More intelligent and robust estimation of battery state-of-charge with an improved regularized extreme learning machine.

State-of-charge (SOC) is the key parameter for battery management, and the accurate estimation of SOC is pretty important for the safe and stable operation of lithium batteries. This paper investigates a regularized extreme learning machine trained with the spectral Fletcher–Reeves algorithm and tuned with the beetle antennae search algorithm (BAS-SFR-RELM) for intelligent and robust SOC estimation. In the experiment section, the urban dynamometer driving schedule (UDDS) profile and the Los Angeles 92 (LA92) profile are performed on a battery test platform for data collection. In the simulation section, the root mean squared error (RMSE) and the mean absolute error (MAE) are adopted to evaluate the performance of the model. Compared with the linear regression (LR), the back propagation (BP) network, the multi-layer perceptron (MLP), and the long short-term memory (LSTM) network, the BAS-SFR-RELM method can efficiently obtain the optimal regularization coefficient to effectively prevent overfitting with faster convergence speed. Increasing the number of hidden neurons in the BAS-SFR-RELM appropriately can improve the SOC estimation precision. Implementing the BAS-SFR-RELM with the noise-added data set gives high robustness for SOC estimation • A novel model is constructed to estimate the SOC of lithium batteries. • The BAS algorithm is applied to optimize the regularization coefficient then overfitting is prevented. • The SFR algorithm is introduced to tune the output weights of the RELM. • The UDDS data set and the LA92 data set are used for model training, validation and testing. [ABSTRACT FROM AUTHOR]