电动汽车的先进电池诊断使用基于CAN的BMS数据与EKF以及数据驱动的预测模型
Shreeram V Kulkarni1, G Arjun1, Sandeep Gupta2
1Department of Electrical and Electronics Engineering, Nitte Meenakshi Institute of Technology (NMIT), Nitte (Deemed to Be University), Bengaluru, India.
Scientific reports
|September 25, 2025
概括
本研究引入了用于电动汽车 (EV) 电池监控的混合框架,提高了安全性和耐用性. 它结合了机器学习和基于模型的方法,用于准确的健康状态 (SoH) 和负荷状态 (SoC) 评估.
科学领域:
- 电动汽车电池技术 电动汽车电池技术
- 数据驱动的诊断数据驱动的诊断
- 离子电池管理系统的管理系统
背景情况:
- 准确的电池健康状态 (SoH) 和充电状态 (SoC) 对电动汽车 (EV) 的安全性,性能和寿命至关重要.
- 传统的监控技术,如库伦计数,可能容易受到传感器噪声和模型不准确的影响.
研究的目的:
- 开发和验证一种新的混合诊断框架,以改善电动汽车电池监控.
- 提高电池健康状态 (SoH) 和充电状态 (SoC) 估计的准确性和可靠性.
主要方法:
- 实施混合框架,结合统计分析,机器学习和基于模型的估计.
- 使用扩展卡尔曼波器 (EKF) 来减轻传感器噪声和模型错误.
- 采用随机森林回归用于SoH评估,k-means集群和动态时间曲线 (DTW) 用于细胞行为分析,以及主要组件分析 (PCA) 用于电压不平衡识别.
主要成果:
- 在传统的库伦计数技术上,EKF表现出了优越的性能.
- 与线性回归相比,随机森林模型实现了SoH评估的更高准确性.
- 有效地确定了异常值单元并分析了电压失衡趋势.
结论:
- 拟议的混合框架为可靠和可解释的电动汽车电池诊断提供了一个实用和可扩展的解决方案.
- 这种方法显著提高了电池监控能力,有助于提高电动汽车安全性和生命周期管理.
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