Energy Consumption Of Electric Buses Research Articles

Q-learning-based hyper-heuristic framework for estimating the energy consumption of electric buses for public transport

Q-learning-based hyper-heuristic framework for estimating the energy consumption of electric buses for public transport

Energy-saving speed profile planning for a connected and automated electric bus considering motor characteristic

Speed profiles play an important impact on the operational energy consumption of electric buses (EBs). In this study, an eco-driving method is proposed for a connected and automated EB serving on the bus rapid transit (BRT) route. Considering the effect of speed and road load conditions on motor efficiency in estimating energy consumption, a nonlinear programming model is proposed to optimize the energy-saving speed profile. Then the sparrow search algorithm is used for the solution to ensure that the computing time is less than the predetermined time gap. Finally, a real BRT route is taken as an example to carry out comparative and sensitivity analyses. Results indicate that: (i) the average motor efficiency can be improved by 2.11% after taking into account the effect of speed and road load conditions on motor efficiency in estimating energy consumption; (ii) the number of driving stages with uniform speed on each segment is recommended as 2 for balancing the computation time and solution quality; (iii) planning the energy-saving speed profile while regulating the signal timing at intersections improves the operational efficiency and exploit the energy-saving potential of the CAEB; (iv) as bus operation punctuality constraints become tighter, there is an increase in the operational efficiency, but a decline in the energy-saving potential of the CAEB.

A hybrid on-line approach for predicting the energy consumption of electric buses based on vehicle dynamics and system identification

Energy consumption modeling for electric vehicles is conducive to achieving eco-driving optimization and alleviating driver ‘range anxiety’. However, it remains challenging due to the complexity of influencing factors. In this study, we propose a novel hybrid cumulative energy consumption (CEC) prediction approach by a combination of vehicle dynamics and the forgetting factor recursive least squares (FFRLS) to achieve real-time online forecasting accurately. The approach is developed with real-world data collected from over 983,000 frames across six electric buses in Guangzhou, China. To enhance the accuracy and practical application of the approach, we divide the data into segments as a unit of calculation for CEC. Subsequently, we constructed the model in three working conditions based on the vehicle longitudinal dynamics, and determined the parameters to be estimated for the model. With the help of the online learning capabilities of FFRLS algorithm, the estimated parameters can be constantly adjusted and updated according to the state of the buses. The results demonstrate that the mean absolute percentage error (MAPE) of the proposed method is 7.05 %. This performance surpasses that of existing relevant studies, indicating that the model provides a more accurate method for eco-driving and city-scale electric bus operation systems.

Analysis and estimation of energy consumption of electric buses using real-world data

With the increasing popularity of electric buses (EBs), accurate estimation of the trip-level energy consumption of EBs has become increasingly essential. In this paper, an effective energy consumption estimation approach was proposed based on real-world operation data of EBs. Operation data of EBs from 3 different bus routes were collected and pre-processed to extract energy consumption-related features from various aspects such as traffic condition, environment, vehicle status and driving behaviour. The analyses of feature distribution, feature interaction and feature importance were then carried out. And the contributions of features to energy consumption were thoroughly analysed by Shapley value. Finally, different machine learning models were built and compared. The optimal results achieved an MAPE of 4.404% under 10-fold cross-validation, with an improvement of at least 29% over existing studies on the subject.

Energy Consumption of Battery- Electric Buses: Review of Influential Parameters and Modelling Approaches

Abstract The electrification of public transportation fleets worldwide can pose a challenge to multiple stakeholders, such as the fleet operator or the operator of the local electrical grid. One of the important prerequisites for the successful integration of these fleets into the existing system is the knowledge of the energy consumption of the buses during their trips. The energy consumption varies depending on multiple factors such as the vehicle or route-related parameters, operational, and environmental parameters. This paper gives an overview of the latest research regarding these influential factors. Another essential prerequisite for the implementation of intelligent management systems for electric bus fleets is the forecasting of energy consumption. Researchers take different approaches to tackle this issue. A review of the latest research considering empirical approaches, physical models, regression, and machine learning is also provided in this paper. The findings of this paper provide a quick overview of different aspects of the energy consumption of electric buses and can therefore support other researchers or decision-makers in their work.

Estimation of route speed parameters designed for electric buses

The paper presents a method for the estimation of speed parameters on urban bus routes designed for the use of electric buses. The considered bus route is divided into stopping and running sections. The bus stops are the stopping sections. The running sections connect two neighbouring bus stops. A bus equipped with the GPS receiver moves along the urban bus route at a variable speed. The GPS receiver records at a constant frequency location data that include current bus position and the measurement time. The location data enable the estimation of the time of varying speeds for the running sections and the stop time for the stopping sections. The speed parameters for the sections involve the specification of time periods assigned to the defined speed ranges. Measurement data were recorded on the selected bus route in off-pick and pick hours. The results obtained allow estimation of speed parameters for individual sections and by aggregation for the entire bus route considered. The speed parameters of the bus route correspond to the energy consumption of electric buses and can be applied to determine the properties of the urban bus routes on which electric buses are introduced.

