Description:FIELD OF INVENTION
The present invention in general relates to a control system for electric vehicles. More particularly, the present invention relates to the control system for controlling the motion of the tracked electric vehicles.

BACKGROUND OF THE INVENTION

The use of automobiles has been increasing worldwide and with that has increased the need to develop vehicles capable of optimum use of energy resources for both highway and off-road driving, providing safe as well as comfortable transportation with minimal time and minimal impact on the environment. However, the challenge is to develop vehicles capable of satisfying such diverse and conflicting requirements.
To meet such challenges, automobiles are increasingly relying on electromechanical subsystems that employ sensors, actuators and feedback control. Further the electronic stability control system may determine that the vehicle has lost directional stability based on a deviation of the vehicle's actual measured states from desired states such as deviation of an actual yaw rate of the vehicle from a desired yaw rate.
Also, one major issue related to the automobiles are energy efficiency, as energy efficiency in electric vehicles (EVs) is crucial to maximizing their performance, range, and sustainability. Several issues are associated with energy efficiency in EVs, which impact both consumers and the broader adoption of electric transportation. Here are some of the key challenges which includes, battery efficiency and energy density, aerodynamics and vehicle design, energy loss in powertrain components, environmental factors, charging time, energy conversion losses.
A US patent application US8457858B2 entitled “vehicle motion control apparatus” discloses a driving/braking force control unit that calculates tractive forces of front and rear axles that minimize energy loss realizing target steering characteristics.
Another US patent application US10407035B1 outlines a control system for vehicles and includes a plurality of vehicle actuators that are operable to affect actual chassis-level accelerations, a vehicle intelligence unit that determines a motion plan, a vehicle motion control unit that determines a chassis-level motion request based on the motion plan, and a chassis control unit that determines actuator commands for the plurality of vehicle actuators based on the chassis-level motion request.
Another US patent application US8655563B2 titled “Braking/driving force controller of vehicle” discloses about a braking/driving force control apparatus, a vehicle target braking/driving force and a vehicle target yaw moment through the control of braking/driving forces of wheels are calculated, and when the target braking/driving force and the target yaw moment cannot be achieved through the control of the braking/driving forces of the wheels, the vehicle target braking/driving force after the modification and the vehicle target yaw moment after the modification are calculated such that, within the range where the ratio of the vehicle target braking/driving force after the modification and the vehicle target yaw moment after the modification coincides with the ratio of the target braking/driving force and the target yaw moment, the vehicle braking/driving force and the vehicle yaw moment by the target braking/driving forces of the wheels take the greatest values.

Another US patent application US008355844B2 entitled “vehicle motion control apparatus” discloses a vehicle motion control apparatus includes a control unit and sensors. The vehicle motion control apparatus used to regulate and optimize vehicle stability, particularly in terms of yaw (rotation around the vehicle's vertical axis) and slip angles, which are important for controlling the vehicle's handling and preventing instability such as oversteering or understeering. Further, the vehicle motion control apparatus includes a control unit and sensors. The actual state quantity obtaining unit calculates a vehicle body actual slip angle BZ act, etc. The reference dynamic-characteristic model calculating unit calculates a reference vehicle body slip angle BZ d, etc. by using a dynamic characteristic model. The vehicle motion control apparatus also includes a first anti-spin target yaw moment FB unit which calculates a first anti-spin-target yaw moment Mc1 asp based on the vehicle body actual slip angle BZ act and a second anti-spin target yaw moment FB unit which calculates a second anti-spin-target yaw moment Mc2 asp based on a lateral acceleration Gs, a vehicle speed Vact and an actual yaw rate Yact.
The actual state quantity obtaining unit calculates a vehicle body actual slip angle BZ act, etc. The reference dynamic-characteristic model calculating unit calculates a reference vehicle body slip angle BZ d, etc. by using a dynamic characteristic model. The vehicle motion control apparatus also includes a first anti-spin target yaw moment FB unit which calculates a first anti-spin-target yaw moment Mc1 asp based on the vehicle body actual slip angle BZ act and a second anti-spin target yaw moment FB unit which calculates a second anti-spin-target yaw moment Mc2 asp based on a lateral acceleration Gs, a vehicle speed Vact and an actual yaw rate Yact.
A non-patent literature entitled “applications of model predictive control to vehicle dynamics for active safety and stability” discloses a model predictive control structure capable of keeping the vehicle within the safe handling boundaries is the final component of the envelope control system. The design of a controller that is capable of smoothly and progressively augmenting the driver steering input to enforce the boundaries of the envelope. The model predictive control formulation provides a method for making trade-offs between enforcing the boundaries of the envelope, minimizing disruptive interventions, and tracking the driver’s intended trajectory.
The patents and publications mentioned above outline the methods and systems for integrated chassis control to enhance safety and drivability, but do not pertain to control of terramechanics effects which are predominant in tracked vehicle applications. Further, the prior arts also do not account for skid steering using synchronous control of e-Powertrain and brakes to minimize power consumption.
Therefore, keeping in view the problem associated with the state of the art there is a need for an improved control system, for tracked electric vehicles, which is capable of energy and torque management on tracked vehicles that utilize a fully electric powertrain.

