TBSI Sunwoda Battery Dataset

AI for science has generated a great deal of enthusiasm from both academia and industry. The field of battery energy storage is no exception due to its cross-cutting properties of materials, chemistry, physics and electrical engineering. Due to the complexity and uncertainty of the manufacturing process, there persistently exists a considerable mismatch in performance between a manufactured battery and its counterpart from material laboratory, leading to compromised product quality, R&D efficiency, investment cost and lifetime sustainability. Sunwoda Electronic Co., Ltd, generates the TBSI Sunwoda Battery Dataset to verify the performance of novel battery material composition designs. The collaboration team at Tsinghua Berkeley Shenzhen Institute (TBSI) performs the main research work by providing an efficient and reliable early battery prototype verification methodology. We open-source this dataset to inspire more diversified data-driven, physics-informed battery management research and real-world applications, including, but not limited to, state of charge (SOC) estimation, state of health (SOH) estimation, remaining useful life (RUL) prediction, degradation trajectory prediction, consistency management, and thermal management.