能源管理
计算机科学
电池(电)
汽车工程
电动汽车
控制工程
功率(物理)
可靠性工程
能量(信号处理)
工程类
数学
量子力学
统计
物理
作者
Jingda Wu,Zhongbao Wei,Weihan Li,Yu Wang,Yunwei Li,Dirk Uwe Sauer
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2020-08-06
卷期号:17 (6): 3751-3761
被引量:223
标识
DOI:10.1109/tii.2020.3014599
摘要
Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.
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