time data.

[0056] In an implementation, the one or more machine learning models 110 are configured to analyze past sensor data to predict load requirements of the electric motor 102, and pre-emptively adjust desired setpoints of the operating parameter (e.g., torque, motor speed) for the PID controller 112 based on the predicted load requirements.

[0057] Aspects of the present disclosure relate to a system and method for sustained high-performance operation in electric vehicle (EV) motors, featuring the adaptive control unit 104 (IntelliControl), continuous energy recovery systems, enhanced peak load handling capabilities, and sustainable operation with reduced maintenance requirements. The system ensures optimal performance, efficiency, and reliability under varying load conditions through one or more algorithms and advanced motor configurations.

[0058] The adaptive control systems (i.e., the adaptive control unit 104) continuously monitors and adjusts motor parameters to optimize performance and prevent overheating. This dynamic control is achieved through a network of sensors 108 and advanced control algorithms, ensuring that the electric motor 102 operates efficiently across varying load conditions.

[0059] Aspects of the present disclosure relate to the real-time monitoring and usage of sensor data using the monitoring unit 106.

[0060] Sensors 108: Examples include high-precision sensors, including thermocouples, hall effect sensors, and load cells, that continuously monitor critical parameters such as, but not limited to, temperature, load, motor speed, and electrical input across all motors.

[0061] Data Acquisition: The sensor data is collected and processed in real-time by the adaptive control unit 104, which uses this information to make instantaneous adjustments.

[0062] Adaptive Control Algorithms are used for peak optimization of performance of the electric motor 102.

[0063] The adaptive control unit 104 employs the PID controller 112 to maintain the desired motor performance by minimizing the error between the desired and actual motor states. The desired setpoint is determined by the control system based on factors such as, but not limited to, driver input, performance requirements, and system conditions. The error in the PID control system (PID controller 112) is the difference between the desired setpoint (e.g., target motor speed) and the actual value (e.g., current motor speed). This error occurs due to various factors, such as changes in load, variations in input voltage, or external disturbances like temperature fluctuations or mechanical wear. The PID controller 112 adjusts voltage and current inputs to the electric motor 102 based on real-time feedback, ensuring stable and efficient operation.

[0064] For example, if the desired motor speed is set to 3000 RPM based on the driver's accelerator input and the actual speed drops to 2900 RPM due to an increased load, the error is 100 RPM. In this case, the PID controller 112 responds by adjusting the input voltage to bring the motor speed back to the desired setpoint.

[0065] In some implementations, the adaptive control unit 104 is further equipped with machine learning algorithms such as, but not limited to, long short-term memory (LSTM) to predict load changes and pre-emptively adjust motor operations. These models are trained based on historical and real-time data to enhance predictive accuracy and responsiveness. The predicted load is given by:

Predicted Load=f(Past Data)

[0066] The machine learning models 110 analyze past sensor data to predict future load requirements. This prediction is used to adjust the setpoints for the PID controller 112 pre-emptively. For example, if the machine learning models 110 predict an increase in load based on past patterns, the machine learning models 110 adjust the desired speed or torque setpoints, allowing the PID controller 112 to handle the anticipated load more effectively.

[0067] Aspects of the present disclosure relate to a continuous energy recovery system that captures energy losses throughout all phases of motor operation, converting this energy into electrical power and feeding it back into the battery. This system ensures maximized energy utilization and efficiency.

[0068] Aspects of the present disclosure further describe a configuration and function of the auxiliary windings 102A of the electric motor 102.

[0069] Configuration: The auxiliary windings 102A are strategically placed within the electric motor 102 to capture energy losses from friction, resistance, and eddy current losses. These windings are designed to operate efficiently across varying motor speeds and load conditions.

[0070] Energy Capture: The captured energy is converted into electrical power and fed back into one or more batteries of the electric vehicle and/or to the electric motor and/or to power the on-board auxiliary systems of the electric vehicle, reducing the need for frequent external charging sessions.

[0071] Aspects of the present disclosure relate to the intelligent energy management integrated with the electric motor 102.

