SOH estimation of lithium-ion batteries based on least squares support vector machine error compensation model

Accurate estimation of the state of health (SOH) of lithium-ion batteries is an important determinant of their safe and stable operation. In this paper, a method for the SOH estimation of lithium-ion batteries based on the least squares support vector machine error compensation model (LSSVM-ECM) is proposed. This method achieves a combination of an empirical degradation model and a data-driven method. Battery degradation can be divided into overall trends and local differences, where the former can be described by an empirical degradation model (EDM) established by the historical data of the battery capacity, while the latter can be mapped by a least squares support vector machine (LSSVM). An LSSVM-ECM is established, where the input is the time interval of the equal charging voltage rising (DV_DT) and the output is the fitting error of the EDM, which represents the local difference of the capacity degradation to dynamically compensate the prediction results of the EDM that represents the global trend in terms of the capacity degradation. Validations are carried out with battery data provided by Oxford and NASA datasets. Results show that the proposed method has a high prediction accuracy and a strong robustness.