插电式四驱混合动力汽车控制策略设计及优化

为有效识别驾驶员的驾驶意图,在保障插电式四驱混合动力汽车动力性的基础上,提高其燃油经济性,提出了一种转矩识别系数计算方法,设计了基于发动机输出转矩最优能量管理控制策略,讨论了每种工作模式的判别条件以及转矩分配方法.为避免单一优化算法运算时间长、容易陷入局部最优的固有缺陷,使用优拉丁超立方的方法进行试验设计,利用径向基函数神经网络(radial basis function, RBF)建立近似模型,使用多岛遗传算法对近似模型进行了优化.研究结果表明:对优化后的控制策略进行离线仿真得出,混合动力汽车在满足动力性能的前提下,百公里油耗降低了16.4%;将优化后的控制策略在dSPACE上进行硬件在环试验表明,所制定的控制策略,可以实现基本的能量管理,且加入转矩识别之后平均车速误差降低了39.9%,百公里油耗降低了8.5%. 

Design and Optimization of Control Strategy for Plug-in 4WD Hybrid Electric Vehicles

In order to identify drivers' intentions, and improve the fuel economy of plug-in 4WD hybrid electric vehicles (HEVs) with their power performances guaranteed, a method for calculating the torque identification coefficient was put forward. An energy management control strategy was designed based on the engine optimal control, and the judging condition of every working mode as well as its torque distribution method was introduced. In order to avoid the inherent defects of a single optimization algorithm that the calculation time was long and it was easy to end up with a local optimal solution, design of experiment (DOE) was conducted by the method of optimal Latin-hypercube design. An approximate model was designed using the RBF (radial basis function) neural network and then optimized using the multi-island genetic algorithm. In addition, an off-line simulation was conducted to verify the optimized control strategy. The results showed that the optimized control strategy could reduce the fuel consumption per 100 km of the plug-in hybrid electric vehicle by 16.4%, without degrading the power performances. After optimization, the control strategy is validated by a hardware-in-the-loop test on dSPACE and the experimental results show that the control strategy can realize the basic energy management. What's more, with torque identification, the average velocity error is reduced by 39.9% and the fuel consumption is reduced by 8.5%.