锂电池荷电状态（SOC）估计具有非线性和时变性，利用传统BP神经网络进行SOC估计，存在收敛速度慢、准确度低的缺点，因此提出了一种基于TLPSO-BP神经网络算法的锂电池SOC估计方法。首先采用Tent混沌映射和莱维（Levy）飞行策略改进粒子群（PSO）算法，然后利用改进粒子群算法对传统BP神经网络的初始权值和阈值进行优化以提高SOC估计的精度，最后将所提算法在激烈驾驶工况（US06）下对锂电池进行SOC估计，并与PSO-BP算法和BP算法做对比。试验结果表明，利用TLPSO-BP算法估计锂电池SOC的均方根误差相比PSOBP算法和BP算法分别降低了14.5%和32.4%，表明该算法具有良好的泛化能力和较高的估计精度。

The state of charge (SOC) estimation of lithium battery exhibits nonlinear and time-varying characteristics. Traditional BP neural networks for SOC estimation suffer from slow convergence speed and reduced accuracy. A lithium battery SOC estimation method was proposed based on the TLPSO-BP neural network algorithm. The method first enhanced the particle swarm optimization (PSO) algorithm by Tent chaotic mapping and Levy flight strategy, thereby improving exploration and convergence behavior. These enhancements were then applied to optimize the initial weights and thresholds of the traditional BP neural network, which increases the accuracy and stability of SOC estimation. Finally, the proposed algorithm was applied to SOC estimation of lithium battery under aggressive driving conditions (US06) and compared with PSOBP and BP algorithms. The experimental results indicate that the root mean square error of SOC estimation using the TLPSOBP algorithm is reduced by 14.5% and 32.4% compared to the PSO-BP and BP algorithms, respectively. These improvements verify the good generalization capability and high estimation accuracy of the algorithm.