Pareto-based optimal control strategy for battery capacity decline

As environmental problems become increasingly severe, achieving qualitative breakthroughs in the energy consumption and emissions of traditional internal combustion engine vehicles is difficult. In contrast, new energy vehicles are environmentally friendly and have low fuel consumption, which is important for the future development of vehicles. A plug-in hybrid electric vehicle (PHEV) is widely regarded as the most promising alternative solution for improving energy efficiency and reducing emissions. The optimization of the energy management strategy (EMS) mainly focuses on reducing fuel consumption and improving the economy. However, the durability of the power battery also needs attention, as the lack of life remains a major obstacle to the large-scale commercialization of PHEVs. Because PHEV batteries can obtain relatively cheap power through the grid, the traditional control strategy only considers the full use of the battery power but ignores its excessive use, which will accelerate the decline of the power battery capacity. Therefore, determining how to make full use of the battery power and control the decline of the battery capacity is a new research focus. Based on the semiempirical decay model of the battery, the energy management strategy of balancing the degradation of the battery capacity was established by introducing the battery utilization degree factor. The multiobjective optimization problem was transformed into a single-objective problem by selecting the appropriate weight factor through the Pareto noninferior target domain. A dynamic programming algorithm was used to obtain the global optimal solution of the weight coefficient. The optimal weight coefficient was selected by weighing the fuel consumption and battery capacity decline degree under different weights. In the case of equivalent fuel consumption, the decay rate of battery life can be effectively inhibited when the weight coefficient is 0.9. Finally, the validity of the proposed solution is verified by comparing the online equivalent consumption minimization strategy (ECMS) simulation with the dynamic programming solution under the same weight.