Transfer Learning-Based State of Health Estimation for Lithium-Ion Battery at Varying Temperatures

Accurate estimation of the Lithium-ion batteries (LiBs) state of health (SOH) is essential to ensure the safe and reliable operation of battery-powered devices. Most of the current data-driven SOH estimation models are designed under fixed ambient temperatures, overlooking the high sensitivity of LiBs to changing ambient temperatures. To bridge this gap, a novel method is proposed with transfer learning (TL) to model and estimate the SOH at varying ambient temperatures. First, canonical variate analysis is employed to capture the temporal dynamics in the time-series data of LiBs and extract temporal features. Second, at the reference temperature, an interpretable SOH estimation model with a long short-term memory network is utilized to learn the regression relationship between the temporal features and the labeled capacities. Third, at the new temperature, a small amount of data is projected into the key feature domain at the reference temperature to obtain the common temporal features. Afterward, TL is employed to take advantage of the existing process knowledge through keeping all functional layers of the previous well-trained SOH estimation model. Finally, the efficacy of the proposed method is verified on the NASA dataset with two discrete temperatures, 24°C and 44°C. Evaluated by the index of root mean squared error, the proposed method is capable of improving prediction accuracy by 94.18% when the model is transferred from 44°C to 24°C.