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
energy management systems , zero‐emissions range , traffic congestion , charge blended control strategy , energy consumption , hybrid electric vehicles , energy prediction framework , vehicle fleet operators , neurocontrollers , PHEV , inner‐city public transport , standard legislative test procedures , neural network , real‐world driving conditions , energy management control system , road type , urban delivery vehicles , power control , energy requirements , plug‐in hybrid electric vehicles
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