Pulse Voltage Response Generation: TBSI-Lijing Battery Dataset

In the retired batteries sustainable utilization scenario, preprocessing steps such as capacity grading and consistency matching is essential to determine how a battery should be reused or recycled. The conventional approach of measuring battery state of health (SOH), charge-discharge profiles, and other related properties through long-time charge-discharge cycle is both time-consuming and energy-intensive. Therefore, developing rapid, non-invasive, and sustainable preprocessing methods for randomly retired batteries is crucial. However, actual measured data in this field are very limited, both in terms of the quantity of retired batteries and the diversity of battery chemistries and materials. To address this gap, we open-source this Pulse Voltage Response Generation: TBSI-Lijing Battery Dataset to foster further academic research and industrial applications in the field of battery SOH fast estimation and consistency fast assessment. Xiamen Lijing New Energy Technology Co., Ltd., collected this dataset. The collaboration team at Tsinghua Berkeley Shenzhen Institute (TBSI) processed this dataset and utilized generative models for data augmentation, significantly enhancing the economic feasibility of large-scale battery repurposing.