Multi-scenario surface temperature estimation in lithium-ion batteries with transfer learning and LGT augmentation.

The State of Temperature (SOT) plays a crucial role in ensuring the safety and reliability of lithium-ion batteries, as well as the stability of electric vehicles (Evs). Recently, data-driven methods for lithium-ion battery temperature estimation have often obtained short-term estimation information in a single scenario. To alleviate the above issues, this paper proposes a method for estimating the surface temperature of lithium-ion batteries based on Local and Global Trend (LGT) data augmentation and Long Short-Term Memory (LSTM) model transfer. Initially, voltage and current data are processed using two types of filtering methods to reduce noise while preserving the fluctuating characteristics of the original data. Differential features are subsequently extracted from the polynomially filtered curves. Then all the data, including the filtered data and the features are fed into the LGT data augmentation algorithm, followed by the use of an LSTM transferable model to estimate the surface temperature of the target battery. Furthermore, the method is tested on both laboratory datasets and datasets with real driving conditions, covering a wide range of environmental temperatures and driving scenarios typical of EVs. The experimental results demonstrate that retraining with only the first 30 % of the target battery's data yields effective SOT estimation results. • Utilizing voltage and current to capture the temperature characteristics. • LGT augmentation enhances data diversity and robustness in SOT prediction models. • Transferable LSTM model predicts surface temperature with limited early-stage data. • The method's performance is validated on lab and real-world driving datasets. [ABSTRACT FROM AUTHOR]