An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

Yajing Gao, Shixiao Guo, Jiafeng Ren, Zheng Zhao, Ali Ehsan and Yanan Zheng
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Yajing Gao: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
Shixiao Guo: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
Jiafeng Ren: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
Zheng Zhao: Department of Automation, North China Electric Power University, Baoding 071003, China
Ali Ehsan: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Yanan Zheng: Energy Research Institute, Academy of Macroeconomic Research, NDRC, Beijing 100038, China

Energies, 2018, vol. 11, issue 8, 1-17

Abstract: With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

Keywords: similar day selection; the grey relational analysis; multi external factors; wavelet neural network; modeling of power consumption; optimal charging scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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