Achieving a satisfactory high-voltage battery lifetime while preserving fuel economy is a key challenge in the design of hybrid electric vehicles (HEVs). While several battery state-of-health (SOH) sensitive control approaches for HEVs have been presented in the literature, these approaches have not typically been experimentally validated. This paper thus aims at illustrating an optimal, multi-objective battery SOH sensitive off-line HEV control approach, which is based on dynamic programming (DP) and is experimentally validated in terms of prediction capability of the battery lifetime. An experimental campaign is conducted which ages cells with current profiles for three different predicted lifetime cases. The predictive accuracy of the battery ageing model is subsequently improved by including the effect of temperature and updating the empirical ageing characterization curve. The improved ageing model is then used to assess HEV performance in terms of fuel economy and battery lifetime for various high-voltage battery pack sizes and control goals. Results suggest that, thanks to the proposed multi-objective battery SOH sensitive control approach, the battery pack may be downsized by 35% with no impact on battery lifetime and a fuel consumption increase of just 1.1%. Engineers and designers could thus potentially adopt the proposed control approach to design HEVs which take tradeoffs between fuel economy and battery lifetime into consideration. Considerable reductions in battery pack cost, weight and production related CO2 emissions could be achieved in this way.

在设计混合动力汽车（HEV）时，在保持燃油经济性的同时实现令人满意的高压电池寿命是一项关键挑战。尽管文献中已经提出了几种针对HEV的电池健康状态（SOH）敏感控制方法，但这些方法通常未经实验验证。因此，本文旨在说明一种最佳的，多目标电池SOH敏感的离线HEV控制方法，该方法基于动态编程（DP），并根据电池寿命的预测能力进行了实验验证。进行了一项实验性活动，该活动针对三种不同的预期寿命情况使用当前配置文件对细胞进行了老化。随后，通过包括温度的影响并更新经验老化特征曲线，可以改善电池老化模型的预测准确性。然后，将改进的老化模型用于评估各种高压电池组尺寸和控制目标的燃油经济性和电池寿命方面的混合动力汽车性能。结果表明，由于提出了多目标电池SOH敏感控制方法，电池组的尺寸可以缩小35％，而对电池寿命没有影响，而燃油消耗仅增加1.1％。因此，工程师和设计人员可以潜在地采用建议的控制方法来设计混合动力汽车，该混合动力汽车考虑了燃料经济性和电池寿命之间的折衷。大大降低了电池组成本，