OBJECTIVES OF THE INVENTION

The primary objective of the present invention is to provide a control system for energy and torque management on tracked electric vehicles.
Another objective of the present invention is to provide the system and method to control the motion of tracked battery electric vehicles that address the problem of energy and torque management on tracked vehicles that utilize a fully electric powertrain.
Another objective of the present invention is to control, optimize and manage the traction e-Powertrain torque of battery electric vehicles that utilize a fully electric powertrain.
Yet another objective of the present invention is to provide a system and method to control the traction systems for tracked vehicles that control the vehicle steering and lateral maneuverability on a wide variety of surfaces using skid steering mechanism while minimizing energy consumption from the energy storage system (130) (ESS).
Still another objective of the present invention is to provide a system and method to control the tracked battery electric vehicles having reliable real-time prediction of terramechanics in order to predict track resistance and track slip.
Other objectives and advantages of the present invention will become apparent from the following description taken in connection with the accompanying drawings, wherein, by way of illustration and example, the aspects of the present invention are disclosed.

SUMMARY OF THE INVENTION
The present invention relates to an integrated chassis control system for a tracked electric vehicle, with a centralized supervisory control architecture comprising a central torque controller, an e-Powertrain system, an electromechanical friction braking system, an energy storage system and a plurality of sensors. The disclosed control system is used to predict track resistance and track slip in real time to optimally control vehicle level velocities and accelerations while minimizing ESS power consumption. Traction motors installed at a plurality of positions on the tracked electric vehicle comprise the e-Powertrain system and are controlled by the central torque controller. An electromechanical braking system comprising actuators and pressure sensors, arranged in the tracked electric vehicle, is also controlled by the central torque controller. The central torque controller is configured to monitor a number of system parameters including, but not limited to, voltage, RPMs, currents, temperatures, accelerations, velocities, battery charge and constituent system statuses; and is configured to estimate track-surface interaction parameters to generate energy-optimized driving and braking torque commands tracking reference maneuver requests and maintaining system response within the desired vehicle-level performance envelope and user requirements.
Further, the e-Powertrain system comprises of a left traction motor and a right traction motor along with integrated sensors and lower level regulatory controllers, mechanically coupled to the left and right gearboxes, respectively. Further, the braking system comprises the left brake actuator and the right brake actuator to activate the left brake and the right brake, respectively. Further, the left brake pressure sensor and the right brake pressure sensor are electronically connected to the central torque controller, for computing the respective braking torques. The central torque controller estimates vehicle states and track-terrain interaction parameters, and generates energy-optimized torque commands on receiving a maneuver request from the user, and comprises an upper controller which is configured to estimate terra mechanics parameters and to compute target vehicle level accelerations and velocities based on motion requests, which are then translated into reference velocities that are compatible with the state space model used to formulate the LTV-MPC algorithm. A lower controller is configured to execute the LTV-MPC algorithm to arrive at the optimal e-Powertrain and braking torques that minimize energy consumption from the ESS.

