As the worldwide environmental crisis worsens, electric vehicles (EVs) are establishing themselves as ecofriendly alternatives to conventional fossil fuel vehicles. Lithium-ion batteries (LIBs) are a typical source of energy for EVs, but it is important to predict their life in order to ensure safe and optimal operation. However, because LIBs degrade in a nonlinear fashion and their state of health varies depending on operating conditions, achieving fast and accurate cycle life prediction has been a challenge. More importantly, on-board estimation is necessary because even the identical battery cells manufactured by the same company vary in their cycle lifetimes and operational characteristics, which we cannot specify in advance. In this paper, we propose a set of novel features that enable on-board battery cycle life prediction while maintaining high memory efficiency and low calculation complexity. The features’ performances were evaluated using a variety of machine learning models, ranging from simple linear elastic nets to nonlinear neural networks.