Electrochemical models based on first principles have shown great potential to accurately predict both the terminal voltage and internal states of lithium ion batteries. However, such models usually require significant computational time and contain a large number of parameters that describe the physical and electrochemical properties of the battery. In this paper, a systematic methodology is presented to generate control-oriented electrochemical models and identify those electrochemical parameters. The solid-phase governing partial differential equations are reduced by the volume average method, and a Galerkin projection is applied to the liquid-phase partial differential equation for model order reduction. Then, a two-step parameter identification strategy based on particle swarm optimization is proposed to obtain 26 model parameter values. Extensive calibration and validation results based on 18 sets of experimental data show that the reduced-order model with identified parameters agrees very well with experimental data at a wide range of operating conditions, covering steady-state discharge, relaxation, different driving cycles and ambient temperatures. A cubature Kalman Filter is then designed to demonstrate the capabilities of the resultant model in estimating battery state of charge. Results against all datasets show that the estimation error is less than $\pm$ 1% for most of the 18 conditions.

基于第一原理的电化学模型显示出巨大的潜力，可以准确预测锂离子电池的端电压和内部状态。但是，这种模型通常需要大量的计算时间，并且包含大量描述电池物理和电化学特性的参数。在本文中，提出了一种系统化的方法来生成面向控制的电化学模型并识别那些电化学参数。固相控制的偏微分方程通过体积平均法进行了简化，并且将Galerkin投影应用于液相偏微分方程以进行模型降阶。然后，提出了一种基于粒子群算法的两步参数辨识策略，获得了26个模型参数值。基于18组实验数据的广泛校准和验证结果表明，具有确定参数的降阶模型与在宽范围的工作条件下的实验数据非常吻合，涵盖了稳态排放，松弛，不同的行驶周期和环境温度。然后设计了一个库曼卡尔曼滤波器，以证明所得模型在估计电池充电状态方面的能力。针对所有数据集的结果表明，估计误差小于 然后设计了一个库曼卡尔曼滤波器，以证明所得模型在估计电池充电状态方面的能力。针对所有数据集的结果表明，估计误差小于 然后设计了一个库曼卡尔曼滤波器，以证明所得模型在估计电池充电状态方面的能力。针对所有数据集的结果表明，估计误差小于$\pm$ 在18种情况中，大多数情况下为1％。