Multi-Scale Co-Estimation of SOC and SOH Based on Cloud Transmission Protocol

The fast growth of electric vehicles has resulted in the widespread application of lithium-ion batteries. Recognized as a critical problem, the accurate estimation of the battery state has drawn much attention. Meanwhile, the continuous progress of the Internet of vehicles technology promotes the algorithm on cloud platform. However, the state of charge and state of health estimation based on on-board battery management system present deficiency such as data loss, noise interference and inconsistent sampling interval through the transmission. Thus, this article developed a multi-scale co-estimation method on the SOC and SOH with consideration of the dataset quality. Firstly, a Thevenin model for SOC estimation is constructed and parameters are identified by least square method. It is noteworthy that the frequency of SOC and SOH updates different time scales. To achieve the co-estimation on the both state and health, the extended Kalman filter algorithm is used twice. The dual extended Kalman filter is applied to operate at different time scales depending on the frequency of state and health updates. Finally, the impact of data loss, repetition and time scale is discussed where the dataset is incomplete through transfer protocol. Simulation confirms that this algorithm can still achieve maximum deviation of 5% for SOC estimation under sampling frequency, and the error can be limited within 6% for capacity estimation. The article provides an SOC and SOH estimation technique for the cloud platform.