Adaptive voltage estimation for EV Li-ion cell based on artificial neural networks state-of-charge meter

This paper reports some results relating to adaptive cell modeling from neural network state-of-charge (SOC) estimation in a full-electric-vehicle (EV) application. The cells in question are commercialized ones, Lithium-ion Polymer based, with a nominal capacity of about 100 Ah and dedicated to energy applications. Using a recurrent neural network, we developed a SOC predictor that takes into account operational conditions. More importantly, the predictor allows very precise SOC estimation, therefore allowing the vehicle controller to confidently use the battery pack's full operating range without problem of over- or under-charging cells. In this work, the estimated SOC values helped to estimate the parameters of an adaptive-dynamic battery model using RLS algorithm with time-dependent forgetting factor. Simulation results confirmed the accuracy of the terminal voltage estimation of the battery.