A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation

电动汽车 健康状况 航程(航空) 荷电状态 汽车工程 电压 锂离子电池 计算机科学 可靠性工程 工程类 电池(电) 电气工程 功率(物理) 物理 航空航天工程 量子力学
作者
Verena Klass,Mårten Behm,Göran Lindbergh
出处
期刊:Journal of Power Sources [Elsevier BV]
卷期号:270: 262-272 被引量:209
标识
DOI:10.1016/j.jpowsour.2014.07.116
摘要

Capacity and resistance are state-of-health (SOH) indicators that are essential to monitor during the application of batteries on board electric vehicles. For state-of-health determination in laboratory environment, standard battery performance tests are established and well-functioning. Since standard performance tests are not available on-board a vehicle, we are developing a method where those standard tests are applied virtually to a support vector machine-based battery model. This data-driven model is solely based on variables available during ordinary electric vehicle (EV) operation such as battery current, voltage and temperature. This article contributes with a thorough experimental validation of this method, as well as the introduction of new features – capacity estimation and temperature dependence. Typical EV battery usage data is generated and exposed to the suggested method in order to estimate capacity and resistance. These estimations are compared to direct measurements of the SOH indicators with standard tests. The obtained estimations of capacities and instantaneous resistances demonstrate good accuracy over a temperature and state-of-charge range typical for EV operating conditions and allow thus for online detection of battery degradation. The proposed method is also found to be suitable for on-board application in respect of processing power and memory restrictions.
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