Development of a predictive system for car fuel consumption using an artificial neural network |
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Authors: | Jian-Da Wu Jun-Ching Liu |
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Affiliation: | 1. Department of Automation, Tsinghua University, Beijing 100084, China;2. Bourns College of Engineering – Center for Environmental Research & Technology (CE-CERT), University of California, Riverside, 92507 CA, USA;1. School of Engineering Science, University of Science and Technology of China, Hefei 230026, PR China;2. Department of Civil, Architectural and Environmental Engineering, University of TEXAS at Austin, TX, USA;3. Beijing Key Lab of MFPTS, China Academy of Safety Science and Technology, Beijing 100012, PR China |
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Abstract: | A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors which will effect the fuel consumption of a car in a practical drive procedure, however, in the present system the impact factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In the fuel consumption forecasting, to verify the effect of the proposed predictive system, an artificial neural network with back-propagation neural network has a learning capability for car fuel consumption prediction. The prediction results demonstrated that the proposed system using neural network is effective and the performance is satisfactory in fuel consumption prediction. |
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