基于行驶工况合成的混合动力汽车电池寿命优化

Battery life optimization of hybrid electric vehicle based on driving cycle construction

针对复杂行驶工况易导致混合动力汽车动力电池使用条件恶化、增加车辆全寿命周期成本的问题,以一款行星混联式混合动力汽车为研究对象,从能量管理优化角度主动延长电池寿命。基于历史数据构建了实车代表性工况,采用动态规划算法求解以整车综合燃油消耗和电池寿命衰减最小的多目标优化问题,保证系统性能全局最优。由于动态规划算法具有工况局限性且运算量大,因而基于全局优化结果训练神经网络控制器来实现能量管理控制。仿真结果表明:与以油耗为单目标优化相比,多目标优化可使电池寿命衰减减少43.28%,油耗仅增加1.22%,在减缓电池寿命衰减的同时兼顾了燃油经济性,基于神经网络的控制策略可实现主动适应驾驶员日常行驶工况的优化控制,具有良好的应用前景。

To solve the problems that the use conditions of hybrid electric vehicle power batteries deteriorate and the full-life-cycle cost of vehicle increases due to complex driving cycle,a planetary hybrid electric vehicle was taken as the research object to proactively extend the battery life from the perspective of energy management optimization.Based on the historical driving data,a representative real vehicle driving cycle was built.The dynamic programming algorithm was used to solve the multi-objective optimization problem with the minimum overall fuel consumption and battery life attenuation to ensure the global optimal system performance.Due to the problem that dynamic programming algorithm has limitation of driving cycle and demands large amount of calculation,the neural network controller was trained for realizing energy management control based on the global optimization results.Simulation results show that compared with the optimization with a single goal of fuel consumption,multi-objective optimization can reduce battery life attenuation by 43.28%and increase fuel consumption by only 1.22%,which slows battery life attenuation while taking into account fuel economy.The control strategy based on neural network achieves optimized control that actively adapts to the daily driving cycle of the driver,which has a good application prospect.