Online mixed-integer optimal energy management strategy for connected hybrid electric vehicles
Online mixed-integer optimal energy management strategy for connected hybrid electric vehicles
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DOI：
10.1016/j.jclepro.2022.133908
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发表时间：
2022-09
影响因子：
11.1
通讯作者：
Liuquan Yang;Weida Wang;Chao Yang;Xuelong Du;Wei Zhang
中科院分区：
文献类型：
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作者：
Liuquan Yang;Weida Wang;Chao Yang;Xuelong Du;Wei Zhang
针对互联式混合动力汽车，提出了一种在线混合整数最优能量管理策略。首先，基于反向传播神经网络构建预测框架，旨在利用联网车辆技术预测未来信息。随后，针对预测时域内的混合整数规划问题，在预测框架下提出了一种新的优化算法。最后，在仿真和硬件在环系统环境下验证了所提出的策略。结果表明，在两种典型工况下，与基于规则的EMS和基于当量消耗最小化策略（ECMS）的EMS相比，该策略分别降低了25.34%和1.13%，与基于规则的EMS和基于ECMS的EMS相比，分别降低了25.79%和1.78%。在相同的典型条件下，该策略比基于粒子群优化的EMS算法减少了84%的计算时间。在实际工况下，与基于ECMS的EMS相比，所提出的策略可以降低9.2%的燃油消耗。该策略在硬件在环实验中取得了满意的结果。
In this paper, an online mixed-integer optimal energy management strategy is proposed for connected hybrid electric vehicles. Firstly, a predictive framework is constructed based on the backpropagation neural network, aiming to predict the future information utilizing the connected vehicle technology. Subsequently, for the mixed-integer programming problem in the predictive horizon, a novel optimal algorithm is proposed in the predictive framework. Finally, the proposed strategy is verified under both simulation and hardware-in-the-loop system environments. The results show that the proposed strategy reduces fuel consumption by 25.34% and 1.13% compared with the rule-based EMS and equivalent consumption minimization strategy (ECMS)-based EMS, and reduces fuel consumption by 25.79% and 1.78% compared with the rule-based EMS and ECMS-based EMS in two typical conditions. The proposed strategy can reduce 84% computation time than the particle swarm optimization-based EMS in the same typical condition. Using real-word conditions, the proposed strategy can reduce fuel consumption by 9.2% compared with ECMS-based EMS. The proposed strategy achieved satisfactory results in a hard-in-loop experiment.