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Exploiting driving history for optimising the Energy Management in plug-in Hybrid Electric Vehicles

机译：利用驾驶历史，以优化插入式混合动力电动汽车中的能源管理

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This paper proposes an Energy Management Strategy (EMS) for a plug-in parallel Hybrid Electric Vehicle (pHEV) with the goal of minimising the fuel consumption while fulfilling the constraint on the terminal battery State-ofCharge (SoC). The proposed strategy assumes that the route was previously covered several times by the vehicle, in order to extract information about the feasible operating conditions in the driving cycle. Note that this situation is usual in commuting and daily trips. In this sense, the history of vehicle speeds and positions are used to build space-dependent transition probability matrices that are latter used for driving cycle estimation by means of Markov-Chain approach. Once the driving cycle is estimated, the torque-split problem in parallel hybrid powertrain is addressed using the Equivalent Consumption Minimisation Strategy (ECMS), where the associated boundary value problem of finding the weighting factor between battery and fuel cost that drives the SoC to the desired level at the end of the estimated cycle is solved and applied to the system. Finally, in order to make up for cycle estimation error, the ECMS is solved recurrently. For the sake of clarity, the proposed strategy is initially developed and analysed in a modelling environment. Then tests in an engine-in-the-loop basis are done for validation. In order to show the potential of proposed strategy, results are presented using a trade-off between the fuel consumption and the terminal-SoC for four different methods: the optimal power-split that requires a priori knowledge of the driving cycle for benchmarking, online ECMS with a fixed cycle estimation (average speed profile obtained from previous trips on the route), the proposed method, i.e. online ECMS with a dynamic cycle estimation and finally a rule-based charge depleting and charge sustaining strategy. The results demonstrate that the online ECMS outperforms the rest of online applicable methods.
机译：本文提出了一种能量管理策略（EMS）用于插入式并联混合动力电动车（PHEV），其目的是最小化燃料消耗，同时满足终端电池状态的限制（SOC）。该拟议的策略假设车辆以前通过车辆覆盖了几次，以便在驾驶循环中提取有关可行运行条件的信息。请注意，这种情况通常在通勤和日常旅行中。从这个意义上讲，车速和位置的历史用于构建空间相关的转换概率矩阵，该概率矩阵是通过马尔可夫链方法驱动循环估计的后者。一旦估计驾驶循环，使用等效的消耗最小化策略（ECM）解决了并联混合动力系中的扭矩分割问题，其中发现电池与驱动SOC的电池和燃料成本之间的加权因子的相关边值问题解决了估计周期末尾的所需水平并将其应用于系统。最后，为了弥补循环估计误差，ECM经常解决。为了清楚起见，拟议的策略最初在建模环境中开发和分析。然后在循环的基础上进行测试以进行验证。为了显示所提出的策略的潜力，使用燃料消耗与四种不同方法之间的替换之间的权衡来提出结果：最佳功率分配需要先验的驾驶循环用于基准，在线具有固定循环估计的ECM（从路线上的先前跳闸获得的平均速度分布），所提出的方法，即具有动态循环估计的在线ECM，最后是基于规则的电荷耗尽和充电持续策略。结果表明，在线ECMS优于在线适用方法的其余部分。

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