Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization

To enhance the model estimation performance and minimize the possibility of overfitting, we propose a statistical optimization strategy of healthy features (HFs) for estimating the state-of-health (SOH) of lithium-ion batteries (LIBs). Firstly, a series of initial HFs were extracted from the voltage, current, temperature, incremental capacity (IC) curves, and differential thermal voltammetry (DTV) curves according to the battery characteristics. Secondly, the six statistical features (i.e., mean, median, lower quartile, range, upper quartile, and standard deviation) of the initial HFs for each charge cycle were calculated. Thirdly, to minimize the impact of redundant features and noise, the optimal HF set was identified by a comparative analysis among different combinations of the six statistical features. Thus, the battery SOH can be estimated using the dual-kernel Gaussian process regression (GPR), which improves the estimation accuracy and generalization ability of the single-kernel GPR. To prevent estimation errors due to manual adjustments, the GPR hyperparameters were optimized via the northern goshawk optimization (NGO) algorithm. Experiments were conducted on the NASA and Oxford datasets, with most estimation errors below 1%. Experimental results demonstrate the lightweight NGO-Dualkernel-GPR model, by incorporating the statistical feature optimization strategy, achieves exceptional SOH estimation performance for different LIBs.