Knowledge implementation and transfer with an adaptive learning network for real-time power management of the plug-in hybrid vehicle

Quan Zhou, Dezong Zhao, Bin Shuai, Yanfei Li, Huw Williams, Hongming Xu

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Abstract

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.
Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
Early online date14 Jul 2021
DOIs
Publication statusE-pub ahead of print - 14 Jul 2021

Keywords

  • Deep deterministic policy gradient (DDPG) network
  • fuzzy inference system
  • plug-in hybrid vehicle
  • power management
  • transfer learning.

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