数据驱动的电动汽车电池SOH预测

2015 IEEE International Conference on Consumer Electronics (ICCE) Pub Date : 2015-03-26 DOI:10.1109/ICCE.2015.7066533

Gae-won You, Sangdo Park, Sunjae Lee

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引用次数: 3

摘要

随着电动汽车的普及，作为电动汽车核心技术的电池管理系统(BMS)的研究备受关注。在BMS的众多功能中，以电池老化为指标的健康状态(SOH)预测是确定电池更换时间或行驶里程的关键。本文研究了电池动态充放电的电动汽车实际环境中SOH的预测问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Data-driven SOH prediction for EV batteries

As electric vehicles (EVs) have been popularized, research on battery management system (BMS) of EVs' core technology has considerably drawn attention. Among various functions of BMS, predicting state-of-health (SOH) that indexes batteries' aging is the most crucial to determine replacement time of the battery or to estimate driving mileage. This paper studies how to predict SOH in practical EV environments where the batteries are charged and discharged dynamically.

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来源期刊

2015 IEEE International Conference on Consumer Electronics (ICCE)

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