Zhou Quan, Zhao Dezong, Shuai Bin, Li Yanfei, Williams Huw, Xu Hongming
IEEE Trans Neural Netw Learn Syst. 2021 Dec;32(12):5298-5308. doi: 10.1109/TNNLS.2021.3093429. Epub 2021 Nov 30.
Essential decision-making tasks such as power management in future vehicles will benefit from the development of artificial intelligence technology for safe and energy-efficient operations. To develop the technique of using neural network and deep learning in energy management of the plug-in hybrid vehicle and evaluate its advantage, this article proposes a new adaptive learning network that incorporates a deep deterministic policy gradient (DDPG) network with an adaptive neuro-fuzzy inference system (ANFIS) network. First, the ANFIS network is built using a new global K-fold fuzzy learning (GKFL) method for real-time implementation of the offline dynamic programming result. Then, the DDPG network is developed to regulate the input of the ANFIS network with the real-world reinforcement signal. The ANFIS and DDPG networks are integrated to maximize the control utility (CU), which is a function of the vehicle's energy efficiency and the battery state-of-charge. Experimental studies are conducted to testify the performance and robustness of the DDPG-ANFIS network. It has shown that the studied vehicle with the DDPG-ANFIS network achieves 8% higher CU than using the MATLAB ANFIS toolbox on the studied vehicle. In five simulated real-world driving conditions, the DDPG-ANFIS network increased the maximum mean CU value by 138% over the ANFIS-only network and 5% over the DDPG-only network.
未来车辆等关键决策任务，如动力管理，将受益于人工智能技术的发展，以实现安全且节能的运行。为了开发在插电式混合动力汽车能量管理中使用神经网络和深度学习的技术并评估其优势，本文提出了一种新的自适应学习网络，该网络将深度确定性策略梯度（DDPG）网络与自适应神经模糊推理系统（ANFIS）网络相结合。首先，使用一种新的全局K折模糊学习（GKFL）方法构建ANFIS网络，以实时实现离线动态规划结果。然后，开发DDPG网络，用实际强化信号调节ANFIS网络的输入。将ANFIS和DDPG网络集成，以最大化控制效用（CU），控制效用是车辆能量效率和电池荷电状态的函数。进行了实验研究，以验证DDPG-ANFIS网络的性能和鲁棒性。结果表明，配备DDPG-ANFIS网络的研究车辆的控制效用比使用MATLAB ANFIS工具箱的研究车辆高出8%。在五种模拟的实际驾驶条件下，DDPG-ANFIS网络的最大平均控制效用值比仅使用ANFIS网络时提高了138%，比仅使用DDPG网络时提高了5%。
