Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

Abstract

Electric buses have many significant advantages, such as zero emissions and low noise and energy consumption, making them play an important role in saving the operation cost of bus companies and reducing urban traffic pollution emissions. Therefore, in recent years, many cities in the world dedicate to promoting the electrification of public transport vehicles. Whereas due to the limitation of on-board battery capacity, the driving range of electric buses is relatively short. The accurate estimation of energy consumption on the electric bus routes is the premise of conducting bus scheduling and optimizing the layout of charging facilities. This study collected the actual operation data of three electric bus routes in Meihekou City, China, and established the support vector machine regression (SVR) model by taking the state of charge (SOC), trip travel time, mean environment temperature and air-conditioning operation time as the independent variables; while the energy consumptions of the route operations served as the dependent variables. Furthermore, the grey wolf optimization (GWO) algorithm was adopted to select the optimal parameters of the proposed model. Finally, a support vector machine regression model based on the grey wolf optimization algorithm (GWO-SVR) is proposed. Three real bus lines were taken as examples to validate the model. The results show that the mean average percentage error is 14.47% and the mean average error is 0.7776. In addition, the estimation accuracy and training time of the proposed model are superior to the genetic algorithm-back propagation neural network model and grid-search support vector machine regression model.

Study on the Relationship between the Spatial Distribution of Shared Bicycle Travel Demand and Urban Built Environment

“…As an efficient machine learning method, SVR can take into account the complexity and learning ability of the model and minimize empirical errors. It has advantages in small-sample data and nonlinear conditions [30]. The integration (Rn) of the road network in downtown Shanghai presents the characteristics of the single-center structure.…”

“…As an efficient machine learning method, SVR can take into account the complexity and learning ability of the model and minimize empirical errors. It has advantages in small-sample data and nonlinear conditions [30].…”

A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses

“…This model was used to gain energy management insights for public transport network planning. In [20], a support vector machine regression model, optimized with the grey wolf optimization algorithm and based on data from three e-bus routes in Meihekou City, China, highlighted the importance of the state of charge, trip duration, ambient temperature, and AC operation time in accurate energy consumption estimation, with a mean average percentage error of 14.47%. The impact of ambient temperature on electric bus efficiency in colder climates was investigated in [21], where data from four battery-electric buses in Tampere, Finland, showed a 40-45% higher energy consumption in winter seasons than in summer periods.…”

Medium-Voltage DC-DC Converter Topologies for Electric Bus Fast Charging Stations: State-of-the-Art Review

“…According to the study presented in [1], more than 600,000 E-buses are deployed in Chinese cities in 2021. It is expected that the number of E-buses will further increase, Energies 2022, 15, 5487 2 of 20 reaching 1,323,490. Several Asian countries are also electrifying their public transport, such as Delhi, wherein 2019, 1000 E-buses were ordered.…”

“…To further illustrate, due to the restrictions and regulations imposed to reduce fossil fuel prices as well as gas emissions, there is a high interest in E-mobility technologies worldwide as a substitution for gasoline-powered vehicles due to their better performance in terms of reducing CO 2 emissions as well as reducing the consumption of petroleum [7][8][9][10][11][12][13][14][15][16]. Although gasoline power vehicles have been realized as a well-known technology for the past 100 years, it is expected that E-mobility technology will be further adopted to overtake the domination of conventional vehicles.…”