Application of Artificial Intelligence to State-of-Charge and State-of-Health Estimation of Calendar-Aged Lithium-Ion Pouch Cells

Accurate State-of-Charge (SOC) and State-of-Health (SOH) estimation of lithium-ion batteries (LIBs) is essential for the battery management system (BMS). For the first time, a feed-forward artificial neural network (ANN) has been used to estimate SOC of calendar-aged lithium-ion pouch cells. Calendar life data has been generated by applying galvanostatic charge/discharge cycle loads at different storage temperature (35 degrees C and 60 degrees C) and conditions (fully-discharged and fully-charged). The data has been obtained at various C-rates for duration of 10 months at one-month intervals. In order to include LIB hysteresis effect, two separate ANNs have been trained for charge and discharge data. The ANN have achieved a Root Mean Square Error (RMSE) of 1.17% over discharge data and 1.81% over charge data, confirming the ability of the network to capture input-output dependency. The calendar-aged battery data at various degradation conditions has been employed to train a new ANN to estimate SOH. The ANN has shown RMSE of 1.67%, demonstrating the network capability to estimate SOH. This study highlights the importance of considering aging effects in SOC estimates and the ability of ANN to include these effects efficiently. (C) 2019 The Electrochemical Society.