Paris Ali Topan, M. N. Ramadan, Ghufron Fathoni, A. Cahyadi, O. Wahyunggoro
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引用次数: 69
摘要
为了避免电池故障并保持电池寿命，系统需要考虑电池管理系统(battery Management system, BMS)几个参数中的两个，如充电状态(State of Charge, SOH)和健康状态(State of Health, SOH)来控制其使用。电池管理系统中的充电状态提供了电池容量的百分比，而健康状态则衡量电池的健康状况。采用Thevenin电池模型描述电池的极化特性和动态行为，并使用KalmanFilter(KF)进行估计。采用递归最小二乘法对模型中的参数进行估计。结果表明，KF估计SOH的平均相对误差为5.26%，RLS估计SOH的平均相对误差为7.08%。
State of Charge (SOC) and State of Health (SOH) estimation on lithium polymer battery via Kalman filter
To avoid battery failure and keep the battery lifetime, a system needs control its use by considering two of several parameters of Battery Management System (BMS) such as State of Charge (SOH) and State of Health (SOH). The State of Charge in Battery Management System provides the percentage of battery capacity, while the State Of Health measures the battery health. The Thevenin battery model is used to describe polarization characteristic and dynamic behavior of the battery and estimared using KalmanFilter(KF). Parameters in the model were estimated using Recursive Least Square. As the results, KF is better then RLS to estimate SOH with a mean relative error as much as 5.26% while RLS has 7.08%.
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