Range Estimation of Battery Electric Buses Using Hybrid Modeling

This paper presents a hybrid modeling methodology that integrates a Neural Network with a physics-based vehicle model, for estimating the range of battery electric buses. The neural network component is implemented to model the regenerative braking in the vehicle, as an accurate analytical approach is challenging due to limited knowledge of the electric drive system control, the Battery Management System and scarcity of experimental data. The proposed method leverages the strengths of hybrid modeling and allows for the Neural Network component being trained on a very small dataset and focusing on improving the regenerative breaking prediction. Comparing the predicted energy regeneration against experimental data, results show that the hybrid model outperforms the Physics Based Model in predicting the battery negative current. Specifically, the negative current errors were reduced significantly in 8 out of 9 simulated velocity profiles. Copyright (c) 2024 The Authors.