Energy management plays an important role in improving the fuel economy of plug-in hybrid electric vehicles (PHEV). Therefore, this paper proposes an improved adaptive equivalent consumption minimization strategy (A-ECMS) based on long-term target driving cycle recognition and short-term vehicle speed prediction, and adapt it to personalized travel characteristics. Two main contributions have been made to distinguish our work from exiting research. Firstly, online long-term driving cycle recognition and short-term speed prediction are considered simultaneously to adjust the equivalent factor (EF). Secondly, the dynamic programming (DP) algorithm is applied to the offline energy optimization process of A-ECMS based on typical driving cycles constructed according to personalized travel characteristics. The improved A-ECMS can optimize EF based on mileage, SOC, long-term driving cycle and real-time vehicle speed. In the offline part, typical driving cycles of a specific driver is constructed by analyzing personalized travel characteristics in the historical driving data, and optimal SOC consumption under each typical driving cycle is optimized by DP. In the online part, the SOC reference trajectory is obtained by recognizing the target driving cycle from Intelligent Traffic System, and short-term vehicle speed is predicted by Nonlinear Auto-Regressive (NAR) neural network which both adjust EF together. Simulation results show that compared with CD-CS, the fuel consumption of A-ECMS proposed in the paper is reduced by 8.7%.

中文翻译：

---

基于实时驾驶周期预测和个性化出行特征的PHEV的A-EMCS

能源管理在提高插电式混合动力汽车（PHEV）的燃油经济性方面起着重要作用。因此，本文提出了一种基于长期目标驾驶周期识别和短期车速预测的改进的自适应等效消耗最小化策略（A-ECMS），并将其适应于个性化的出行特征。为区分我们的工作与现有研究做出了两个主要贡献。首先，同时考虑在线长期驾驶周期识别和短期速度预测以调整等效因子（EF）。其次，基于个性化出行特征构造的典型行驶周期，将动态规划算法应用于A-ECMS的离线能量优化过程。改进的A-ECMS可以基于里程，SOC，长期驾驶周期和实时车速优化EF。在离线部分中，通过分析历史驾驶数据中的个性化行驶特性来构造特定驾驶员的典型驾驶周期，并且通过DP优化每个典型驾驶周期下的最佳SOC消耗。在在线部分中，通过从智能交通系统识别目标驾驶周期来获得SOC参考轨迹，并通过同时调整EF的非线性自回归（NAR）神经网络预测短期车速。仿真结果表明，与CD-CS相比，本文提出的A-ECMS的燃油消耗降低了8.7％。通过分析历史驾驶数据中的个性化行驶特性来构造特定驾驶员的典型驾驶循环，并通过DP优化每个典型驾驶循环下的最佳SOC消耗。在在线部分中，通过从智能交通系统识别目标驾驶周期来获得SOC参考轨迹，并通过同时调整EF的非线性自回归（NAR）神经网络预测短期车速。仿真结果表明，与CD-CS相比，本文提出的A-ECMS的燃油消耗降低了8.7％。通过分析历史驾驶数据中的个性化行驶特性来构造特定驾驶员的典型驾驶循环，并通过DP优化每个典型驾驶循环下的最佳SOC消耗。在在线部分中，通过从智能交通系统识别目标驾驶周期来获得SOC参考轨迹，并通过同时调整EF的非线性自回归（NAR）神经网络预测短期车速。仿真结果表明，与CD-CS相比，本文提出的A-ECMS的燃油消耗降低了8.7％。SOC参考轨迹是通过从智能交通系统识别目标驾驶周期而获得的，而短期车速是通过同时调整EF的非线性自回归（NAR）神经网络预测的。仿真结果表明，与CD-CS相比，本文提出的A-ECMS的燃油消耗降低了8.7％。SOC参考轨迹是通过从智能交通系统识别目标驾驶周期而获得的，而短期车速是通过同时调整EF的非线性自回归（NAR）神经网络预测的。仿真结果表明，与CD-CS相比，本文提出的A-ECMS的燃油消耗降低了8.7％。