Method for estimating the energy consumption of electric vehicles and plug-in hybrid electric vehicles under real-world driving conditions

This study presents a novel framework by which the energy consumption of an electric vehicle (EV) or the zero-emissions range of a plug-in hybrid electric vehicle (PHEV) may be predicted over a route. The proposed energy prediction framework employs a neural network and may be used either `off-line' for better estimating the real-world range of the vehicle or `on-line' integrated within the vehicle's energy management control system. The authors propose that this approach provides a more robust representation of the energy consumption of the target EVs compared to standard legislative test procedures. This is particularly pertinent for vehicle fleet operators that may use EVs within a specific environment, such as inner-city public transport or the use of urban delivery vehicles. Experimental results highlight variations in EV range in the order of 50% when different levels of traffic congestion and road type are included in the analysis. The ability to estimate the energy requirements of the vehicle over a given route is also a pre-requisite for using an efficient charge blended control strategy within a PHEV. Experimental results show an accuracy within 20-30% when comparing predicted and measured energy consumptions for over 800 different real-world EV journeys.