Optimal Torque Distribution Control of Multi-Axle Electric Vehicles with In-wheel Motors Based on DDPG Algorithm

Liqiang Jin, Duanyang Tian, Qixiang Zhang and Jingjian Wang
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Liqiang Jin: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Duanyang Tian: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Qixiang Zhang: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Jingjian Wang: College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China

Energies, 2020, vol. 13, issue 6, 1-19

Abstract: In order to effectively reduce the energy consumption of the vehicle, an optimal torque distribution control for multi-axle electric vehicles (EVs) with in-wheel motors is proposed. By analyzing the steering dynamics, the formulas of additional steering resistance are given. Aiming at the multidimensional continuous system that cannot be solved by traditional optimization methods, the deep deterministic policy gradient (DDPG) algorithm for deep reinforcement learning is adopted. Each wheel speed and deflection angle are selected as the state, the distribution ratio of drive torque is the optimized action and the state of charge ( SOC ) is the reward. After completing a large number of training for vehicle model, the algorithm is verified under conventional steering and extreme steering conditions. The maximum SOC decline of the vehicle can be reduced by about 5% under conventional steering conditions based on the motor efficiency mapused. The combination of artificial intelligence technology and actual situation provides an innovative solution to the optimization problem of the multidimensional state input and the continuous action output related to vehicles or similar complex systems.

Keywords: electric vehicles (EVs); independent-drive technology; deep reinforcement learning (DRL); optimal torque distribution (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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