Real-world battery duty profile of a neighbourhood electric vehicle
The study of traction batteries real-world usage in vehicular applications faces a handful of serious challenges. To date, we are unable to accurately predict the cycle life of a battery under real-world operating conditions. There are a couple of reasons for that; on the one hand, the battery technology evolves rapidly whereas the battery cycle life testing requires large amounts of time and on the second hand, we know little about real-world duty profile of batteries. The work presented here intends to tackle this latter issue. In this paper, we evaluate real-world collected data of battery usage in an instrumented neighbourhood electric vehicle, using a new approach based on the concept of duty pulses. This automated method relies on the K-means clustering algorithm and aims at classifying duty pulses according to their current and energy distributions. We present the way data must be prepared and we discuss the results of the clustering. We report on the duty profiles of this vehicles battery under various driving conditions.
real-world battery duty pulse ev instrumented vehicle pattern recognition