Lithium-ion batteries have been widely used in many fields. The state-of-health is necessary and important for battery performance evaluation and lifetime prediction. A reliable state-of-health estimation is essential to help batteries work in a safe and suitable condition. In this paper, a novel state-of-health estimation approach is proposed for lithium-ion batteries based on statistical knowledge. An improved battery model, which combines the open-circuit-voltage modeling and the Thevenin equivalent circuit model, is proposed to improve the model accuracy and study the relation between internal parameters and states of the battery. The joint extended Kalman filter-recursive-least squares algorithm is employed to estimate battery state-of-charge and identify the model parameters and open-circuit-voltage simultaneously. Then a particle swarm optimization-least square support vector regression approach is employed to give a reliable state-of-health estimation result with high accuracy and good generalization ability, where the particle swarm optimization algorithm is used to improve the algorithm ability of global optimization. In order to verify the accuracy of the proposed method, static and dynamic current profile tests are carried out on lithium iron phosphate batteries in different aging levels. The experimental results indicate that the proposed method can present suitability for state-of-health estimation with high accuracy.

锂离子电池已广泛用于许多领域。健康状况对于电池性能评估和寿命预测是必要且重要的。可靠的健康状况评估对于帮助电池在安全合适的条件下工作至关重要。在本文中，基于统计知识，提出了一种新颖的锂离子电池健康状态估计方法。提出了一种改进的电池模型，该模型结合了开路电压模型和戴维南等效电路模型，以提高模型的准确性并研究电池内部参数与状态之间的关系。采用联合扩展卡尔曼滤波器-递推最小二乘算法来估计电池的充电状态，并同时识别模型参数和开路电压。然后采用粒子群优化-最小二乘支持向量回归的方法给出可靠的健康状态估计结果，具有较高的准确性和良好的泛化能力，其中使用了粒子群优化算法提高了全局优化的算法能力。为了验证该方法的准确性，在不同老化水平的磷酸铁锂电池上进行了静态和动态电流曲线测试。实验结果表明，该方法可以为健康状态估计提供较高的适用性。其中使用了粒子群优化算法来提高全局优化的算法能力。为了验证该方法的准确性，在不同老化水平的磷酸铁锂电池上进行了静态和动态电流曲线测试。实验结果表明，该方法可以为健康状态估计提供较高的适用性。其中使用了粒子群优化算法来提高全局优化的算法能力。为了验证该方法的准确性，在不同老化水平的磷酸铁锂电池上进行了静态和动态电流曲线测试。实验结果表明，该方法可以为健康状态估计提供较高的适用性。