A Battery Cloud or cloud battery management system leverages the cloud computational power and data storage to improve battery safety, performance, and economy. This work will present the Battery Cloud that collects measured battery data from electric vehicles and energy storage systems. Advanced algorithms are applied to improve battery performance. Using remote vehicle data, we train and validate an artificial neural network to estimate pack SOC during vehicle charging. The strategy is then tested on vehicles. Furthermore, high accuracy and onboard battery state of health estimation methods for electric vehicles are developed based on the differential voltage (DVA) and incremental capacity analysis (ICA). Using cycling data from battery cells at various temperatures, we extract the charging cycles and calculate the DVA and ICA curves, from which multiple features are extracted, analyzed, and eventually used to estimate the state of health. For battery safety, a data-driven thermal anomaly detection method is developed. The method can detect unforeseen anomalies such as thermal runaways at the very early stage. With the further development of the internet of things, more and more battery data will be available. Potential applications of battery cloud also include areas such as battery manufacture, recycling, and electric vehicle battery swap.

Battery Cloud是云电池管理系统，它利用云计算能力和数据存储来提高电池的安全性、性能和经济性，通过测量电动车和能量存储系统的电池数据来改进电池性能，训练和验证人工神经网络以估计车辆充电时的电池SOC，以及开发基于差分电压和增量容量分析的高精度电动车电池状态估计方法和数据驱动的热异常检测方法。

先进算法的电池云