This paper presents the synthesis of an induction motor neural controller and a regenerative braking controller for an electric vehicle architecture, based on two energy system, a Main Energy System (MES) and an Auxiliary Energy System (AES). Such controllers are based on system identification, trajectory tracking and state estimation. System identification uses a Recurrent High Order Neural Network (RHONN), trained with an Extended Kalman Filter (EKF). RHONN obtains an accurate motor model which is robust in presence of external disturbances and parameter variations. To force the motor to track a desired torque trajectory and to reject undesired disturbances, an inverse optimal controller based on the identified neural model is proposed. Therefore, the proposed scheme does not need a-priori knowledge of motor parameters. For state estimation a super-twisting observer is implemented to estimate the rotor magnetic fluxes. The regenerative braking controller addresses the issue when the battery is not capable to accept the generated energy due to braking; Therefore, an AES based on a super capacitor and a buck–boost converter is a solution to recover the braking energy and give power to the motor during acceleration. The regenerative braking controller is based on a PI control to regulate voltage and current of the super capacitor. Simulation and experimental results illustrate the performance of the proposed controllers which are implemented using a rapid control prototyping platform integrated by a dSPACE board. Experimental tests are carried out for a topology with and without AES to illustrate the improvement of the proposed EV architecture.

本文介绍了基于两个能源系统、主能源系统 (MES) 和辅助能源系统 (AES) 的电动汽车架构的感应电机神经控制器和再生制动控制器的综合。这种控制器基于系统识别、轨迹跟踪和状态估计。系统识别使用循环高阶神经网络 (RHONN)，并使用扩展卡尔曼滤波器 (EKF) 进行训练。RHONN 获得准确的电机模型，该模型在存在外部干扰和参数变化的情况下是稳健的。为了迫使电机跟踪所需的扭矩轨迹并拒绝不需要的干扰，提出了一种基于识别出的神经模型的逆优化控制器。因此，所提出的方案不需要电机参数的先验知识。对于状态估计，使用超扭曲观测器来估计转子磁通量。再生制动控制器解决了电池无法接受制动产生的能量时的问题；因此，基于超级电容器和降压-升压转换器的 AES 是一种在加速过程中回收制动能量并为电机供电的解决方案。再生制动控制器基于 PI 控制来调节超级电容器的电压和电流。仿真和实验结果说明了所提出的控制器的性能，这些控制器是使用由 dSPACE 板集成的快速控制原型平台实现的。对有和没有 AES 的拓扑进行了实验测试，以说明所提出的 EV 架构的改进。