[0072] The IntelliControl system works seamlessly with the continuous energy recovery system to optimize energy utilization. By integrating real-time data from the energy recovery process. The IntelliControl system adjusts motor parameters to ensure efficient energy feedback. The intelligent energy management system determines the optimal rate of energy feedback to the battery based on real-time battery state of charge (SOC), current power load on the motor, and ambient temperature. The energy feedback rate is given by:

Energy Feedback Rate=f(Battery SOC, Power Load, Ambient Temperature)

[0073] For example, the above equation ensures that if the battery SOC is high and the power load is low, the feedback rate is reduced to prevent overcharging. The integration of the adaptive control algorithms with energy feedback data allows the system to fine-tune motor operations dynamically.

Min ∑∣Ideal Feedback − Actual Feedback∣

[0074] The above equation minimizes the difference between ideal and actual energy feedback rates, ensuring optimal energy recovery. For instance, if the actual feedback rate deviates from the ideal rate due to changing conditions, the IntelliControl system adjusts to minimize this deviation, maintaining efficient energy use.

[0075] Aspects of the present disclosure relate to dynamic load balancing or load redistribution using the system described herein. The system employs dynamic load balancing algorithms that redistribute operational loads among multiple motors in real-time. This approach optimizes performance and prevents overheating.

[0076] The system ensures that no single motor is overloaded by evenly distributing the load across all motors. For example, if one motor is approaching its thermal limit, the system redistributes some of its load to other motors.

[0077] By managing the distribution of loads across multiple motors, the system ensures balanced performance and prevents any single motor from being subjected to excessive stress.

[0078] In summary, aspects of the present disclosure relate to a comprehensive system and method for sustained high-performance operation in electric vehicle motors through the integration of IntelliControl adaptive control mechanisms, continuous energy recovery systems, and advanced peak load handling capabilities. The real-time adaptive control systems dynamically monitor and adjust motor parameters to maintain optimal performance and prevent overheating. The continuous energy recovery system captures energy losses throughout all phases of motor operation and feeds it back into the battery, optimizing energy utilization. The IntelliControl system seamlessly integrates with the energy recovery process to fine-tune motor operations dynamically, ensuring efficient energy feedback. Additionally, dynamic load balancing algorithms redistribute operational loads among multiple motors in real-time, preventing overheating and optimizing performance.

[0079] FIG. 2A is a diagrammatic representation of a stator of an induction motor in accordance with an exemplary implementation of the disclosure. Referring to FIG. 2A, there is shown a stator 200 of the induction motor, which includes a frame or yoke 202, a stator core 204, stator slots 206 and stator windings 208.

[0080] The frame or yoke 202 is made of close-grained alloy cast iron or aluminum alloy and forms an integral part of the stator 200. The main function of the frame or yoke 202 is to provide a protective cover for other sophisticated components or parts of the induction motor. The stator core 204 is made up of laminations which include the stator slots 206 that are punched from sheets of electrical grade steel. The space provided in the stator slots 206 is sufficient to accommodate the stator windings 208 that include one or more sets of winding wires. In related aspects, the space provided in the stator slots 206 may be more than in conventional slots. The winding wires are insulated wires. The size of the stator slots 206 may be adjusted and maintained for uniform distribution of the stator windings 208.

[0081] The space provided in the stator slots 206 is configured to accommodate the one or more sets of winding wires which include the main winding (M) which carries the supply power/energy (RMF) required for rotating the rotor and the one or more additional windings (A) which is used for transmission of the power (alternating EMF) induced in the one or more additional windings (A) while the rotor is rotating. The energy produced during the rotation of the rotor meets part of the energy requirement of the induction motor, as the induction motor partly functions as a generator.

[0082] Further, the stator 200 may include rabbets and bore that are punched carefully to ensure uniformity of air gap. The shaft and bearings used in the rotor of the induction motor are like any other conventional induction motor. The heat produced in the induction motor is comparatively less because of less current and low losses.

[0083] A number of poles and a number of windings that will be required for the stator 200 is decided based on the speed of the induction motor as the speed of the induction motor is directly proportional to frequency and inversely proportional to the number of poles according to the equation, N = 120f/P, wherein ‘N’ is the speed, ‘f’ is the frequency and ‘P’ is the number of poles.

