A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles
A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles
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DOI：
10.1016/j.energy.2021.122727
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发表时间：
2022-02-03
期刊：
影响因子：
9
通讯作者：
Li, Guang
中科院分区：
文献类型：
--
作者：
Chen, Zhihang;Liu, Yonggang;Li, Guang
对于插电式混合动力电动汽车，等效消耗最小化策略通常被视为电池荷电状态参考跟踪方法。因此，相应的控制性能强烈地依赖于荷电状态参考生成的质量。提出了一种基于双神经网络的智能当量消耗最小化策略和一种新的当量因子修正方法，该方法能够在不需要参考荷电状态的情况下自适应地调整当量因子，以达到接近最优的燃油经济性。构建贝叶斯正则化神经网络在线预测近优当量因子，设计反向传播神经网络预测发动机开/关，提高当量因子预测质量。相应的神经网络训练利用动态规划的全局最优性。此外，新的等效因子校正可以保证电能沿着行程逐渐消耗，并且终端电池荷电状态满足预定约束。一系列的虚拟仿真下，共9个驱动循环表明，所提出的方法可以提供一个有竞争力的燃油经济性相比，从动态规划的最优解，以及调节电池的充电状态，以达到所需的终端值在行程结束时。(c)2021爱思唯尔有限公司保留所有权利。
For plug-in hybrid electric vehicles, the equivalent consumption minimum strategy is typically regarded as a battery state of charge reference tracking method. Thus, the corresponding control performance is strongly dependent on the quality of state of charge reference generation. This paper proposes an intelligent equivalent consumption minimum strategy based on dual neural networks and a novel equivalent factor correction, which can adaptively regulate the equivalent factor to achieve the nearoptimal fuel economy without the support of the state of charge reference. The Bayesian regularization neural network is constructed to predict the near-optimal equivalent factor online, while the backpropagation neural network is designed to forecast the engine on/off with the aim of improving the quality of equivalent factor prediction. The corresponding neural network training takes advantage of the global optimality of dynamic programming. Besides, the novel equivalent factor correction can guarantee that the electrical energy is gradually consumed along the trip and the terminal battery state of charge satisfies the preset constraints. A series of virtual simulations under a total of nine driving cycles demonstrates that the proposed method can deliver a competitive fuel economy comparing to the optimal solution derived from the dynamic programming, as well as regulating the battery state of charge to reach the desired terminal value at the end of the trip. (c) 2021 Elsevier Ltd. All rights reserved.