A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs

The widespread adoption of battery electric vehicles (BEVs) has highlighted the critical importance of precise energy consumption prediction models to address the problem of range anxiety among drivers. This study aims to enhance the accuracy of such models by combining real-time traffic state recognition and velocity prediction, thereby mitigating range anxiety and enhancing the driving experience. Consequently, we propose an improved Fuzzy C-Means (FCM) clustering algorithm that use historical traffic data and dynamic traffic information accurately identify traffic conditions. In addition, a Fuzzy-Markov-based velocity prediction model is developed to generate future velocity profiles under diverse traffic scenarios. In the energy consumption prediction stage, a particle swarm optimization-radial basis function neural network (PSO-RBFNN) model is employed to estimation the energy consumption. Simulation results show a significant improvement in prediction accuracy, with the Mean Absolute Percentage Error (MAPE) reduced to below 3.2% under diverse traffic scenarios.