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.
Method for estimating the energy consumption of electric vehicles and plug-in hybrid electric vehicles under real-world driving conditions
IET Intelligent Transport Systems ; 7 , 1 ; 138-150
2013-03-01
13 pages
Article (Journal)
Electronic Resource
English
neural network , energy consumption , energy prediction framework , road type , standard legislative test procedures , energy management control system , energy requirements , real-world driving conditions , zero-emissions range , energy management systems , neurocontrollers , charge blended control strategy , PHEV , power control , urban delivery vehicles , vehicle fleet operators , plug-in hybrid electric vehicles , inner-city public transport , hybrid electric vehicles , traffic congestion
Metadata by IET is licensed under CC BY 3.0
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