Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks
Research into the monitoring of lithium-ion batteries has become increasingly important, due to their use in a variety of complex, high-performance, energy-storage applications in hybrid and electric vehicles (HEV and EV). This paper investigates the behavior and state-of-health monitoring of lithium-ion batteries. The first part presents a model for a high-energy-density lithium-ion cell dedicated to EV applications, based on Electrochemical Impedance Spectroscopy (EIS) measurements. The key characteristic of this model, based on an equivalent-circuit approach, is not only its simplicity, but also the fact it takes into account several important phenomena that occur inside lithium cells, such as the dependence of part of the internal resistance and the open-circuit voltage on the state of charge (SOC). The second part describes state-of-health (SOH) monitoring of a high-power-density lithium-ion cell, using recurrent neural networks (RNNs) to predict the deterioration in battery performance. This comprehensive approach was used to monitor several batteries dedicated to HEV and EV applications, covering the entire process, from behavior modeling to predicting performance degradation and use.
recurrent neural network lithium-ion battery modeling electrochemical impedance spectroscopy aging