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.科学智能（AI for Science）已在学术界与工业界引发广泛关注与热忱。电池储能领域亦不例外，因其兼具材料学、化学、物理学与电气工程学的交叉学科属性。由于电池制造工艺兼具复杂性与不确定性，量产电池与材料实验室制备的原型电池之间始终存在显著的性能偏差，进而导致产品质量、研发效率、投入成本与寿命可持续性均受到损害。欣旺达电子股份有限公司（Sunwoda Electronic Co., Ltd）构建并发布了TBSI欣旺达电池数据集，用于验证新型电池材料组分设计的性能。清华伯克利深圳学院（Tsinghua Berkeley Shenzhen Institute, TBSI）的合作团队通过提供高效可靠的早期电池原型验证方法，承担了本次主要研究工作。我们将该数据集开源，以期推动更多样化的数据驱动与物理信息融合的电池管理研究及实际应用，包括但不限于荷电状态（state of charge, SOC）估计、健康状态（state of health, SOH）估计、剩余使用寿命（remaining useful life, RUL）预测、退化轨迹预测、一致性管理以及热管理。