Transportation sector is one of the major sources of pollutants as it contributes more than 80% of CO, while almost all HC and 90% of NOx and PM. Although battery electric vehicles are well-known for reducing environmental pollution, the driving range and battery lifespan put a significant barrier to its large-scale commercialization. In this study, an integrated system, which includes an uninterrupted dual input transmission and hybrid energy storage system, is proposed to improve energy efficiency and extend battery lifespan. Given the limitations of dynamic programming in practice, a real-time optimal control strategy is designed to evaluate the power loss and battery capacity degradation of the proposed integrated system based on detailed mathematical models of individual powertrain components. To achieve a desirable trade-off between battery degradation, energy consumption, and acquisition cost, a mixed-integer multi-objective genetic algorithm is implemented to optimize the parameters of the hybrid energy storage system, while Pareto principal is adopted to find the proper solution according to different purposes. The simulation results reveal that the proposed integrated system shows the potential of saving 15.85%–20.82% of the energy consumption in typical driving cycles and more than 22.61%–31.11% Life-cycle cost compared with the single-ratio transmission-based battery electric vehicles. The selected Pareto front can further enhance Life-cycle cost from 26.53% to 28.13% in the HWFET cycle. It can be concluded that the integrated uninterrupted dual input transmission and hybrid energy storage system not only can improve motor efficiency and reduce energy consumption, it also can extend the battery lifespan to decrease Life-cycle cost compared to conventional single-ratio battery-only EV.

运输行业是主要的污染物来源之一，因为它贡献了超过80％的CO，而几乎所有的HC以及90％的NOx和PM。尽管电池电动车以减少环境污染而闻名，但续驶里程和电池寿命为其大规模商业化带来了重大障碍。在这项研究中，提出了一个包括不间断双输入传输和混合能量存储系统的集成系统，以提高能源效率并延长电池寿命。鉴于实际中动态编程的局限性，基于单个动力总成组件的详细数学模型，设计了实时最优控制策略来评估所提出的集成系统的功率损耗和电池容量降低。为了在电池性能，能耗和购置成本之间取得理想的折衷，实施了一种混合整数多目标遗传算法来优化混合储能系统的参数，同时采用帕累托原理来寻找合适的解决方案根据不同的目的。仿真结果表明，与基于单比例变速箱的电池相比，在典型的驾驶循环中，所提出的集成系统具有节省15.85％–20.82％的能耗的潜力，并且可以节省超过22.61％–31.11％的生命周期成本。汽车。选择的Pareto前沿可以将HWFET周期中的生命周期成本从26.53％进一步提高到28.13％。