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通过形集成和容量降低补偿,提高电动汽车电池中的SOC精度

Shreeram V Kulkarni¹, Sandeep Gupta², G Arjun¹

¹Department of Electrical and Electronics Engineering, Nitte Meenakshi Institute of Technology (NMIT), Nitte (Deemed to be University), Bengaluru, India.

Scientific reports

|January 31, 2026

概括

这项研究引入了一种用于电动汽车 (EV) 充电状态 (SOC) 估计的新梯形集成方法. 与传统方法相比,它提高了准确性,减少了错误,提高了电池性能和安全性.

科学领域:

电气工程电气工程
电池管理系统电池管理系统
计算方法计算方法

背景情况:

准确的电荷状态 (SOC) 估计对于电动汽车 (EV) 电池的安全性和性能至关重要.
传统的库伦计数方法存在累积错误,并且随着时间推移而变化.
没有建模的容量退化和传感器噪声进一步影响传统的SOC估计准确性.

研究的目的:

为 SOC 估计提出一种新的梯形数值整合技术.
为了弥补电池容量衰减和减少累积的SOC漂移.
为复杂的基于模型的估计器提供轻量级和成本效益的替代方案.

主要方法:

模拟了240分钟的合成驱动周期,其中包括放电和再生充电阶段.
与传统的矩形 (欧勒) 方法相比,比较了一种拟议的梯形整合方法.
使用诸如平均绝对误差 (MAE),SOC漂移和 ΔSOC 分布等指标评估性能.

主要成果:

梯形集成方法表明,SOC漂移和错误传播始终较低.
观察到显著的改善,特别是在充电和放电阶段之间的过渡期间.
与传统方法相比,拟议的方法实现了较低的累积漂移.

结论:

形数值集成技术有效地弥补了电动汽车电池容量减少的情况.
这一进步提供了一种适用于低功耗电池管理系统 (BMS) 的轻量级和准确的SOC估计解决方案.
该方法为提高电动汽车电池可靠性和安全性提供了一个可行的,低成本的替代方案.

关键词:

电池的退化降低了电池的性能.电池管理系统 (BMS) 是一种电池管理系统.容量减弱容量减弱复杂的系统.复杂的系统.库伦的计数是库伦的计数.电动汽车 (EV) 是一种电动汽车.离子电池是一种离子电池.在SOC估计中,SOC的估计.收费情况 (SOC)梯形形的整合方式.

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