[0084] FIG. 2B is a diagrammatic representation of a connection configuration between a main winding and an additional auxiliary winding of the stator to achieve high power factor in accordance with an exemplary implementation of the disclosure. Referring to FIG. 2B, there is shown a main coil 202 of the stator with a start terminal (Ms) and an end terminal (Me) and an auxiliary coil 204 with a start terminal (As) and an end terminal (Ae).

[0085] As depicted in FIG. 2B, the start terminal (As) of the auxiliary coil 204 is connected to the end terminal (Me) of the main coil 202.

[0086] An electronic control unit (ECU) of the induction motor receives the back EMF from the stator coils and provides the same to the auxiliary windings. Thus, the main winding and the multiple auxiliary windings are enabled with more than one power component being feed to each winding. Each winding has more than one power component enabling an efficiency component as:

• Power Component generated from the back EMF induced from the previous coil due to flux cutting.
• Power component generated by the back EMF at the coil due to flux cutting while the rotor is rotating the magnetic field (RMF).

[0087] The above-described connection configuration of the windings reduces the input current component.
[0088] FIG. 3 is a diagrammatic representation of a rotor of an induction motor in accordance with an exemplary implementation of the disclosure. Referring to FIG. 3, there is shown a rotor 300 which includes steel laminations 302, aluminum bars 304, a rotor shaft 306 and end rings 308.

[0089] The rotor 300 includes a cylinder of the steel laminations 302, with the aluminum bars 304. In some implementations, the rotor 300 may include highly conductive metal (typically aluminum or copper) embedded into its surface. At both ends of the rotor 300, rotor conductors are short-circuited by the continuous end rings 308 of similar materials to that of the rotor conductors. The rotor conductors and their end rings 308 by themselves form a closed circuit.

[0090] When an alternating current is run through the stator windings 208, the RMF is produced. This induces a current in the rotor windings, which produces its own magnetic field. The interaction of the magnetic fields produced by the stator and rotor windings produces a torque on the rotor 300.

[0091] FIG. 4 is a flowchart illustrating a method for real-time adaptive control and continuous energy recovery in an electric motor according to an aspect of the present disclosure. Referring to FIG. 4, there is shown a flowchart of a method 400 for real-time adaptive control and continuous energy recovery in the electric motor (e.g., electric motor 102 of FIG. 1).

[0092] Referring to FIG. 4, at 402, the method 400 includes monitoring in real-time sensor data from a plurality of sensors (e.g., sensors 108).

[0093] At 404, the method 400 includes adjusting at least one of a voltage or a current input to the electric motor based on real-time feedback based on the sensor data, using a Proportional-Integral-Derivative (PID) controller (e.g., PID controller 112), wherein the real-time feedback is indicative of an error between a desired state of the electric motor and an actual state of the electric motor, a state of the electric motor being associated with an operating parameter of the electric motor.

[0094] The present invention may be realized in hardware, or a combination of hardware and software. The present invention may be realized in a centralized fashion, in at least one computer system, or in a distributed fashion, where different elements may be spread across several interconnected computer systems. A computer system or other apparatus/devices adapted to carry out the methods described herein may be suited. A combination of hardware and software may be a general-purpose computer system with a computer program that, when loaded and executed on the computer system, may control the computer system such that it carries out the methods described herein. The present invention may be realized in hardware that comprises a portion of an integrated circuit that also performs other functions. The present invention may also be realized as a firmware which form part of the media rendering device.

[0095] The present invention may also be embedded in a computer program product, which includes all the features that enable the implementation of the methods described herein, and which when loaded and/or executed on a computer system may be configured to carry out these methods. Computer program, in the present context, means any expression, in any language, code or notation, of a set of instructions intended to cause a system with information processing capability to perform a particular function either directly, or after either or both of the following: a) conversion to another language, code or notation; b) reproduction in a different material form.

