Advanced Range Estimation for Electric Busses with Physics Informed Machine Learning

Given the growing focus on environmentally sustainable practices and the desire for cost effective solutions, electric buses have caught the eye of many public transportation companies. To make electric buses an ideal addition to a fleet, they must complete required routes in all conditions, making accurate range finding of these buses an invaluable tool. A current approach for range estimation is to develop energy-based models of components and integrate them in a larger model that predicts the overall battery power draw, estimating the remaining range available. Such an analytical model is limited by the variety of extraneous variables affecting the system (traffic, temperature, passenger count), individual components which are difficult to model accurately, as well as finite access to required data and parameters for calibration and verification. In this context, the proposed research aims to improve the state of the art of range estimation for electric vehicles by combining data driven machine learning techniques with physics-based analysis (PBA). This combined model is applied to a case study of the regenerative braking in electric buses. First, a feed forward neural network model was trained to estimate regenerative braking based on available experimental data, then this network was integrated into a physics-based bus model. This implementation was then used to assess the capabilities of the combined model to account for various lapses in data quality, and how the overall accuracy can be improved from using a strictly analytical model. The combined model resulted in a clear improvement of the regenerative braking modeling, and therefore an improvement in the analytical modeling of the electric bus.No embargoAcademic Major: Mechanical Engineerin