A comprehensive methodology for developing and evaluating driving cycles for electric vehicles using real-world data

Abstract

This study presents a comprehensive methodology for developing optimized driving cycles for electric vehicles (EVs) based on real-world driving data from specific regions. This study encompasses the entire process, including route selection, speed data acquisition, data processing, and driving cycle generation, and utilizes simulation techniques to evaluate the generated driving cycles. In the data processing stage, realistic driving cycles were created by combining the micro-trip analysis, K-means clustering, Markov chain, and transition probability matrix methods. The generated driving cycles were validated through speed–acceleration frequency distribution analysis, confirming their accurate reflection of real-world driving data. Furthermore, simulations using MATLAB Simulink demonstrated that the generated driving cycles represented real driving environments more accurately than standard driving cycles, improving the precision of energy consumption predictions.