插电式混合动力汽车（PHEV）是我国新能源汽车重点普及车型之一，开展计及动力电池老化抑制的能量管理策略研究是当前提升PHEV节能与安全智能化控制水平的关键，本项目针对PHEV能量管理：(1)探究动力电池在车用复杂工况中性能衰退规律及其影响因素，构建融合数据驱动和衰退经验模型双驱动的电池寿命预测方法，实现电池寿命衰减的在线量化；(2)揭示PHEV能耗与其行驶工况的耦合规律，应用机器学习算法开展工况预测方法研究，构建能够充分映射工况特征的张量模型及缺失信息补全算法，降低随机工况对能量管理效果的影响；(3)深入挖掘PHEV节能潜力，引入帕累托算法分析燃油消耗与电池损耗的竞争关系并确定其优化权重，在模型预测控制的架构下进行多目标滚动寻优，实现兼顾燃油经济性及电池老化抑制的能量管理方法。研究成果将为PHEV预测能量管理奠定新的理论基础，对提升PHEV节能与安全水平、实现其智能化控制具有重要科学意义。

Plug-in hybrid vehicle （PHEV）as a kind of the new-energy vehicle is one of the key strategic technology supported by Chinese government. To inhibit battery aging process and simultaneously, improve fuel efficiency is the leading edge research in the area of PHEV energy management. To achieve such energy management system (EMS) this proposal makes a very feasible plan as follows. Firstly, The decay-progress of battery and its recursively quantified expressions will be explored to establish an online battery lifetime degradation model with the active hybridization of the data-driven method within the empirical decay-model. Secondly, to achieve an accurate prediction of global driving cycle with the consideration of the variable driving environment, a tensor model is proposed in this study. Both the structural decomposition method and missing-information recovery algorithm are studied to enhance the effectiveness for the tensor model. Thirdly, the predictive energy management strategy for the PHEV is developed based on the model predict control method which introduces the Pareto- Front model to analysis the trade-off between the battery lifetime extending and fuel consumption saving. This study will provide a new theoretical foundation for the predictive energy management of PHEV and further explore the intelligence, controllability and energy saving of the PHEV system, hence an grantable proposal.