Data-Driven Nonparametric Li-Ion Battery Ageing Model Aiming At Learning From Real Operation Data: Holistic Validation With Ev Driving Profiles

Conventional Li-ion battery ageing models require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. Furthermore, there is still an uncertainty on the validity of purely laboratory data-based ageing models for the accurate ageing prediction of battery systems deployed in field. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of in-field battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing, reduce the development cost of ageing models and at the same time ensure the validity of the model for prediction under real operating conditions. In this paper, a holistic data-driven ageing model developed under the Gaussian Process framework is validated with experimental battery ageing data. Both calendar and cycle ageing are considered, to predict the capacity loss within real EV driving scenarios. The model can learn from the driving data progressively observed, improving continuously its performances and providing more accurate and confident predictions.