Knowledge-Guided Data-Driven Model With Transfer Concept for Battery Calendar Ageing Trajectory Prediction

Abstract

Dear Editor, Lithium-ion (Li-ion) battery has become a promising source to supply and absorb energy/power for many energy-transportation applications. However, Li-ion battery capacity would inevitably degrade over time, making its related ageing prediction necessary. This letter presents effective battery calendar ageing trajectory prediction by deriving a knowledge-guided data-driven model with transfer concept. More specifically, this data-driven model is based on the support vector regression (SVR) technology. To ensure highly-accurate prognostics of battery calendar ageing trajectory under wit-nessed conditions, a knowledge-guided kernel is first developed by coupling the mechanism and empirical knowledge elements of battery storage temperature, state-of-charge (SoC), and time. To im-prove model's generalization ability under unwitnessed conditions, the knowledge-guided data-driven model is then equipped with trans-fer concept by adding a classical Gaussian kernel for all inputs. A well-rounded real battery ageing dataset under eight different storage conditions is collected to evaluate the performance of developed model. Results illustrate that this knowledge-guided battery ageing trajectory prediction model presents satisfactory accuracy for wit-nessed conditions with R2 over 0.98. After using only 20% starting capacity point to tune its transfer part, it can also generalize well for unwitnessed conditions with R2 over 0.97, further heavily reducing the required ageing experimental time and cost.