[0096] While the present disclosure is described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted without departure from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departure from its scope. Therefore, it is intended that the present disclosure is not limited to the particular embodiment disclosed, but that the present disclosure will include all embodiments that fall within the scope of the appended claims.
, C , Claims:1. A system for real-time adaptive control and continuous energy recovery in an electric motor, the electric motor comprising a main winding and one or more auxiliary windings in a stator of the electric motor, the system comprising:
an adaptive control unit configured to:
monitor in real-time sensor data from a plurality of sensors; and
adjust at least one of a voltage or a current input to the electric motor based on real-time feedback based on the sensor data, using a Proportional-Integral-Derivative (PID) controller, wherein the real-time feedback is indicative of an error between a desired state of the electric motor and an actual state of the electric motor, a state of the electric motor being associated with an operating parameter of the electric motor.

2. The system of claim 1, wherein the plurality of sensors comprises at least one of thermocouples, hall effect sensors, load sensors, temperature sensors, and speed sensors.

3. The system of claim 1, wherein the operating parameter of the electric motor is at least one of a speed of the electric motor and a torque of the electric motor.

4. The system of claim 3, wherein the operating parameter of the electric motor is the speed of the electric motor, wherein the adaptive control unit is configured to maintain the speed of the electric motor by minimizing an error between a desired speed of the electric motor and an actual speed of the electric motor, using the PID controller.

5. The system of claim 1, wherein the PID controller is configured to determine a desired setpoint for the operating parameter of the electric motor based on at least one of a driver’s input, performance requirements, and system conditions.

6. The system of claim 5, wherein the error in the PID controller is a difference between the desired setpoint and an actual value of the operating parameter.

7. The system of claim 1, wherein the error is based on at least one of changes in load, variations in input voltage, external disturbances, temperature fluctuations and mechanical wear.

8. The system of claim 1, wherein the adaptive control unit is configured to predict changes in a load of the electric motor and pre-emptively adjust the operating parameter of the electric motor, using one or more machine learning models.

9. The system of claim 8, wherein the one or more machine learning models are trained based on historical data and real-time data.

10. The system of claim 9, wherein the one or more machine learning models are configured to:
analyze past sensor data to predict load requirements of the electric motor; and
pre-emptively adjust desired setpoints of the operating parameter (torque, motor speed) for the PID controller based on the predicted load requirements.

11. A method for real-time adaptive control and continuous energy recovery in an electric motor, the electric motor comprising a main winding and one or more auxiliary windings in a stator of the electric motor, the method comprising:
monitoring in real-time sensor data from a plurality of sensors; and
adjusting at least one of a voltage or a current input to the electric motor based on real-time feedback based on the sensor data, using a Proportional-Integral-Derivative (PID) controller, wherein the real-time feedback is indicative of an error between a desired state of the electric motor and an actual state of the electric motor, a state of the electric motor being associated with an operating parameter of the electric motor.

12. The method of claim 11, wherein the plurality of sensors comprises at least one of thermocouples, hall effect sensors, load sensors, temperature sensors, and speed sensors.

13. The method of claim 11, wherein the operating parameter of the electric motor is at least one of a speed of the electric motor and a torque of the electric motor.

14. The method of claim 13, wherein the operating parameter of the electric motor is the speed of the electric motor, wherein the adjusting comprises maintaining the speed of the electric motor by minimizing an error between a desired speed of the electric motor and an actual speed of the electric motor, using the PID controller.

15. The method of claim 11, further comprising determining, using the PID controller, a desired setpoint for the operating parameter of the electric motor based on at least one of a driver’s input, performance requirements, and system conditions.

16. The method of claim 15, wherein the error in the PID controller is a difference between the desired setpoint and an actual value of the operating parameter.

17. The method of claim 11, wherein the error is based on at least one of changes in load, variations in input voltage, external disturbances, temperature fluctuations and mechanical wear.

18. The method of claim 11, further comprising predicting changes in a load of the electric motor and pre-emptively adjusting the operating parameter of the electric motor, using one or more machine learning models.

19. The method of claim 18, wherein the one or more machine learning models are trained based on historical data and real-time data.

20. The method of claim 19, further comprising:
analyzing, using the one or more machine learning models, past sensor data to predict load requirements of the electric motor; and
pre-emptively adjusting, using the one or more machine learning models, desired setpoints of the operating parameter for the PID controller based on the predicted load requirements.