Accurate estimation of the lithium-ion battery state of charge plays an important role in the real-time monitoring and safety control of batteries. In order to solve the problems that the real-time estimation of the lithium-ion battery is difficult and the estimation accuracy is not high under various working conditions, a lithium-ion battery is taken as a research object, and the working characteristics of the lithium-ion battery are studied under various working conditions. In order to reduce the computational complexity of the traditional unscented Kalman algorithm, an improved unscented Kalman algorithm is proposed. Considering the importance of accurately estimating the initial state of charge for later estimation, the initial estimation value is calibrated by using the open-circuit voltage method. Then, the improved unscented Kalman filter algorithm based on a reduced-order model is used for assessing and tracking to realize real-time high-precision estimation of the state of charge of the lithium-ion battery. A simulation model is built and combined with a variety of working conditions data for performance analysis. The experimental results show that the convergence speed and tracking effect are good and that the estimation error control is within 0.8%. It is verified that the reduced order of the three-particle nonlinear transform unscented Kalman results in higher accuracy in the state-of-charge estimation of lithium-ion batteries.

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结合三粒子非线性变换无迹卡尔曼滤波的新型降阶建模方法用于电池荷电状态估计

准确估计锂离子电池的荷电状态对电池的实时监测和安全控制具有重要作用。为解决锂离子电池在各种工况下实时估算困难、估算精度不高的问题，以锂离子电池为研究对象，对锂离子电池的工作特性进行研究。锂离子电池在各种工作条件下进行了研究。为了降低传统无迹卡尔曼算法的计算复杂度，提出了一种改进的无迹卡尔曼算法。考虑到准确估计初始荷电状态对后期估计的重要性，采用开路电压法校准初始估计值。然后，采用基于降阶模型的改进无迹卡尔曼滤波算法进行评估和跟踪，实现对锂离子电池荷电状态的实时高精度估计。建立仿真模型，结合多种工况数据进行性能分析。实验结果表明，收敛速度和跟踪效果良好，估计误差控制在0.8%以内。验证了三粒子非线性变换无迹卡尔曼的降阶导致锂离子电池荷电状态估计的更高准确性。建立仿真模型，结合多种工况数据进行性能分析。实验结果表明，收敛速度和跟踪效果良好，估计误差控制在0.8%以内。验证了三粒子非线性变换无迹卡尔曼的降阶导致锂离子电池荷电状态估计的更高准确性。建立仿真模型，结合多种工况数据进行性能分析。实验结果表明，收敛速度和跟踪效果良好，估计误差控制在0.8%以内。验证了三粒子非线性变换无迹卡尔曼的降阶导致锂离子电池荷电状态估计的更高准确性。