随着能源短缺和环境污染问题的日益严重，新能源汽车尤其是电动汽车迎来了前所未有的发展机遇。电池管理系统是电动汽车最核心的技术之一，精确的电池SOC估计对于电池管理系统至关重要。本文以18650三元锂离子电池为研究对象，以提高电池SOC估计精度为出发点，进行了以下研究：

首先，分析了锂离子电池工作原理和关键性能参数，通过电压、容量和内阻等特性测试实验对电池的动态特性进行了分析。对比分析了常用的四种等效电路模型后选用二阶RC模型作为本文所用锂离子电池的等效电路模型。对电池进行HPPC测试，通过指数拟合的方法离线辨识得到了模型参数，并在MATLAB/Simulink中验证了所建立的二阶RC模型的有效性。

其次，针对在不确定性噪声环境下传统最小二乘法参数辨识精度不高的问题，提出带遗忘因子的偏差补偿递推最小二乘法进行在线参数辨识，并以电池端电压为观测值对其辨识精度进行了验证。在实现参数辨识的基础上，采用扩展卡尔曼滤波（EKF）、无迹卡尔曼滤波（UKF）和容积卡尔曼滤波（CKF）三种滤波算法分别进行SOC估计，并对三种算法的精度进行对比，结果表明基于CKF算法的估计精度最优。针对CKF算法系统噪声无法自适应更新和协方差矩阵易失去正定性可能导致算法中止的问题，将Sage-Husa自适应滤波算法和奇异值分解的方法引入其中，采用基于奇异值分解的自适应容积卡尔曼滤波算法进行电池SOC估计，提高了估计精度。

最后，将带遗忘因子的偏差补偿递推最小二乘法与基于奇异值分解的自适应容积卡尔曼滤波算法相结合，实现了电池模型参数和SOC的联合估计，进一步提高了估计精度。基于现有电池测试平台，搭建了算法硬件验证平台，对电池电压、电流采集等系统的软硬件进行了设计。结果表明，本文所提算法的硬件验证结果与仿真结果相一致，能够实现SOC的精确估计，证明了算法在嵌入式设备中的可行性和精确性。

With the increasingly serious problems of energy shortage and environmental pollution, new energy vehicles, especially electric vehicles, have ushered in unprecedented development opportunities. The battery management system is one of the core technologies of electric vehicles, and accurate battery SOC estimation is very important for it. This paper takes the 18650 ternary lithium-ion battery as the research object, in order to improve the estimation accuracy of battery SOC, the following researches are carried out:

Firstly, the working principle and key performance parameters of lithium-ion battery are analyzed, and the dynamic characteristics of the battery are analyzed through the test experiments of voltage, capacity and internal resistance. After comparative analysis of four commonly used equivalent circuit models, the second-order RC model is selected as the equivalent circuit model of lithium-ion battery used in this paper. The HPPC test of the battery are carried out, and the model parameters are obtained by offline identification using the exponential fitting method. The effectiveness of the established second-order RC model is verified in MATLAB/Simulink.

Secondly, in order to solve the problem of low identification accuracy of traditional least squares method in uncertain noise environment, the bias compensation recursive least squares method with forgetting factor is proposed for online parameter identification, and the identification accuracy is verified by taking the battery terminal voltage as the observation value. On the basis of parameter identification, extended Kalman filter(EKF), unscented Kalman filter (UKF) and cubature Kalman filter (CKF) are used to estimate SOC respectively, and the accuracy of the three algorithms is compared. The results show that the estimation accuracy based on CKF algorithm is the best. In view of the problem that the system noise of CKF algorithm cannot be adaptively updated and the covariance matrix is easy to lose the positive definiteness, which may lead to the algorithm suspension, the Sage-Husa adaptive filtering algorithm and singular value decomposition method are introduced. The adaptive cubature Kalman filtering algorithm based on singular value decomposition is used to estimate the battery SOC, and the estimation accuracy is improved.

Finally, the bias compensation recursive least square method with forgetting factor and the adaptive cubature Kalman filter algorithm based on singular value decomposition are combined to realize the joint estimation of battery model parameters and SOC, which further improves the estimation accuracy. Based on the existing battery test platform, the hardware verification platform of the algorithm is built, and the hardware and software of the battery voltage and current acquisition system are designed. The experimental results show that the hardware verification results of the proposed algorithm are consistent with the simulation results, which can realize the accurate estimation of SOC and prove the feasibility and accuracy in embedded devices.

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