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

Abstract

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.

Sensitivity Analysis in the Life-Cycle Assessment of Electric vs. Combustion Engine Cars under Approximate Real-World Conditions

“…Lowest bus el. consumption with 0.561 kWh/km taken from Gao et al (2018) [99], highest bus el. consumption with 1.75 kWh/km taken from Zhou et al (2016) [100].…”

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

“…Regarding the charging scheduling of large-scale EBs fleets: Rinaldi et al [13] used a mixed integer linear programming to minimize the charging costs for a bus fleet of up to 8 buses. Gao et al [14] developed a consumption prediction model and an optimization charging model of large-scale EBs fleet. They used genetic algorithms to minimize the EBs operating cost in four different bus lines in China.…”

“…A centralized charging scheduling is operated by a central controller whereas decentralized charging scheduling is managed by individual users that optimize their own charging profiles. Some works have focused on the centralized overnight charging scheduling of EBs fleets [13][14][15][16][17][18][19][20][21]. Other works were dedicated to the optimization of charging infrastructure [22,23].…”

“…The present paper differs from the referred ones [13][14][15][16][17][18][19][20][21][22][23][24][25] as it investigates more precisely the battery electro-thermal and aging behavior in order to minimize the battery aging cost. This work differs also from our previous results [26,27] that dealt with the reduction of the charging cost applied to a multi-objective small-scale EBs fleet and a mono-objective large-scale EBs fleet.…”

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

“…Minimizing costs of EB BSSs has also been considered by some authors. In [92], the operational costs of an EB BSS were minimized. For this, the EB load was forecasted, based on a fuzzy evaluation and through actual survey data.…”