Online SOH Estimation of Lithium-Ion Batteries with a Sequential Gaussian Process

Lithium-ion batteries (LIBs) have been widely used in different fields as energy storage systems, such as electric vehicles and power grids. The performance of LIBs degrades with usage, which poses challenges for battery management. Thus, accurate online estimation of the state of health (SOH) is critical to ensure reliability and prolong the service time of LIBs. To achieve this, data-driven methods have become popular due to the capability of learning the mapping between SOH and measurements without prior knowledge of aging mechanisms. However, the online estimation performance of these methods cannot be guaranteed, since the models are trained offline and do not have the capability of online updating when new data are collected. In addition, the inputs for these methods are constructed with the voltage–capacity (V-Q) curve within a fixed voltage interval, which can hardly be realized in real-life applications due to the randomness of the charging or discharging process. This study proposes a Sequential Gaussian Process (Seq-GP) model-based LIB SOH estimation method, where model parameters can be updated using newly collected LIB data, such that online estimation can be fulfilled. Moreover, a novel feature extraction method is presented using random parts of the LIB V-Q curve to meet the requirements for practical applications. The proposed method is evaluated on two public battery datasets, showing competitive estimation accuracy together with online updating, uncertainty quantification, and low computational cost under the tested protocol. The results will be beneficial for online SOH estimation of LIBs in practical scenarios.