A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models.

Accurate estimating the state of charge (SOC) can improve battery reliability, safety, and extend battery service life. The existing battery models used for SOC estimation inadequately capture the dynamic characteristics of a battery in a wide temperature over the full SOC range, leading to significant inaccuracies in SOC estimation, especially in low temperature and low SOC. A novel SOC estimation approach is developed based on a fusion of neural network model and equivalent circuit model. Firstly, the weight-SOC-temperature relationship is established by obtaining the weights of the equivalent circuit model and the neural network model offline using the standard deviation weight assignment method. Following that, an online adaptive weight correction approach is implemented to update the weight-SOC-temperature relationship. Finally, a novel multi-algorithm fusion technique is utilized to achieve SOC estimation accuracy within 1%. The results clearly demonstrate that the developed approach achieves twice the accuracy of the existing approach, highlighting its superior effectiveness. • A method to integrate a NN model and an ECM is developed to obtain a fusion model. • An online adaptive correction method for updating the model weight is developed to build the fusion model. • A method for SOC fusion estimation is proposed in a wide temperature over the full SOC range. • The robustness of the method is verified at various temperatures and operating conditions. [ABSTRACT FROM AUTHOR]