A Novel Method of Developing Driving Cycle for Electric Vehicles to Evaluate the Private Driving Habits

As one of important implications on electric vehicles, driving cycles are recognized as essential components for evaluating the comprehensive performances and they have drawn much attention research. Currently, constructed specifiedly in international standards based local traffic conditions. However, without consideration private habits, unproper lead to imprecision predicting remaining useful life or estimating states. Herein, a novel methodology Markov chain Monte Carlo method is developed extract personal characteristics elements divided kinematic fragments. Principal component analysis adopted address high-dimensional parameter vector, cluster used classify The statistics demonstrates that processed database exhibits great consistency with our cycle compared against original database, where temperature, state-of-charge utilized describe patterns. Moreover, by using operational data, comparable other cycles, which good performance. Overall, presented vehicle can be considered an effective way battery states related applications. may promoted future better energy management vehicles owing promotion connected autonomous vehicles.