提高电动汽车电池寿命:集成主动平衡和机器学习来准确估计RUL
Yara A Sultan1, Abdelfattah A Eladl2, Mohamed A Hassan3
1Mechatronics Department, Faculty of Engineering, Horus University-Egypt, New Damietta, Egypt.
Scientific reports
|January 4, 2025
概括
本研究介绍了电动汽车离子电池组的活性电池平衡方法,以平衡充电水平,提高性能和寿命. 机器学习模型准确地预测了电池的剩余使用寿命,提高了电动汽车电池的整体管理.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 电动汽车 (EV) 使用离子电池组用于储能.
- 不一致的电池特性和操作条件导致电荷状态不平衡 (SOC),降低电动汽车电池容量并加速降解.
- 有效的电池管理系统对于优化电动汽车性能和寿命至关重要.
研究的目的:
- 开发和验证电动汽车中离子电池组的活性电池平衡方法.
- 通过在各个电池中均等SOC来增强充电和放电能力.
- 评估机器学习模型,准确预测电池寿命和剩余使用寿命 (RUL).
主要方法:
- 实施一个双策略的活性电池平衡系统:充电平衡以最大限度地提高容量和放电平衡以延长持续时间.
- 评估了七个机器学习模型 (k-最近的邻居,随机森林等). 使用R平方 (R2) 和平均绝对误差 (MAE) 进行RUL预测.
- 集成主动平衡与RUL预测,以创建一个反循环,以优化电池管理.
主要成果:
- 提出的主动平衡方法有效地减少了SOC差异,从而提高了充电和放电能力.
- K-最近邻居和随机森林模型在RUL预测中表现出高准确性,达到0.996+的R2值和低MAE.
- 综合系统表明,平衡的SOC水平促进了电池的健康,而RUL预测则为最佳平衡策略提供了信息.
结论:
- 主动电池平衡对于提高电动汽车离子电池组的性能和寿命至关重要.
- 机器学习为准确的电池RUL预测提供了一个强大的工具,这对于主动维护至关重要.
- 积极平衡和RUL预测的协同集成代表了电动汽车电池管理系统的重大进步,提高了可靠性和寿命.
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