Ensuring the safety of lithium-ion power batteries is the primary prerequisite for developing electric vehicles and energy storage systems. The conventional method relies on temperature parameters and only qualitatively assesses the state of safety (SOS), which reduces the warning time of the battery management system (BMS). Here we present a thermal runaway warning method based on SOS. Specifically, we analyze the strain evolution trend of thermal runaway under different abuse conditions and propose the strain trigger point for thermal runaway. Furthermore, multidimensional parameters such as temperature rise, median voltage, capacity, power, and strain are used to quantify the SOS. The SOS is a battery state parameter, with its value ranging from 0% to 100%. Experimental results demonstrate that the presented approach can warn of thermal runaway around 5 h in advance.

Efficient and accurate available capacity estimation of lithium-ion batteries is crucial for ensuring the safe and effective operation of electric vehicles. However, incomplete charging cycles in practical applications challenge conventional methods. Here we manipulate fragmented charge capacity data to estimate available capacity without complete charging information. Considering correlation, charging time, and initial state of charge, 36 feature combinations are available for estimation. The basic machine learning model is established on 11,500 cyclic samples, and a transfer learning model is fine-tuned and validated on multiple datasets. The validation results indicate that the best root-mean-square error for the basic model is 0.012. Furthermore, the RMSE demonstrates consistent stability across different datasets in the transfer learning model, with fluctuations within 0.5% when considering feature combinations across cycles with spacings of 5, 10, and 20. This work highlights the promise of available capacity estimation using actual, readily accessible fragmented charge capacity data.