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Analysis of the Statistical Energy Consumption and Its Application to an Economic Evaluation of Plug-In Hybrid Electric Vehicles
Technical Paper
2019-01-0933
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The energy consumption depends not only on the structures of vehicles but also on their operating conditions. For vehicles with the same structure, the operating conditions will vary from driver to driver. In this paper, considering the difference of operating conditions, the concept of statistical energy consumption is proposed to reveal the statistical law of actual vehicle energy consumption. In this paper, a plug-in hybrid electric vehicle (PHEV) is taken as the research object. Based on the distribution law of three vehicle use factors, i.e. vehicle mass, daily driving distance and driving aggression, Monte Carlo method is used to simulate and calculate the statistical energy consumption and statistical comprehensive energy consumption. Then, the energy consumption values that only considered the daily driving distance is calculated. Comparing with the methods adopted by the energy consumption regulations, the results show that the energy consumption value calculated by the average charging mileage weighting coefficient method and the utilization factor method are lower than the actual value. But the latter is equal to the statistical energy consumption considering only the daily driving distance. And it is closer to the real average energy consumption value when considering the three factors. The average charging mileage coefficient factor method used by Economic Commission of Europe (ECE) has the lowest energy consumption value. Statistical energy consumption can reflect the distribution and statistical characteristics of actual energy consumption considering the operating conditions of the vehicle.
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Authors
Citation
Zhong, Z., Bao, Y., Hu, C., and Shao, Z., "Analysis of the Statistical Energy Consumption and Its Application to an Economic Evaluation of Plug-In Hybrid Electric Vehicles," SAE Technical Paper 2019-01-0933, 2019, https://doi.org/10.4271/2019-01-0933.Also In
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