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

Research output: Contribution to journalArticlepeer-review

Authors

  • Dezong Zhao
  • Yanfei Li
  • Huw Williams

External organisations

  • Loughborough University
  • Tsinghua University

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.

Details

Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
Early online date14 Jul 2021
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.