汽车工业
电池(电)
系统工程
计算机科学
工程类
控制器(灌溉)
风险分析(工程)
控制工程
可靠性工程
功率(物理)
农学
量子力学
医学
生物
物理
航空航天工程
作者
Xiaosong Hu,Zhongwei Deng,Xianke Lin,Yi Xie,Remus Teodorescu
标识
DOI:10.1016/j.rser.2021.111695
摘要
Current battery management systems (BMSs) in automotive applications monitor and control batteries in a relatively simple, conservative manner, with limited capabilities of sensing, estimation, proactive controls, and fault diagnosis. With ever-increasing computing power onboard and/or in the cloud, enhanced environmental perception and vehicular communications, emerging electrified vehicles and smart grids provide unprecedented opportunities for designing and developing next-generation smart BMSs. However, three entrenched technical challenges need to be addressed, including 1) limited knowledge of battery internal states and parameters; 2) poor adaptability to extreme operating conditions; and 3) lack of efficient predictive maintenance, resulting in great concern for battery safety and economy. This paper aims to present some critical insights into possible solutions to the three challenges. First, the multi-physics coupled battery modeling concept is introduced to emphasize that looking at mechanical-electrochemical-thermal-aging dynamics is critically important for devising revolutionary BMS algorithms. Second, electrothermal modeling, advanced optimization routines, and predictive control with vehicular autonomy and connectivity facilitate innovative designs in dynamically hysteresis-aware thermal management, heat transfer under extreme fast charging, and preheating in a cold climate. Third, battery models and machine learning are complementary and can be very useful for improving battery remaining useful life prediction and fault diagnosis, achieving high-efficiency predictive maintenance.
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