BRIEF DESCRIPTION OF DRAWINGS

The present invention will be better understood after reading the following detailed description of the presently preferred aspects thereof with reference to the appended drawings, in which the features, other aspects and advantages of certain exemplary embodiments of the invention will be more apparent from the accompanying drawing in which:
Figure 1 illustrates an exemplary diagram showing shearing stress is initiated at the track-terrain interface on application of torque to the wheel or the sprocket of a track;
Figure 2 illustrates the physical dynamics of tracked vehicles;
Figure 3 illustrates a block diagram of the architecture of the invention;
Figure 4 describes the electronic system architecture used to implement the preferred embodiment of the invention;
Figure 5 provides the top-level control architecture and signal flow diagram of the invention;
Figure 6 provides the method of operation for the upper controller; and
Figure 7 provides the method of operation for the lower controller.

DETAILED DESCRIPTION OF THE INVENTION

The following section describes various features and functions of the disclosed control system with reference to the accompanying figures. In the figures, similar symbols identify similar components, unless context dictates otherwise. The illustrative aspects described herein are not meant to be limiting. It may be readily understood that certain aspects of the disclosed control system can be arranged and combined in a wide variety of different configurations, all of which have not been contemplated herein.
Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
The terms and words used in the following description are not limited to the bibliographical meanings, but, are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustrative purpose only and not for the purpose of limiting the invention.
It is to be understood that the singular forms “a”, “an” and “the” include plural referents unless the context clearly dictates otherwise.
It should be emphasized that the term “comprises/comprising” when used in this specification is taken to specify the presence of stated features, steps or components but does not preclude the presence or addition of one or more other features, steps, components or groups thereof.
The tracked battery electric vehicles require a skid steering mechanism which leads to significant energy loss thereby limiting the driving range of the vehicle. Further, the surface characteristics known as terra mechanics play an important role in determining energy loss.
The present invention relates to a control system (200) present on the vehicle (100) for energy and torque management of tracked battery electric vehicles and a method thereof. The system (200) is provided with a traction system, for tracked vehicles, which is adaptable to control vehicle steering and lateral maneuverability on one or more types of surfaces while minimizing energy consumption of the energy storage system (130) (ESS).
The present invention provides a system for energy and torque management of tracked battery electric vehicles and a method thereof. Further, the control system (200) present in the vehicle (100) comprises an e-Powertrain system (110 a-b) comprising a plurality of traction motors with lower level controllers (112 a-b) and gearboxes (114 a-b), a plurality of electromechanical braking systems (120 a-b), a plurality of sensors (140, 112 a-b, 124 a-b, 132), an energy storage system (130) with a battery management system (132), and a central torque controller (150).
The central torque controller (150) is configured to receive maneuver requests (acceleration/deceleration and steering), and for estimating track-surface interaction parameters, calculating and monitoring system parameters including voltage, RPMs, currents, temperatures, accelerations, velocities, battery charge and constituent system statuses, and generating energy-optimized driving and braking torque commands that track reference maneuver requests and maintain system response within the desired vehicle-level performance envelope and user requirements.
The traction motors with integrated sensors and controllers (112 a-b) are mechanically coupled to respective gearboxes (114 a-b), and comprise a plurality of sensors including but not limited to rotor AMR sensors, and current, voltage and temperature sensors for the rotor, stator and power electronics. Each traction motor also comprises of a lower level motor controller unit, responsible for regulatory control of the power electronics to generate the motor torque computed by the central torque controller.
The track-terrain interaction mathematical model addressing terramechanics, tractive effort and compaction resistance, lateral resistance and tracked vehicle dynamics are explained in the embodiments below.
In an embodiment, when a torque is applied to the wheel or the sprocket of a track, shearing stress is initiated at the track-terrain interface, as shown in Figure 1. The tractive effort Ft of the track is produced by the shear displacement of the terrain. Maximum tractive effort Ft, max that may be developed by the track depends on the shear strength tmax of the terrain and is defined as:
Ft,max = ? ? tmax dA (1)

Where dA is an infinitesimally small area at the track-terrain interface. Assuming a constant pressure distribution, this relationship may be approximated as:
Ft,max = tmax A (2)

Where A is the total contact area between the track-terrain interface. The relationship between max shear stress tmax and normal pressure s is defined as:
tmax = c + s tan ? (3)
Where c is the apparent cohesion coefficient and ? is the angle of internal shearing resistance.
In longitudinal vehicle dynamics models explained in the embodiment, it is often preferred to correlate tractive effort with track slip ratio over the full operating range. The slip ratio is defined as:

