The state of charge (SOC) of the battery is one of the core parameters for managing lithium batteries in electric vehicles. This paper proposes a lithium battery SOC estimation model based on an improved white whale algorithm optimized BiTCN-BiGRU. Firstly, a SOC estimation model combining bidirectional time domain convolutional network (BiTCN) and bidirectional gated recurrent unit (BiGRU) is constructed. Then, the beluga whale optimization (BWO) is used to optimize the hyperparameters of the BiTCN-BiGRU model to fully leverage the advantages of the combined network model. Improvement strategies are introduced in the exploration and whale fall stages of traditional BWO to solve the problem of traditional BWO easily falling into local optima and slow convergence speed. Finally, the performance of the improved SOC estimation model was verified based on the open-source lithium battery charging and discharging dataset. The results showed that under standardized urban cycling conditions at three temperatures, the improved white whale algorithm optimized the BiTCN-BiGRU model SOC estimation with an average absolute error of 0.428% and a root mean square error of 0.38%, which can be well applied to lithium battery SOC estimation.