Aiming at the problems of low computing efficiency, poor real-time performance and low estimation accuracy of the existing SOH and SOC estimation methods for electric vehicle power battery, a recurrent gated neural network model is proposed to accurately estimate the SOC & SOH of electric vehicle power battery. Firstly, the calculation methods of update gate and reset gate in the Gated Recurrent Unit are improved and the candidate hidden state activation function is replaced by the ThLU function to shorten the training time and effectively alleviate the gradient vanishing. Secondly, the sequence data input method is optimized, and the loop GRU calculation mode is introduced to improve the model computing efficiency and estimation accuracy. Lastly, the model is based on the convolutional neural network and the improved gate recurrent unit, the full-cycle SOH and SOC are simultaneously estimated using the voltage, current, and temperature data collected by the sensors, and the SOH estimation is included in the SOC estimation to eliminate the adverse effects of the aging factor on the SOC estimation. Experimental validation using the Oxford University battery dataset shows that compared with the traditional estimation model, the SOC estimation accuracy of the model proposed in this paper is effectively improved, and the prediction error basically stays within 0.5%.