Remaining Driving Range Estimation Framework for Electric Vehicles in Platooning Applications
- Resource Type
- Conference
- Authors
- Lamantia, Maxavier; Su, Zifei; Chen, Pingen
- Source
- 2021 American Control Conference (ACC) American Control Conference (ACC), 2021. :424-429 May, 2021
- Subject
- Aerospace
Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Road transportation
Uncertainty
Drag
Simulation
Estimation
Aerodynamics
Electric vehicles
- Language
- ISSN
- 2378-5861
Range anxiety has been one of the largest issues for battery electric vehicle (BEV) adoption. Accurate estimation of EV remaining driving range (RDR) can alleviate this issue by informing drivers of appropriate timings and energy needed to propel their EVs to meet their needs, particularly during a long trip. Vehicle platooning with a short inter-vehicle distance has demonstrated significant energy saving benefits on high-way operation by reducing aerodynamic drag force. On the other hands, vehicle platooning also introduces uncertainties to aerodynamic drag coefficient estimation and model-based RDR estimation algorithms. Therefore, it is a challenging task to precisely estimate the RDR for EVs in vehicle platoon. This work proposes a novel EV RDR estimation method that integrates the real-time estimation of aerodynamic drag coefficient and the operation mode from advanced driver-assistance systems (ADAS) or platooning into a physics-based EV model to improve RDR estimation accuracy in highway operation. Simulation results demonstrate that the proposed estimation method can potentially reduce EV RDR estimation errors when compared to the baseline EV RDR estimation method which is based on historical data.