Convolutional Neural Network-Bagged Decision Tree: A hybrid approach to reduce electric vehicle's driver's range anxiety by estimating energy consumption in real-time

• URL: http://arxiv.org/abs/2008.13559v1
• Date: Mon, 31 Aug 2020 12:45:15 GMT
• Title: Convolutional Neural Network-Bagged Decision Tree: A hybrid approach to reduce electric vehicle's driver's range anxiety by estimating energy consumption in real-time
• Authors: Shatrughan Modi, Jhilik Bhattacharya, Prasenjit Basak
• Abstract summary: A hybrid CNN-BDT approach has been developed, in which Convolutional Neural Network (CNN) is used to provide an energy consumption estimate. Bagged Decision Tree (BDT) is used to fine tune the estimate. Compared results with existing techniques show that the developed approach provides better estimates with least mean absolute energy deviation of 0.14.
• Score: 9.475039534437332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To overcome range anxiety problem of Electric Vehicles (EVs), an accurate real-time energy consumption estimation is necessary, which can be used to provide the EV's driver with information about the remaining range in real-time. A hybrid CNN-BDT approach has been developed, in which Convolutional Neural Network (CNN) is used to provide an energy consumption estimate considering the effect of temperature, wind speed, battery's SOC, auxiliary loads, road elevation, vehicle speed and acceleration. Further, Bagged Decision Tree (BDT) is used to fine tune the estimate. Unlike existing techniques, the proposed approach doesn't require internal vehicle parameters from manufacturer and can easily learn complex patterns even from noisy data. Comparison results with existing techniques show that the developed approach provides better estimates with least mean absolute energy deviation of 0.14.

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