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Optimal braking control for an independent four wheel-motor-driven electric vehicle

机译：独立的四轮电动汽车的最佳制动控制

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The lightweight, integrated, and high performance motor-wheel driven electric vehicle is clean, energy saving, and safe, and has the potential to form the ideal electric vehicle for the future. This paper proposes an optimal fuzzy neural network braking control strategy to determine the allocation of the front and rear regenerative braking torque and friction braking torque for the independent four Wheel-Motor-Driven (4WMD) electric vehicle. The proposed fuzzy neural network controller applies a five-layered neural network to map relations between the inputs (required total braking torque and battery State Of Charge (SOC)) and the outputs (front and rear in-wheel-motor regenerative braking torques), and uses a genetic algorithm to optimize offline the weights and thresholds of this fuzzy neural network to determine the dynamic allocation of the front and rear in-wheel-motor regenerative braking torque and friction braking torque. The objective is to maximize the battery SOC at the end of braking, and the constraints are the required braking speed of the vehicle, the limited regenerative braking torque of an in-wheel-motor, and the allowable SOC range of the battery. A lightweight independent 4WMD electric vehicle model (including vehicle longitudinal dynamic model, tyres, in-wheel motors, batteries and disc brakes) and this fuzzy neural network braking control model are built in the Matlab/Simulink environment, and are simulated and validated under different braking scenarios. The simulation results illustrate that this optimal braking control is better than the simple rule-based braking control, recovering more regenerative braking energy while meeting the vehicle braking performance requirements.
机译：轻便，集成，高性能的电动轮驱动的电动汽车清洁，节能，安全，并且有可能成为未来理想的电动汽车。本文提出了一种最优的模糊神经网络制动控制策略，来确定独立的四轮驱动（4WMD）电动汽车的前后再生制动扭矩和摩擦制动扭矩的分配。所提出的模糊神经网络控制器应用五层神经网络来映射输入（所需总制动扭矩和电池充电状态（SOC））与输出（前后轮内电动机再生制动扭矩）之间的关系，并使用遗传算法离线优化此模糊神经网络的权重和阈值，以确定前后轮内电机再生制动扭矩和摩擦制动扭矩的动态分配。目的是在制动结束时使电池SOC最大化，其约束条件是车辆所需的制动速度，轮毂电机的有限再生制动扭矩以及电池的允许SOC范围。在Matlab / Simulink环境中构建了轻量级的独立4WMD电动汽车模型（包括汽车纵向动态模型，轮胎，轮内电动机，电池和盘式制动器）以及此模糊神经网络制动控制模型，并在不同条件下进行了仿真和验证制动情况。仿真结果表明，该最佳制动控制优于简单的基于规则的制动控制，可在满足车辆制动性能要求的同时回收更多的再生制动能量。

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