Where rs is the driving sprocket radius, ? is the sprocket angular velocity and Vx is the vehicle longitudinal velocity. Assuming a uniform normal pressure distribution s = Fz/(bl) , the tractive effort may be computed as :

This tractive effort is opposed by the compaction resistance, computed as:

Fz is the vertical load [N]
Ft is the tractive effort [N]
Rc is the compaction resistance [N]
A is the contact area at track-terrain interface [m2]
l is the track length [m]
b is the track width [m]
c is the cohesion coefficient [Pa]
? is the angle of internal shearing resistance [?]
K is the shear deformation modulus [m]
s is the track slip ratio [-], s ? [-1, 1]
rs is the sprocket radius [m]
n is the sinkage exponent [m]
kc is cohesive modulus [kN/mn+1]
k? is frictional modulus [kN/mn+2]

Lateral Resistance

Due to the existence of yaw rate r and CG sideslip angle ß during vehicle cornering, track elements would have an associated lateral velocity. Hence, due to the cohesive and frictional properties of the track-terrain interface, the tracks would experience a lateral resistance Rl during cornering.
Lateral resistance may be computed using:

Where µt is the coefficient of lateral resistance, and s0 is the distance between vehicle CG and yaw center (center of rotation about the vertical axis).

Tracked Vehicle Dynamics
From the derived equations, a mathematical model governing the dynamics of the tracked vehicle may be formulated.
Tractive effort on the two tracks may be modeled as a function of their respective slip ratios as:

Compaction resistances acting on the tracks may be modeled as:

Lateral resistances acting on the tracks may be modeled as:
The corresponding moment caused by lateral resistances is thereby modeled as:

The dynamic model of the tracked vehicle may therefore be written as:

Where B is the lateral distance between the track centerlines, Izz is the vehicle yaw inertia, and V is resultant vehicle velocity computed as

Assuming small sideslip angle, distance from the center of rotation to the CG may be approximated:
Where R is the instantaneous turn radius which may be computed as:

The torques on the sprocket and driveshaft may be computed using the tractive force reference value and the sprocket radius using the general equation
Corresponding torque values are thereafter used as initial guesses for the LTV-MPC algorithm.
Figure 2 describes the overall physical dynamics of tracked vehicles, wherein the tractive forces generated by the e-Powertrain and brakes are represented by their respective torques, i.e., [TqM1, TqB1, TqM2, TqB2], along with vehicle velocities, i.e., longitudinal velocity vx , lateral velocity vy , and yaw rate r. In the above example, TqB1 is 0 as the vehicle is taking a left-hand turn and does not require braking torque on the outside track, as shown in Figure 2. It may be seen from the figure that a difference in left and right tractive forces is necessary to steer the vehicle about the vertical axis. In some cases, as shown above, tractive force on one of the tracks might be positive while the tractive force on the other track might be negative. This opposition of forces leads to significant energy loss during cornering, and thus must be carefully controlled to maximize available driving range. It should also be noted that the aforementioned forces must be controlled within the limits of the traction envelope, defined by the shear strength of the track-terrain interface. Exceeding this limit would lead to track slip, resulting in drastic energy loss.
The physical system architecture defined in Figure 3 outlines the physical architecture of the invention. Further, the central torque controller (150) synchronizes the e-Powertrain system (110 a-b) and the electromechanical braking system (120 a-b) to achieve energy-optimized tractive forces. A plurality of sensors (140, 112 a-b, 124 a-b, 132), including AMR sensors for rotor speed and position measurement, pressure sensors for computing braking torque, suspension position sensors to estimate load transfer and terramechanics, inertial measurement unit (IMU) for measuring vehicle level accelerations and velocities, and current and voltage sensors interfaced through the battery management system (132) are used to provide necessary feedback information to the central torque controller (150). Further, there are at least four sets of plurality of sensors (140, 112 a-b, 124 a-b, 132) and each set is coupled to the vehicle, the e-Powertrain, the brakes, and the energy storage system(130), respectively.
The electronic system architecture described in Figure 4 elaborates the preferred embodiment of the invention. Controller area network (CAN) bus is used as the medium of communication between the central torque controller (CTC) (150) and lower level ECUs responsible for regulatory control of e-Powertrain and electromechanical friction brakes. Sensor data fetched from lower level ECUs is communicated back to the CTC (150) via CAN.
The control system is implemented by a firmware program that is stored in the ROM of the CTC memory module (158). Sensor data and other real-time information is stored in the RAM of the CTC memory module (158). Both memory modules are integrated within the CTC microprocessor (152). The CTC microprocessor uses multiple clock rates and priority-based task allocation which ensures real-time optimization algorithms do not run into computational limitations. Input/output interface of the CTC (156) receives and sends data to the CAN bus.
The control architecture describes a system and further explains the state space model formulation, state estimation and linear time variant model predictive control (LTV-MPC) algorithm.
The control architecture described in Figure 5 provides the top-level control architecture and signal flow diagram of the invention. The central torque controller (150) is subdivided into upper and lower controllers. The upper controller (210) receives motion requests for acceleration/braking and steering from the user, along with vehicle status signals. Depending on the vehicle sensor and state estimator feedback (for both the vehicle and track-surface interaction), target or reference values for vehicle velocities (vx , vy & r) are computed and sent to the lower controller, which in turn executes a linear time-variant model predictive control (LTV-MPC) algorithm to generate energy-optimized e-Powertrain and braking torque commands that fulfil the aforementioned vehicle motion request. These torque commands are then communicated to lower layer electronic control units (ECUs) for the e-Powertrain and braking actuators, respectively.
State Space Model Formulation
The state space model formulation elaborates the dynamics of tracked vehicle may be written in state space form as

