State of Health Estimation of Lithium Ion Batteries using Recurrent Neural Network and its Variants

Numerous internal and external factors affect performance and capacity degradation of batteries over a period of time. SOH prediction of batteries becomes challenging task owing to unpredictable and unknown features which influence battery's health. This paper proposes a data-driven approach for SOH estimation by using the battery ageing datasets of Prognostic Center of Excellence (PCoE) of NASA. SOH estimation requires tracking of long sequential and temporal data of battery aging which exhibit dynamic states. The state of the art algorithm, Recurrent Neural Networks (RNN), due to its internal memory isappropriate for processing and predicting battery SOH. Hence this work employs different RNN techniques to build battery SOH prediction model, and the results of different techniques are compared and analyzed. The internal modeling parameters are trained by NASA battery datasets, where discharge cycles are introduced for SOH estimation. Experimental results show that RNN techniques can accurately estimate battery SOH.