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.