适用于无人水下潜航器电池管理系统的SOC-SOH联合估计

Joint SOC-SOH estimation for UUV battery management system

为了提高无人水下潜航器(UUV)电池管理系统状态的估计精度,提出荷电状态-健康状态(SOC-SOH)联合估计方法.搭建测试台架,采用4组锂离子电池进行全寿命周期下的充放电测试,获取不同老化程度下的特性数据.经理论推导和实验分析设计四维表征因子,建立基于改进支持向量回归(SVR)的SOH估计模型.探究电池状态的耦合关系,建立基于扩展卡尔曼滤波(EKF)的SOC估计模型,采用遗忘因子递推最小二乘算法(RLS)更新模型参数,利用SOH对SOC估计结果进行修正.通过不同工况的实验进行验证,结果表明:四维表征因子和电池容量相关性好,SOH估计模型精度高,SOC估计模型精度在联合修正后得到提升.所提的联合估计方法具有较高的通用性和可靠性,可以作为有效的嵌入式电池管理系统状态估计算法.

A joint state of charge(SOC)-state of health(SOH)method of estimation was proposed in order to improve the state estimation accuracy of unmanned underwater vehicle(UUV)battery management system.A test bench was constructed,and four groups of lithium-ion batteries were used for charging and discharging test under the whole life cycle.Data under different attenuation degrees were obtained.Four-dimensional factors were designed by theoretical derivation and experimental analysis,and a SOH estimation model based on improved support vector regression(SVR)was established.The coupling relationship between battery states was explored.A SOC estimation model based on extended Kalman filter(EKF)was established and the forgetting factor recursive least squares(RLS)algorithm was used to update the model parameters.The SOC estimation results were corrected by SOH.The method was validated through different testing conditions experiment.Results show that the four-dimensional characterization factor and battery capacity have good correlation.The accuracy of SOH estimation model is high,and the accuracy of SOC estimation model is improved by joint modification.The proposed joint estimation method has high universality and reliability,and can be used as an effective state estimation algorithm for embedded battery management system.