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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Liqiang Jin & Duanyang Tian & Qixiang Zhang & Jingjian Wang, 2020. "Optimal Torque Distribution Control of Multi-Axle Electric Vehicles with In-wheel Motors Based on DDPG Algorithm," Energies, MDPI, vol. 13(6), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1331-:d:331983
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    References listed on IDEAS

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    1. Jinhyun Park & Houn Jeong & In Gyu Jang & Sung-Ho Hwang, 2015. "Torque Distribution Algorithm for an Independently Driven Electric Vehicle Using a Fuzzy Control Method," Energies, MDPI, vol. 8(8), pages 1-25, August.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

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    2. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.

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