Abstract Although hybrid electric buses (HEB) has fixed route condition, there are differences upon daily driving conditions affected by traffic, weather and so on. Thus, the rule-based strategy is difficult to get the best energy-saving result under this circumstance. To improve strategy adaptability and optimality, the paper presents a hierarchical optimization control strategy based on driving information. Driving information is first deeply explored and utilized from the history and future two dimensions, including typical cycle construction and future driving prediction. The upper control strategy adopts global optimization to plan SOC trajectory by using typical cycle construction from the overall perspective, determining the proportions of electric and hybrid modes, and realizing the rational use of electric energy. Low-level control realizes real-time optimal torque distribution based on the prediction of driving condition, which adapts to different driving conditions from the local real-time perspective. Finally, simulation and hardware-in-loop tests are performed under an actual bus route. In contrast to rule-based strategy, the hierarchical optimal intelligent strategy nearly achieves the global optimization results with 9.02% fuel efficiency. Therefore, the proposed optimization strategy improves the driving condition adaptability and fuel economy of fixed-route HEBs from global and local dimensions.

中文翻译：

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基于驾驶信息的动力分流混合动力客车分层优化智能能量管理策略

摘要 混合动力公交车（HEB）虽然具有固定的路线条件，但受交通、天气等因素影响，日常驾驶条件存在差异。因此，在这种情况下，基于规则的策略很难获得最佳的节能效果。为了提高策略的适应性和最优性，本文提出了一种基于驾驶信息的分层优化控制策略。首先从历史和未来两个维度对驾驶信息进行深度探索和利用，包括典型循环构建和未来驾驶预测。上位控制策略采用全局优化，通过典型循环构建从全局角度规划SOC轨迹，确定纯电动和混合动力模式的比例，实现电能的合理利用。低级控制基于对行驶工况的预测，实现实时优化扭矩分配，从局部实时角度适应不同的行驶工况。最后，在实际公交线路下进行仿真和硬件在环测试。与基于规则的策略相比，分层优化智能策略几乎达到了全局优化结果，燃油效率为 9.02%。因此，所提出的优化策略从全局和局部维度提高了固定路线 HEB 的驾驶条件适应性和燃油经济性。与基于规则的策略相比，分层优化智能策略几乎达到了全局优化结果，燃油效率为 9.02%。因此，所提出的优化策略从全局和局部维度提高了固定路线 HEB 的驾驶条件适应性和燃油经济性。与基于规则的策略相比，分层优化智能策略几乎达到了全局优化结果，燃油效率为 9.02%。因此，所提出的优化策略从全局和局部维度提高了固定路线 HEB 的驾驶条件适应性和燃油经济性。