Performance comparison of electric-vehicle drivetrain architectures from a vehicle dynamics perspective

Abstract

Recent electric vehicle studies in literature utilize electric motors within an anti-lock braking system, traction-control system, and/or vehicle-stability controller scheme. Electric motors are used as hub motors, on-board motors, or axle motors prior to the differential. This has led to the need for comparing these different drivetrain architectures with each other from a vehicle dynamics standpoint. With this background in place, using MATLAB simulations, these three drivetrain architectures are compared with each other in this study. In anti-lock braking system and vehicle-stability controller simulations, different control approaches are utilized to blend the electric motor torque with hydraulic brake torque; motor ABS, torque decomposition, and optimal slip-tracking control strategies. The results for the anti-lock braking system simulations can be summarized as follows: (1) Motor ABS strategy improves the stopping distance compared to the standard anti-lock braking system. (2) In case the motors are not solely capable of providing the required braking torque, torque decomposition strategy becomes a good solution. (3) Optimal slip-tracking control strategy improves the stopping distance remarkably compared to the standard anti-lock braking system, motor anti-lock braking system, and torque decomposition strategies for all architectures. The vehicle-stability controller simulation results can be summarized as follows: (1) higher affective wheel inertia of the on-board and hub motor architecture dictates a higher need of wheel torque in order to generate the tire force required for the desired yaw rate tracking. A higher level of torque causes a higher level of tire slip. (2) Optimal slip-tracking control strategy reduces the tire slip trends drastically and distributes the traction/braking action to each tire with the control-allocation algorithm specifying the reference slip values. This reduces reference tire slip-tracking error and reduces vehicle sideslip angle. (3) Tire slip trends are lower with the hub motor architecture, compared to the other architectures, due to more precise slip control.

Design and Analysis of DYC and Torque Vectoring using Multiple-Frequency Control Electronic Differential in an Independent Rear Wheel Driven Electric Vehicle

“…Optimal control strategy for slip-tracking drastically reduces the tire slip trends and distributes the required torque abiding by the control-allocation algorithm following the reference slip value [38].…”

Active motor damping strategy for driveline vibrations of a hybrid electric vehicle during ABS operation

“…It should also be stated that although adaptive control theory is not applied in this work, the same control algorithm can be modified easily for different hybrid electric vehicle architectures, such as on board or in wheel motors. 11 In such a case the only difference is that the open differential would not need to be modeled for control development purpose.…”

“…In addition to the aforementioned studies that focus on minimization of the half shaft torque by minimizing the angle of twist and/or the shaft wrap angular speed during acceleration or mild braking, there are also studies in literature that focus on controlling the electric motor during ABS braking. [8][9][10][11][12][13][14] In all these studies, the net desired wheel torque is supplied by blending the hydraulic brake torque and the electric motor torque. In other words, electric motor torque and hydraulic brake torque are controlled simultaneously for tire slip regulation.…”

Optimal adaptive race strategy for a Formula-E car

“…Intermediate values of equivalence factors can then be obtained by linearly interpolating within the discretized values. Considering five discretized values for each of the three variables affecting the equivalence factors leads to retain 5 3 = 125 different values to be calibrated for each equivalence factor, thus 250 parameters in total. The consideration of five discretized elements of equivalence factors with respect to the values of both SOC, T batt , and lap number stems from seeking a trade-off between control accuracy and computational cost.…”

“…1,2 As example, a large variety of forward-looking technologies has been proposed regarding vehicle drivetrain electrification and the related energy storage system and power electronics. [3][4][5][6][7] In this framework, the institution of the Formula E Ò race championship by the Federation Internationale de l'Automobile (FIA) in 2014 has also been aimed at further fostering these deep technological innovations. 8 Formula E Ò as a new motorsport category for pure electric road vehicles indeed targets not only the accelerated advancement of transportation electrification, 9 but it also represents a crucial playground for the digitalization of race competitions.…”