Predictive Energy Management Strategy for Hybrid Electric Air-Ground Vehicle Considering Battery Thermal Dynamics
Predictive Energy Management Strategy for Hybrid Electric Air-Ground Vehicle Considering Battery Thermal Dynamics
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DOI：
10.3390/app13053032
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Zhe Li;X. Jiao;Mingjun Zha;Chao Yang;Liuquan Yang
中科院分区：
文献类型：
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作者：
Zhe Li;X. Jiao;Mingjun Zha;Chao Yang;Liuquan Yang
混合动力空地车辆（HEAGV），可以在陆地上运行，并在空中飞行，被认为是一种很有前途的未来交通工具。HEAGV的运行伴随着高能耗，可能导致电池温度升高，这可能会影响电池的寿命。为了延长电池的使用寿命并提高能量利用效率，一个有效的能量管理策略（EMS）是HEAGV运行的必要条件。在这方面，本文提出了一种基于模型预测控制（MPC）的预测EMS。首先利用智能网络技术获取车速信息，实现对电力需求的预测，然后规划出荷电状态（SOC）参考轨迹。其次，提出了基于庞特里亚金最小原理的模型预测控制（PMP-MPC）框架，其中包括电池热动力学。在该框架下，通过降低电池的温度来提高燃料效率。最后，所提出的方法进行了比较PMP，动态规划（DP），基于规则（RB）的方法。分析了不同预瞄视野大小对燃油经济性和电池温度的影响。两个工况下的验证结果表明，与基于规则的方法相比，该方法的燃油经济性分别提高了5.14%和5.2%，温度分别降低了5.9%和4.9%.仿真结果表明，所提出的PMP-MPC方法可以有效地提高燃油经济性和降低温度。
Hybrid electric air-ground vehicles (HEAGVs), which can run on the land and fly in the air, are considered a promising future transportation. The operation of HEAGVs, accompanied by high energy consumption, could lead to increasing battery temperature, which may affect the lifespan of the battery. To make the battery last longer and improve energy efficiency, an effective energy management strategy (EMS) is necessary for the operation of HEAGVs. In this regard, this paper proposes a predictive EMS based on model predictive control (MPC). Firstly, speed information is obtained by intelligent network technology to achieve a prediction of power demand, and then the state of charge (SOC) reference trajectory is planned. Secondly, a Pontryagin’s minimum principle-based model predictive control (PMP-MPC) framework is proposed, including battery thermal dynamics. Under the framework, fuel efficiency is improved by reducing the temperature of the battery. Finally, the proposed method is compared to PMP, dynamic programming (DP), and rule-based (RB) methods. The effect of different preview horizon sizes on fuel economy and battery temperature is analyzed. Verification results under two driving cycles indicate that compared with the rule-based method, the proposed method improves fuel economy by 5.14% and 5.2% and decreases the temperature by 5.9% and 4.9%, respectively. The results demonstrate that the proposed PMP-MPC method can effectively improve fuel economy and reduce temperature.