基于ASSA-RBF联合算法的三元锂离子电池SOC估计

准确估计三元锂电池的荷电状态(SOC)是保障电动汽车安全稳定运行的基础。针对传统BP神经网络估计精度不高，而RBF神经网络也容易陷入局部最优的问题，提出一种基于自适应麻雀搜索算法与RBF神经网络联合的三元锂电池SOC估计方法。首先，对标准麻雀搜索算法进行改进，采用精英混沌反向机制初始化麻雀种群，采用柯西-高斯变异策略优化麻雀种群中跟随者位置更新公式；然后，使用改进后的麻雀搜索算法对RBF神经网络的初始权值和宽度参数进行寻优，以提升算法对SOC的估计精度；最后，基于三元锂电池的充放电实验数据进行模型验证。结果表明，动态应力测试工况下，所提联合算法模型SOC估计均方根误差为0.694%,平均百分比误差为3.15%,能很好的应用于三元锂电池SOC估计。

SOC estimation of ternary lithium-ion battery based on ASSA-RBF joint algorithm

Accurately estimating the state of charge (SOC) of ternary lithium batteries is the foundation for ensuring the safe and stable operation of electric vehicles. In response to the problem of low estimation accuracy of traditional BP neural networks and the tendency of RBF neural networks to fall into local optima, this paper proposes a ternary lithium battery SOC estimation method based on the combination of adaptive sparrow search algorithm and RBF neural networks. Firstly, the standard sparrow search algorithm is improved by using the elite chaos reverse mechanism to initialize the sparrow population, and the Cauchy Gaussian mutation strategy is used to optimize the follower position update formula in the sparrow population. Then, the improved sparrow search algorithm is used to optimize the initial weight and width parameters of the RBF neural network to improve the algorithm′s estimation accuracy of SOC. Finally, the model was validated based on the charging and discharging experimental data of ternary lithium batteries. The results show that under dynamic stress testing conditions, the proposed joint algorithm model has a root mean square error of 0.694% and an average percentage error of 3.15% in SOC estimation, which can be well applied to SOC estimation of ternary lithium batteries.