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.

中文翻译：

---

利用自适应学习网络实现插电式混合动力汽车实时电源管理的知识实施和转移


未来车辆中的电源管理等基本决策任务将受益于人工智能技术的发展，以实现安全和节能的操作。为了开发在插电式混合动力汽车的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）网络与自适应神经网络相结合。模糊推理系统（ANFIS）网络。首先，使用新的全局K重模糊学习（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网络来用真实世界的强化信号调节ANFIS网络的输入。 AFIS 和 DDPG 网络相集成，可最大限度地提高控制效用 (CU)，这是车辆能效和电池充电状态的函数。进行实验研究来验证 DDPG-ANFIS 网络的性能和鲁棒性。结果表明，与在研究车辆上使用 MATLAB ANFIS 工具箱相比，使用 DDPG-ANFIS 网络的研究车辆的 CU 提高了 8%。在五种模拟的真实驾驶条件下，DDPG-ANFIS 网络的最大平均 CU 值比仅使用 AFIS 的网络提高了 138%，比仅使用 DDPG 的网络提高了 5%。