Data-Driven Approach for Estimating Energy Consumption of Electric Buses under On-Road Operation Conditions

Data-Driven Approach for Estimating Energy Consumption of Electric Buses under On-Road Operation Conditions

Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model

The energy consumption of electric vehicles is closely related to the problems of charging station planning and vehicle route optimization. However, due to various factors, such as vehicle performance, driving habits and environmental conditions, it is difficult to estimate vehicle energy consumption accurately. In this work, a physical and data-driven fusion model was designed for electric bus energy consumption estimation. The basic energy consumption of the electric bus was modeled by a simplified physical model. The effects of rolling drag, brake consumption and air-conditioning consumption are considered in the model. Taking into account the fluctuation in energy consumption caused by multiple factors, a CatBoost decision tree model was constructed. Finally, a fusion model was built. Based on the analysis of electric bus data on the big data platform, the performance of the energy consumption model was verified. The results show that the model has high accuracy with an average relative error of 6.1%. The fusion model provides a powerful tool for the optimization of the energy consumption of electric buses, vehicle scheduling and the rational layout of charging facilities.

Research on the combinatorial optimization of EBs departure interval and vehicle configuration based on uncertain bi-level programming

ABSTRACT Based on uncertainty theory, this paper proposed a vehicle scheduling method considering the dynamic departure interval and vehicle configuration of electric buses (EBs). An uncertain bi-level programming model (UBPM) is established, which takes the total cost of passenger travel (CP) as the upper and total cost of EBs (CB) as the lower. A chance constrained programming model (CCPM) based on the randomness of passenger waiting time and the uncertainty of interstation running time is proposed as the upper model. With a certain confidence level of service level as constraints, the goal is to minimize the total cost of passenger travel. Then, an expected value model (EVM) based on the fluctuation of energy consumption is proposed as the lower model. Taking the number of EBs as the constraint condition, the goal was to minimize the energy consumption of EBs. Finally, a practical bus route is taken as an example to verify the effectiveness of the proposed method. The results demonstrated that the optimal scheduling plan considering the uncertain variables can reduce the passenger travel cost. Collaborative optimization of EBs vehicle configuration can reduce energy consumption, delay, and the number of EBs.

Data-driven estimation of energy consumption for electric bus under real-world driving conditions

Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. A K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.

Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data

With increasing adoption of electric buses, an accurate and real-time electric bus energy consumption (EBEC) prediction can significantly contribute to the battery capacity determination and dynamic scheduling management. In this study, a novel two-step EBEC prediction methodology including a stochastic random forest (RF) model, and a cooperative k nearest neighbour (kNN) RF model has been proposed to improve the prediction accuracy and achieve faster response. The stochastic RF model is developed to enhance prediction ability for the EBEC of the terminus-to-terminus journey by integrating stochastic speed profile generation for kinematic features extraction with RF algorithm for considering external factors as input parameters. Based on this, the kNN RF model is built to substitute the stochastic generation process in order to boost forecasting efficiency after a certain amount of trip accummulation. The models have been performed on real-world data collected from over 163,800 journeys over five consecutive months in Shenzhen, China. The results show that the proposed stochastic RF model has a great prediction performance with all the selected indicators, and the accuracy in MAE is raised by 9.470–51.144% compared with the existing models. In addition, the kNN RF model can save computation time by up to 97.142% with accuracy reduction of 1.795% in MAE compared with stochastic RF model. This means that the kNN searching model can be effectively used to reduce great computational cost with accumulation of trip scenarios by sacrificing very little accuracy. The developed models can be applied to the city-scale electric bus operation system, to benefit real-time scheduling and management.

Strategic Planning of Charging Stations for Electric Public Transportation Bus Systems: a Case Study

Fuel and natural gas are the main factors that drive many systems in many countries, but they have decreased because of the increased demand. However, the use of diesel fuel in public transport is one of the main reasons for the deterioration of air quality in the city. This article studies the strategic planning of charging stations for electric public transportation bus systems. Furthermore, it analyzes the investment costs of the service of electric buses compared with diesel buses by using routes in the city of Nakhon Ratchasima as a case study. The energy consumption of electric buses will also be analyzed in this article. The analysis shows that the electric bus has an energy consumption for traveling 1.169 kWh/km, and ten buses are services in one day. The electric bus charging strategy with the Plug-in charging system is the most suitable one, compared to the diesel bus system and the other charging strategies, since it has lower total investment cost of 11.37 million USD, and the battery investment is the highest one. The appropriate charging strategy depends on the route of the case study as well. If the distance is longer, the calculation of the charging station's investment, the number of buses, and the batteries may increase accordingly.

