The state-of-charge (SOC) estimation is an enabling technique for the efficient management and control of lithium-ion batteries (LIBs). This article proposes a novel method for online SOC estimation, which manifests itself with both high accuracy and low complexity. Particularly, the particle swarm optimization (PSO) algorithm is exploited to optimize the model parameters to ensure high modeling accuracy. Following this endeavor, the PSO algorithm is used to tune the error covariances of extended Kalman filter (EKF) leveraging the early stage segmental data of LIB utilization. Within this PSO-based tuning framework, the searching boundary is derived by scrutinizing the error transition property of the system. Experiments are performed to validate the proposed two-step PSO-optimized SOC estimation method. Results show that even by using a simple first-order model, the proposed method can give rise to a high SOC accuracy, which is comparative to those using complex high-order models. The proposed method is validated to excavate fully the potential of model-based estimators so that the computationally expensive model upgrade can be avoided.

中文翻译：

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基于模型的低阶充电状态估计的两步参数优化方法

充电状态（SOC）估算是一种有效管理和控制锂离子电池（LIB）的使能技术。本文提出了一种在线SOC估计的新方法，该方法具有较高的准确性和较低的复杂度。特别是，利用粒子群算法（PSO）对模型参数进行了优化，以确保较高的建模精度。经过这一努力，利用PSO算法利用LIB利用的早期分段数据来调整扩展卡尔曼滤波器（EKF）的误差协方差。在此基于PSO的调整框架内，通过仔细检查系统的错误转换属性来得出搜索边界。进行实验以验证所提出的两步PSO优化的SOC估计方法。结果表明，即使使用简单的一阶模型，所提出的方法也可以产生较高的SOC精度，这与使用复杂的高阶模型的结果相比。所提出的方法经过验证，可以充分挖掘基于模型的估计器的潜力，从而可以避免计算量大的模型升级。