No abstract

Accurate state of health (SOH) estimation plays a significant role in battery management systems (BMS). This paper investigates a Polak-Ribière-Polyak conjugate gradient (PRPCG) algorithm optimized broad learning system (BLS) for lithium-ion battery (LIB) SOH estimation. First, effective health indicators (HIs) are extracted from the voltage curve in the constant current charge process. Second, a hybrid four layers BLS structure with mapped feature nodes and enhancement nodes connecting to the output is established to build both the linear and nonlinear relationships between the HIs and SOH, in which only the output weights require to be trained. Again, the PRPCG algorithm is adopted for searching optimal output weights without matrix inverse calculation during the training process. Furthermore, certain Gaussian noises are added to enhance the training data for solving the locally low accuracy problem. Finally, under the Oxford battery degradation data set, experiments validate the investigated algorithm has high accuracy in SOH estimation with the mean absolute error (MAE) below 1%. The enhanced data can efficiently improve the model generalization ability.