On motion resistance estimation and modeling for heterogeneous road vehicles

Licentiate thesis, 2023

Climate change is driving the development of CO2 reducing technologies within the transportation industry. One of the most promising technologies is battery electric vehicles. However, the combination of limited battery capacity, relatively long charging times and few charging stations makes them more vulnerable to conditions when energy consumption is higher than usual compared to vehicles driven by fossil fuel. This thesis focuses on vehicle and environment attributes that create energy-consuming forces resisting the vehiclemotion, i.e. the motion resistance and how to model and estimate them.
The method developed in the thesis is based on a separation principle where attributes affecting the motion resistance are separated into vehicle, road and weather characteristics. This enables using vehicle data from heterogeneous vehicles to estimate local road weather conditions. The method is validated using simulations and real vehicle experiments.
The results show that the road and weather conditions can be estimated using data from connected vehicles and energy consumption of heavy-duty vehicle combinations is largely affected by crosswinds. Furthermore, the motion resistance from crosswinds can be characterized by simple models with only a few tuning parameters.
The main conclusions from this work are that road weather conditions including crosswinds need to be accounted for in range estimation algorithms, road weather estimates based on connected vehicle data is a promising technique, and windy days need to be anticipated in advance to avoid potential charging chaos.