期刊
IET INTELLIGENT TRANSPORT SYSTEMS
卷 7, 期 1, 页码 138-150出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-its.2012.0114
关键词
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资金
- UK Technology Strategy Board (TSB) through the Knowledge Transfer Partnership (KTP) scheme
- Morgan Motor Company
- Cenex
- Zytec Automotive
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.
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