考虑电池寿命的插电式混合动力汽车能量管理优化

考虑电池寿命对插电式混合动力汽车全寿命周期成本的影响，以综合燃油消耗和电池寿命衰减最小为目标开展电池充放电功率的多目标优化研究. 引入权重系数将多目标优化问题转化为单目标优化问题，采用动态规划（DP）算法求解实现全局最优，并根据优化结果选择最优权重系数. 为了解决动态规划算法运算速度慢、须预知工况的缺陷，以最优权重系数的优化结果训练神经网络控制器并将其应用于控制策略中. 仿真结果表明，与以油耗为单一目标的优化相比，多目标优化可使电池寿命衰减减少13.5%，而燃油消耗仅增加0.5%，在保证燃油经济性的同时有效减少电池寿命的衰减程度；基于神经网络的控制策略有效克服了动态规划算法的缺点并能达到与其相近的运算效果，具有较好的应用前景.

Battery-health conscious energy management optimization in plug-in hybrid electric vehicles

Considering the effect of battery life on the full-life-cycle cost of plug-in hybrid electric vehicle, a multi-objective optimization method of managing the input/output battery power was proposed, with the minimization of vehicle fuel consumption and battery’s health degradation as the optimization objective. The weight coefficient was introduced to transform the multi-objective optimization problem into a single targeted problem, then the dynamic programming (DP) algorithm was employed to achieve global optimization and the optimal weight coefficient was selected according to optimization results. A neural network controller based on optimization results of the optimal weight coefficient was trained and applied to control strategy, in order to solve the problem that the DP algorithm is slow in computation speed and needs working conditions in advance. Simulation results demonstrate that compared with the single-objective optimization of fuel consumption, the multi-objective optimization method can reduce the degradation rate of battery life by 13.5% while increasing the fuel consumption by only 0.5%. The proposed method lessens the degradation rate of battery life effectively with little increase in fuel consumption. The neural network-based control strategy can overcome the shortcomings of the DP algorithm and achieve the similar optimization effect with the DP algorithm, thus it has a good application prospects.