Research on Optimal Torque Control of Turning Energy Consumption for EVs with Motorized Wheels

Wen Sun, Juncai Rong, Junnian Wang, Wentong Zhang and Zidong Zhou
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Wen Sun: Changzhou Institute of Technology, College of Automotive Engineering, Changzhou 213001, China
Juncai Rong: Changzhou Institute of Technology, College of Automotive Engineering, Changzhou 213001, China
Junnian Wang: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Wentong Zhang: School of Machinery and Rail Transit, Changzhou University, Changzhou 213001, China
Zidong Zhou: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China

Energies, 2021, vol. 14, issue 21, 1-15

Abstract: This paper aims to explore torque optimization control issue in the turning of EV (Electric Vehicles) with motorized wheels for reducing energy consumption in this process. A three-degree-of-freedom (3-DOF) vehicle dynamics model is used to analyze the total longitudinal force of the vehicle and explain the influence of torque vectoring distribution (TVD) on turning resistance. The Genetic Algorithm-Particle Swarm Optimization Hybrid Algorithm (GA-PSO) is used to optimize the torque distribution coefficient offline. Then, a torque optimization control strategy for obtaining minimum turning energy consumption online and a torque distribution coefficient (TDC) table in different cornering conditions are proposed, with the consideration of vehicle stability and possible maximum energy-saving contribution. Furthermore, given the operation points of the in-wheel motors, a more accurate TDC table is developed, which includes motor efficiency in the optimization process. Various simulation results showed that the proposed torque optimization control strategy can reduce the energy consumption in cornering by about 4% for constant motor efficiency ideally and 19% when considering the motor efficiency changes in reality.

Keywords: vehicle dynamics model; torque vectoring distribution; Genetic Algorithm-Particle Swarm Optimization Hybrid Algorithm; torque optimization control strategy; energy consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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