Where:
x (t) = [vx, vy, r]T is the state vector
u (t) = [TqM1, TqB1, TqM2, TqB2]T is the control input vector
In order for the control system to run on digital microcontrollers, the continuous state space model as defined by equations 23 and 24 must be discretized.
Hence, we get the following linear time variant (LTV) state-space model:
x (k + 1) = A(k)x(k) + B(k)u(k) (25)
Where the discrete-time state-transition matrix and control input matrix are computed in real-time for the current operating point in each sample.
State Estimation
The state estimation is required since the control system requires track-terrain parameters to arrive at initial torque guesses for the LTV-MPC algorithm and to reconstruct the full state feedback for closed loop control. Hence, these parameters must be estimated in real-time.
Terrain parameters must be estimated for each track, i.e., for i ? {1, 2} and include the following:

In addition to the track-terrain interaction parameters, certain vehicle metrics are also estimated in real-time to minimize sensor and other instrumentation errors. These include:

In the preferred embodiment of the invention, the aforementioned parameters are estimated using an Unscented Kalman Filter (UKF), which is a method for calculating the statistics of a random variable which undergoes a nonlinear transformation.
Consider propagating a random variable x (having dimension L) through a nonlinear function, y = g(x). Assume x has mean x¯ and covariance Px. To calculate the statistics of y, we form a matrix ? of 2L + 1 sigma vectors ?i (with corresponding weights Wi ), according to the following:

where ? = a2(L + ?) - L is a scaling parameter. a determines the spread of the sigma points around x¯ and is usually set to a small positive value (e.g., 10-3). ? is a secondary scaling parameter which is usually set to 0, and ß is used to incorporate prior knowledge of the distribution of x (for Gaussian distributions, ß = 2 is optimal). v (p(L + ?)Px)i is the ith row of the matrix square root. These sigma vectors are propagated through the nonlinear function:
?i = g (?i) where i = 0, ..., 2L (31)
and the mean and covariance for y are approximated using a weighted sample mean and covariance of the posterior sigma points:
Using the aforementioned technique, the estimated full-state feedback matrix is reconstructed as:

Where x^ in equation (34) represents the estimated vehicle states, and t^ in equation (35) represents the estimated track-terrain interaction parameters. Estimated value of CG sideslip angle ß ^ is used for the real-time computation of state-transition matrix A and control input matrix B.
Thus, full state feedback is reconstructed using UKF-based state estimation.
Linear Time Variant Model Predictive Control (LTV-MPC)
The linear time variant model predictive control (LTV-MPC) algorithm formulates the invention by the following constrained optimization problem:

where np is the length of the prediction horizon and the optimal solution of this problem is the sequence of optimal future inputs.

