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基于FOAMIUHF-UKF模型的离子电池SOC和SOH的联合估计

Zheng Ye¹, ZhiHui Deng², YongHong Xu³

¹College of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, 100192, China.

Scientific reports

|November 19, 2025

概括

准确的动力电池状态估计对于电动汽车至关重要. 本研究引入了一种分数顺序模型,具有适应性波器,用于精确的电荷状态 (SOC) 和健康状态 (SOH) 估计,提高安全性和可靠性.

科学领域:

电气工程电气工程
材料科学材料科学材料科学
计算科学计算科学

背景情况:

准确的充电状态 (SOC) 和健康状态 (SOH) 估计对于电动汽车 (EV) 的安全性和性能至关重要.
现有的方法在对离子电池的SOC和SOH进行共同估计时,面临着模型准确性和稳定性的挑战.

研究的目的:

开发和验证离子电池的联合SOC和SOH估计方法.
在动态运行条件和老化下提高电池状态估计的准确性和稳定性.

主要方法:

建立了分数级二级等效电路模型 (FOM).
采用子搜索算法 (SSA) 进行参数和分数顺序识别.
集成了一个可适应的多创新无香H无穷过器 (FOAMIUHF) 用于SOC估计和一个无香卡尔曼过器 (UKF) 用于SOH预测.

主要成果:

拟议的FOAMIUHF算法在各种驾驶条件下实现了SOC估计的最大平均绝对误差 (MAE) 为0.53%和根平均平方误差 (RMSE) 为0.61%.
在不同初始 SOC 级别和老化状态中,在 SOC 估计误差低于 1% 的情况下,证明了高准确性.
在准确性和稳定性方面超过了FOMIUKF-UKF和FOMIUKF等现有方法.

结论:

分数顺序模型与FOAMIUHF相结合,为电动汽车的联合SOC/SOH估计提供了可靠和有效的解决方案.
该方法显示了提高电动汽车运行安全性和效率的巨大潜力.

关键词:

适应式多创新无香气的h-infinity过器分数顺序模型的分数顺序模型.联合估计联合估计离子电池是一种离子电池.收费状态收费状态健康状况身体健康状况

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