【摘要】 磷酸铁锂电池荷电状态(SOC)用于表征电池的剩余电量,是电池管理系统的重要参数。对SOC进行准确估计有助于提高电池利用率,保证电池的使用寿命和安全。但是SOC不能直接从外部测量得到,只能通过各种间接的方法求得,因此寻求准确的电池SOC估计算法非常重要。对磷酸铁锂电池进行建模,使用14组电池充放电数据分段进行参数辨识,得到具有广泛适用性的模型参数。基于此模型,运用自适应扩展卡尔曼滤波算法进行SOC估计,克服了常用扩展卡尔曼滤波会受到噪声影响的弊端,并通过仿真分析证明了算法的优越性。更多还原

【Abstract】 The state of charge（SOC）of a LiFePO₄ battery is used to characterize its remaining capacity.It is one of the most important parameters of the battery management system.Estimating battery SOC accurately is beneficial to improving battery utilization,lengthening battery life cycle and using it safely.However,the SOC cannot be measured directly and can only be obtained by various indirect methods.Therefore,an accurate battery SOC estimation algorithm is very important.In this paper,the LiFePO₄ battery was modeled,and 14 groups of battery charge and discharge data were used during the parameter identification,then the model parameters with wide applicability were obtained.Based on this model,the adaptive extended Kalman filter algorithm was used for SOC estimation,which overcomes the influence of noise.The superiority of this algorithm over EKF was proved by simulation analysis.更多还原