The current state x^k as estimated by the UKF algorithm is chosen as the operating point for LTV state-space model. The states are weighted by
while inputs are penalized by within the quadratic objective function (equation 36). Selection of Q and R is paramount for arriving at a suitable compromise between performance and energy efficiency. Since this selection may depend on various practical considerations, it is included as a user-defined controller calibration. Input constraints are recalculated at every sampling period according to the maximal forces that the track-terrain interface may handle at the current state, within e-Powertrain system (110 a-b) and ESS limitations.
Upper controller (210) is responsible for estimating terramechanics parameters and computing vehicle level accelerations based on motion requests, which are then translated into reference velocities that are compatible with the state space model used to formulate the LTV-MPC algorithm. This algorithm is executed on the lower controller (220) to arrive at the optimal e-Powertrain (110 a-b) and braking torques (120 a-b) that minimize energy consumption from the ESS (130). The distribution of braking torques between the e-Powertrain (regeneration) and friction brakes is thus determined in real-time using the e-Powertrain efficiency map and ESS limitations. Regenerative braking is maximized until limits as defined by safety set points are reached.
In operation, consider the following exemplary scenario. When the vehicle (100) is switched on, the central torque controller (150) is activated and starts executing the control method 210 on the upper controller and control method 220 on the lower controller. Control methods 210 and 220 cumulatively form the overall control method 200 of the system 100.
Figure 6. illustrate the upper controller 210 which is responsible for computing reference vehicle velocities from user-specified motion requests. The control method 210 initiates in step 211 by reading inputs for vehicle motion request by the user, i.e., acceleration/braking and steering inputs, and any combination thereof. State estimation algorithms compute surface gradient and banking in step 212. Step 213 follows with the computation of left and right track vertical loads according to the values obtained in step 212. Thereafter, real-time terramechanics parameters are computed in step 214 using state estimation algorithms, taking into consideration track vertical loads computed in step 213, along with any dynamic 3-D accelerations measured using the IMU and suspension position sensors. Steps 215 and 216 cumulatively compute the reference values for vehicle-level longitudinal, lateral and yaw motion request, wherein terramechanics-based vehicle-level performance envelope is computed in step 215, followed by the computation of reference vehicle velocities in step 216 which act as reference tracking inputs for the lower controller (220).
Figure 7 describes the lower controller (220) which aims at minimizing the tracking error between reference velocities and actual vehicle velocities, with the objective of minimizing power consumption in the process. Thus, the lower torque controller is responsible for real-time optimization of e-Powertrain and electromechanical braking system torque commands, such that vehicle motion request is fulfilled with minimum possible energy consumption. Control method 220 for the lower torque controller initiates by reading reference values for lateral, longitudinal and yaw velocities computed by the upper controller 210. Steps 222 to 227 collectively form the optimization loop of the control algorithm. Step 222 computes the dynamic actuation limits for the e-Powertrain and braking systems, accounting for system-level limitations as well as terramechanics-based vehicle-level performance envelope. Steps 223a and 224a collectively simulate constituent system-level behavior over the defined simulation time horizon to predict critical system states such as currents, temperatures and voltages. The feasibility of the predicted system states is determined in step 225a. Similarly, steps 223b and 224b collectively simulate vehicle-level behavior using a terramechanics-based vehicle dynamics model accounting for track slip and track resistance, wherein the terramechanics parameters are determined in real-time at step 214. Feasibility of vehicle-level response is checked in step 225b to minimize tracking error between predicted vehicle response and reference vehicle velocities computed in step 216. Step 226 computes and predicts overall power and energy consumption using the vehicle dynamics models and system-level models. Once the combination of e-Powertrain and braking torques that minimize power consumption from the ESS are determined in step 227, these torque command values are sent to the respective lower level ECUs via the communication network present on the vehicle.
A method of operating the control system (200), comprising:
receiving, by the upper controller (210), motion request for acceleration/braking and steering from the user;
calculating, based on the sensor and state estimator feedback, reference values for vehicle velocities (vx , vy & r) and transferred to the lower controller (220);
executing, using a linear time-variant model predictive control (LTV-MPC) instructions, the reference values for obtaining energy-optimized e-Powertrain and braking system torque commands; and
transferring the torque commands to the lower layer electronic control units (ECUs) for operating the e-Powertrain and braking system actuators.

