The SOH estimation of LiFePO4 battery based on internal resistance with Grey Markov Chain

Abstract

The state of health (SOH) of batteries in electric vehicles (EVs) results in the powertrain performance of EVs. However, to predicate the accurate SOH become a challenge due to the complicated factors, such as open circuit voltage, current, temperature, state of charge (SOC), internal resistance and the combination which affect the battery SOH. It is of primary importance that the SOH can be predicted on-line and real-time. This paper proposes a novel SOH prediction method based on Grey-Markov Chain (GMC) to determine the battery health state by taking into account the battery internal resistance. A real-time battery internal resistance monitor device is designed and applied in experimental. The results show that the proposed GMC model is effective and efficient in estimating SOH for LiFePO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> Power Battery.