电动汽车驱动系统效率优化控制策略研究

This project focuses on efficiency optimization control strategy of power-train for electric vehicle(EV) ,which based on the development of efficient control strategy, combined with the driving cycle and real-time optimization of driving ability for different driver behavior. The main works around the efficiency optimization control strategy of EV power-train are described in the following sections..1) To develop suitable control strategy, the operation characteristics of battery pack, driving motor and other key parts of power-train are tested and analyzed. The efficiency characteristics of electric driving system under driving and braking energy recovering process are tested and the influence factors and their influence law of the operating characteristics for EV are analyzed..2) The driving high-efficiency control strategy of EV’s power-train is developed based on the taguchi robust design method and the optimal efficiency path control. For recovering energy effectively, under the premise of driving comfort, the dynamic matrix control (DMC) algorithm is adopted to develop the energy feedback control strategy. An EV model is established using veDYNA. Analysis and optimization of control strategy are carried out using hardware in the loop simulation technology for EV..3) The driving cycle has specific characteristics between different driver behavior. The electric vehicle controller acquires vehicle real-time running data such as acceleration pedal signal, vehicle speed, motor current torque and so on. By statistical analysis for driving cycle, the area of the greatest operation time and characteristic values of driver behavior is obtained..4) The test results indicate that driver’s driving characteristics have great influence on EV energy consumption. Several key variables describing the characteristics of driver behavior are defined and an identification model of the drivers is established using the key variables by electric vehicle controller. A model predictive control strategy was developed to obtain active management of the driver behavior, in order to minimize energy consumption of EV.

本项目研究电动汽车驱动系统效率优化控制策略，在开发高效控制策略基础上，结合车辆行驶工况，根据司机驾驶行为特征进行控制策略适应性优化研究。包括：1）驱动系统运行特性研究，测试和分析驱动系统运行特性的影响因素和影响规律；2）高效控制策略研究，基于田口鲁棒优化方法和最优轨迹控制进行驱动控制策略研究，基于动态矩阵预测控制进行制动能量回馈策略研究，利用veDYNA构建电动车辆模型，通过硬件在环仿真，对整车控制策略进行分析和优化。3）车辆行驶工况在线统计分析研究，整车控制器对车辆运行的加速踏板和制动踏板行程、车速、电机当前转矩等信息进行实时采集，统计车辆驱动系统常用工作区间，得到反映特定司机驾驶车辆行驶的特征值和典型工况；4）司机驾驶习惯自适应性优化控制策略研究，整车控制器对司机驾驶特性进行识别，实现驾驶规划策略与每个具体的司机对象相适应，达到整车控制策略与驾驶员的最优匹配。

本项目以纯电动汽车为研究对象，对动力电池组充电特性、放电特性、效率特性进行了测试分析；对驱动电机系统转矩特性、效率特性和过载特性进行了测试分析；对由动力电池组系统和驱动电机系统组成的驱动系统（以下简称驱动系统）的驱动特性和能量回馈特性进行了测试分析。研究了电池组SOC、温度、放电电流、电机转速和转矩等因素对驱动效率的影响规律；测试和分析了电池组SOC、充电电流、电机转速和制动转矩等因素对能量回馈效率的影响规律。以交流异步电机为研究对象，测试分析了电机转速、电机驱动转矩、电机系统母线电压和高速弱磁范围内电机工作对驱动电机系统能耗的影响规律；以磷酸铁锂电池组为研究对象，测试分析了电池组容量、电池组SOC、电池组温度和电池组电流对电池组能耗的影响规律。基于Infineon XE164FN-40F80L微控制器，开发了电动汽车整车ECU硬件系统；基于田口鲁棒优化方法开发了驱动控制策略，基于动态矩阵预测控制（Dynamic Matrix Control，DMC）开发了制动能量回馈策略。整车控制器对车辆运行信息进行实时采集和分析，得到反映特定司机驾驶车辆行驶的特征值：加速踏板位置、加速踏板位置最大变化率、平均功率、稳定车速、最大加速度。整车控制器基于自组织特征映射(Self-Organizing Feature Map)人工神经网络，利用司机驾驶特征参数，对司机驾驶意图进行辨识，结合田口鲁棒算法，优化转矩输出值，达到整车控制策略与驾驶员的最优匹配。采用基于司机驾驶习惯自适应性优化的控制策略，可有效降低电动汽车能耗，改善不同司机驾驶车辆行驶时的差异，对提高电动汽车续驶里程具有重要意义。