The method of working of the upper controller (210 ) , comprising:
reading user inputs for acceleration/braking and steering and feedback signals from the plurality of sensors ;
estimating surface gradient and banking using IMU;
estimating left and right track vertical loads using IMU, suspension position sensors and road geometry estimation model;
estimating real-time terramechanics parameters using track vertical loads;
computing dynamic longitudinal, lateral and yaw acceleration performance envelope for the tracked vehicle using real-time terramechanics parameters;
computing reference values for longitudinal, lateral and yaw velocities according to user inputs and dynamic performance envelope; and
transferring, to the lower controller (220), the reference values for longitudinal, lateral and yaw velocities.
The method of working of the lower controller (220), comprising:
executing a linear time-variant model predictive control algorithm using a lower controller of the central torque controller to determine optimal e-Powertrain and braking torques.
Further, the one or more factor, include but not limited to, motoring torques for e-Powertrain system, regenerative braking torques for e-Powertrain system, and friction braking torques for electromechanical braking system. Further, the dynamic system-level behaviour is predicted over specified time horizon for e-Powertrain system, electromechanical braking system and ESS.
Further, the dynamic vehicle-level behaviour is predicted over specified time horizon using real-time vehicle dynamics simulations with terramechanics-based traction models. Further, the one or more parameters, for the system level performance, include but not limited to current draw, temperature rise and voltage drop over pre-specified time duration. Further, the one or more parameter, for the vehicle level performance, include but not limited to, lateral, longitudinal and yaw velocities for combinations of e-Powertrain and braking system torques, using predictions of terramechanics-based track slip and track resistance over pre-specified time horizon . Further, the lower torque controller computes the dynamic actuation limits for e-Powertrain and braking systems for every time step, and if any torque command is found to be beyond the upper and lower actuation limits, it is re-computed and brought within the actuation limits by the LTV-MPC algorithm.
While the present invention has been described with reference to one or more preferred aspects, which also have been set forth in considerable detail for the purposes of making a disclosure of the invention, such aspects are merely exemplary and are not intended to be limiting or represent an exhaustive enumeration of all aspects of the invention. Further, it will be apparent to those of skill in the art that numerous changes may be made in such details without departing from the principles of the invention.
, Claims:WE CLAIM:
1. An integrated chassis control system (200) for a tracked electric vehicle, comprising:
(a) an e-Powertrain system(110 a-b) for providing driving torque and braking torque;
• a left traction motor and a right traction motor, each with integrated sensors and lower level controllers; and
• a left gearbox and a right gearbox mechanically coupled to the left traction motor and right traction motor, respectively.
(b) an electromechanical braking system (120 a-b) for providing braking torque;
• a left brake actuator and a right brake actuator; and
• a left brake pressure sensor and a right brake pressure sensor electronically connected to the central torque controller.
(c) an energy storage system(130) for supplying power; and
(d) a plurality of sensors (140, 112 a-b, 124 a-b, 132) for providing feedback data;
• vehicle sensors coupled to the vehicle (100);
• e-Powertrain sensors coupled to the e-Powertrain system (110 a-b) ;
• brake sensors coupled to the electromechanical braking system (120 a-b); and
• energy storage system sensors coupled to the energy storage system (130).
wherein,
i. a central torque controller (150) connected to the e-Powertrain system (110 a-b), the electromechanical braking system (120a-b), the energy storage system (130), and the plurality of sensors, wherein the central torque controller is configured to:
• receive maneuver requests,
• estimate real-time terramechanics interaction parameters and vehicle acceleration performance envelope,
• calculate dynamic system parameters in real time, and
• generate energy-optimized driving and braking torque commands.
ii. the central torque controller comprises:
• an upper controller configured to estimate terramechanics parameters and compute target vehicle level accelerations and velocities based on motion requests; and
• a lower controller configured to execute a linear time-variant model predictive control algorithm to determine optimal e-Powertrain and braking torques.
2. The integrated chassis control system for a tracked electric vehicle as claimed in claim 1, wherein the system parameters include but not limited to monitoring voltage, RPMs, currents, temperatures, accelerations, velocities, battery charge and constituent system statuses.
3. The integrated chassis control system for a tracked electric vehicle as claimed in claim 1, wherein energy-optimized driving and braking torque commands are generated while minimizing energy consumption from the energy storage system (130) and maintaining system response within the desired vehicle-level performance envelope and user requirements.
4. A method of operating the control system of claim 1, comprising:
• receiving, by the upper controller (210), motion request for acceleration/braking and steering from the user;
• calculating, based on the sensor and state estimator feedback, reference values for vehicle velocities (vx , vy & r) and transferring to the lower controller (220);
• executing, using a linear time-variant model predictive control (LTV-MPC) algorithm, the reference values for obtaining energy-optimized e-Powertrain and braking torque commands; and
• transferring the torque commands to the lower layer electronic control units (ECUs) for operating the e-Powertrain and braking actuators.

