Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning

Weihan Li, Han Cui, Thomas Nemeth, Jonathan Jansen, Cem Ünlübayir, Zhongbao Wei, Xuning Feng, Xuebing Han, Minggao Ouyang, Haifeng Dai, Xuezhe Wei and Dirk Uwe Sauer

Applied Energy, 2021, vol. 293, issue C, No S0306261921004499

Abstract: In order to fulfill the energy and power demand of battery electric vehicles, a hybrid battery system with a high-energy and a high-power battery pack can be implemented as the energy source. This paper explores a cloud-based multi-objective energy management strategy for the hybrid architecture with a deep deterministic policy gradient, which increases the electrical and thermal safety, and meanwhile minimizes the system’s energy loss and aging cost. In order to simulate the electro-thermal dynamics and aging behaviors of the batteries, models are built for both high-energy and high-power cells based on the characterization and aging tests. A cloud-based training approach is proposed for energy management with real-world vehicle data collected from various road conditions. Results show the improvement of electrical and thermal safety, as well as the reduction of energy loss and aging cost of the whole system with the proposed strategy based on the collected real-world driving data. Furthermore, processor-in-the-loop tests verify that the proposed strategy can achieve a much higher convergence rate and a better performance in terms of the minimization of both energy loss and aging cost compared with state-of-the-art learning-based strategies.

Keywords: Energy management; Vehicle-to-cloud; Reinforcement learning; Battery aging; Lithium-ion; Battery safety (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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DOI: 10.1016/j.apenergy.2021.116977

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