Small sample state of health estimation based on weighted Gaussian process regression

-Battery state of health (SOH) estimation is essential for the safety and reliability of electric vehicles. Data-driven approaches are compelling in SOH estimation as they work effectively without human intervention and have excellent nonlinear approximation capabilities. Most studies assume that the training data is sufficient. However, in practical applications, data acquisition is often expensive and time-consuming. A novel weighted Gaussian process regression SOH estimation method is proposed to reduce the model's dependence on data through knowledge transfer. The squared exponential covariance function is introduced with a penalty mechanism to control the cross-battery knowledge transfer process. Experiments are carried out with battery cyclic aging data under different working conditions. Experimental results show that the proposed weighted Gaussian process SOH estimation model can obtain reliable prediction results, although the training data only accounts for 20% of the total dataset.

Automated summary

This study proposes a novel weighted Gaussian process regression method for SOH estimation, which reduces the model's dependence on data through knowledge transfer. Experimental results show that the proposed method achieves reliable prediction results.