Accurate state of charge (SOC) estimation is considered as the main barrier to adopt Lithium-ion battery-based electric vehicles as alternatives to conventional internal combustion engine vehicles. There are several SOC estimation techniques recommended by many researchers still; they are all not accurate. This paper presents a robust and accurate SOC estimation battery model developed using an independently recurrent neural network (IndRNN). The proposed SOC estimation battery model is developed by training IndRNN constructively under various experimental datasets collected from Lithium Nickel Cobalt Aluminium Oxide battery cell at different ambient temperatures. Without any prior knowledge about battery internals, the proposed battery model successfully characterizes the non-linear behaviour of the battery effectively. Furthermore, its performance is proved by comparing with similar RNN architecture such as gated recurrent unit (GRU) and long short-term memory (LSTM). The attained results demonstrate that an IndRNN outperformed both GRU and LSTM in terms of accuracy under different electric vehicle drive cycle with minimal root mean square error of 0.7633% and mean absolute error of 0.6389% for diverse temperature conditions. Abbreviations: ANN: artificial neural network; BMS: battery management system; BPNN: back-propagation neural network; DFS: deep feature selection; EV: electric vehicle; FL: fuzzy logic; FNN: feedforward neural network; GA: genetic algorithm; GRU: gated recurrent unit; HWFET: highway fuel economy test; ICE: internal combustion engine; IndRNN: independently recurrent neural network; LA92: Los Angeles 92; LiB: lithium-ion battery; LSTM: long short-term memory; MAE: mean absolute error; MAX: maximum error; RNN: recurrent neural network; RMSE: root mean square error; ReLU: rectified linear unit; SOC: state of charge; SOH: state of health; SVM: support vector machine; US06: supplemental federal test procedure; UDDS: urban dynamometer driving schedule. © 2021 IETE.