5. The method of working of the upper controller (210) , comprising:
• reading user inputs for acceleration/braking and steering and feedback signals from the plurality of sensors ;
• estimating surface gradient and banking using IMU;
• estimating left and right track vertical loads using IMU, suspension position sensors and road geometry estimation model;
• estimating real-time terramechanics parameters using track vertical loads;
• computing dynamic longitudinal, lateral and yaw acceleration performance envelope for the tracked vehicle using real-time terramechanics parameters;
• computing reference values for longitudinal, lateral and yaw velocities according to user inputs and dynamic performance envelope; and
• transferring, to the lower controller (220), the reference values for longitudinal, lateral and yaw velocities.
6. The method of working of the lower controller (220) , comprising:
• reading, by the lower controller, the reference values for longitudinal, lateral and yaw velocities;
• computing, based on safe system-level limits and terramechanics-based vehicle-level performance envelope, upper and lower dynamic actuation limits for the e-Powertrain and electromechanical braking systems;
• predicting dynamic system level behavior and vehicle level behavior over pre-specified time horizon for torque values upto upper and lower dynamic actuation limits;
• simulating system level performance and vehicle level performance in real-time using one or more parameters;
• analyzing the one or more parameters, for maintaining the safe system level limits and for maintaining the required vehicle level acceleration performance;
• predicting, on successfully meeting safe system limits and required vehicle level acceleration performance, power consumption from ESS over pre-specified time horizon using the vehicle dynamics models and the system models for e-Powertrain and braking systems;
• analyzing the power consumption from the ESS for the given combination of torques and finding combination of torques which minimize the power consumption from the ESS;
• sending e-Powertrain and electromechanical braking system torques to the respective lower level controller ECUs.
7. The method of working of the lower torque controller (220) as claimed in claim 6, wherein the one or more factor, include but not limited to, motoring torques for e-Powertrain system, regenerative braking torques for e-Powertrain system, and friction braking torques for electromechanical braking system.
8. The method of working of the lower torque controller (220) as claimed in claim 6, wherein the dynamic system-level behaviour is predicted over specified time horizon for e-Powertrain system, electromechanical braking system and ESS.
9. The method of working of the lower torque controller (220) as claimed in claim 6, wherein the dynamic vehicle-level behaviour is predicted over specified time horizon using real-time vehicle dynamics simulations with terramechanics-based traction models.
10. The method of working of the lower torque controller (220) as claimed in claim 6, wherein the one or more parameters, for the system level performance, include but not limited to current draw, temperature rise and voltage drop over pre-specified time horizon.
11. The method of working of the lower controller (220) as claimed in claim 6, wherein the one or more parameters, for the vehicle level performance, include but not limited to, lateral, longitudinal and yaw velocities for combinations of e-Powertrain and braking system torques, using predictions of terramechanics-based track slip and track resistance over pre-specified time horizon.
12. The method of working of the lower controller (220) as claimed in claim 6, wherein the lower controller (220) re-computes, on not meeting vehicle level acceleration performance requirements, or/and exceeding the upper and lower dynamic actuation limits for one or more systems, or/and if the power consumption from the ESS is not minimized for the given combination of torques.