Dynamic Generation of Recommendations for EV Battery Health

Electric vehicles equipped with Lithium-ion batteries face performance loss due to battery ageing. This effect can be actively influenced through behaviour introduced by vehicle users. Therefore, this paper proposes a dynamic recommendation architecture to automatically generate dynamic recommendations in order to prolong battery lifetime. We propose dynamic recommendations as well as requirements for them. The recommendations suggest a certain user behaviour for a specific chronological scope in the future as well as a weight based on their impact on maintaining battery health. Furthermore, we present an exemplary architecture, based on the requirements. Using historical electric vehicle driving data, it can automatically derive dynamic recommendations.