Examining influential factors on the energy consumption of electric and diesel buses: A data-driven analysis of large-scale public transit network in Beijing

Switching from diesel-powered to battery-powered buses has been a global tendency. Traditional approaches rely on standard driving cycles or fuel economy data for energy consumption on a limited number of buses; thus, expanding to large bus fleets at the city level has become challenging. This study uses high-resolution GPS and smart card transaction data to generate each bus driving profile and weight dynamics in a large-scale transit network. Two vehicle activity-based energy consumption models are adopted and calibrated for diesel bus (DB) and electric bus by using the field data of 630 bus routes in Beijing. The average energy consumptions of DBs and electric buses are 43.5 and 14.1 L/100 km, respectively. A gradient boosting regression tree algorithm is presented to examine and rank distinct influential factors on the energy consumption of DBs and electric buses. Heterogenous behaviors are identified: the leading attributes affecting the energy consumption of electric buses and DBs are route characteristics and operational condition, respectively. After computing, the total energy conservation of electrifying all bus fleets is equivalent to 0.87% of daily electricity demand in Beijing. These findings set a good base for further studies on bus fleet replacement, charging infrastructure deployment, and electrified route prioritization.

Study on saving energy for electric auxiliary systems of electric bus

ABSTRACT Since Electric Auxiliary Systems (EAS) accounts for a large proportion of electric bus energy consumption, in order to reduce EAS energy consumption and improve bus driving range, the working principles of EAS have been studied. The number of passengers, bus interior and exterior temperature can have great influence on EAS energy consumption, it is important to dynamically manage EAS working status according to the changes of bus driving conditions for saving energy, then an integrated control method for EAS is proposed for better meeting the real demands of EAS. The proposed method takes EAS as a whole and can manage EAS working status mainly according to the changes of bus passengers. For verifying the effectiveness of the proposed method, an electric bus model including EAS has been built and simulated by co-simulation in different platforms. Compared with common control method, the experimental simulation results shows that EAS energy consumption can be effectively reduced by dynamically managing EAS working status according to the changes of bus driving conditions, energy consumption proportion of each subsystem in EAS also has been discussed. The proposed method gives new way for saving EAS energy and has great application value for electric buses.

Construction of Analytical Models for Driving Energy Consumption of Electric Buses through Machine Learning

In recent years, the Taiwan government has been calling for the use of public transportation and has been popularizing pollution-reducing green vehicles. Passenger transport operators are being encouraged to replace traditional buses with electric buses, to increase their use in urban transportation. Reduced energy consumption and operating costs are important operational benefits for passenger transport operators, and driving behavior has a significant impact on fuel consumption. Although many literatures or real-world systems have addressed the issues related to reducing energy consumption with electric buses, these works do not involve the records collected from an on-vehicle battery management system (BMS). Accordingly, the results of analyses of existing works lack in-depth discussions, and therefore the applicability of existing works is insignificant. Therefore, in this study, driving data were collected using a battery management system (BMS), and vehicular power consumption was classified according to energy efficiency. Then, decision trees and random forest were applied to construct energy consumption analytical models. Finally, the driving behaviors that influence energy consumption were investigated. A case study was conducted in which a Taichung passenger transport operator’s electric bus driving data on urban routes were collected to construct energy consumption analytical models. The data consisted of two parts, i.e., vehicle records and route records. On the basis of these records, we considered the practicability and applicability of the analytical models by transforming the unstructured records into raw data. Passenger transport operators and drivers can leverage the obtained eco-driving indicators for different bus routes for energy savings and carbon reduction.

Novel Electric Bus Energy Consumption Model Based on Probabilistic Synthetic Speed Profile Integrated With HVAC

This paper proposes a novel and generic model to calculate the Electric Bus Energy Consumption (EBEC) without the need for a high–resolution speed profile data. The proposed model generates a set of speed profiles using the basic information of the bus trip: trip time, trip length, and distances between successive bus stops. The generated speed profiles could accurately reflect the various traffic conditions and speed behaviors of real–world situations. Roadway Level of Service (LoS) is incorporated in the proposed model to simulate different traffic conditions. Further, a stochastic model for the bus speed profile is adopted to simulate the probability of the bus to stop at each on–route designated stop. The generated speed profiles are then inputted to an accurate EBEC model that considers the route topography, auxiliary loads (lighting, sound, and radio systems) and the impact of the weather conditions. The operation of the heat, ventilation and air conditioning system (HVAC) is also incorporated in the model using the thermal mass balance principle. Using the proposed model, the characteristics of EBEC on a given route can be evaluated through generating a set of speed profiles for the studied route. The proposed model provides transit network planners with a useful tool to appropriately design electric–based transit networks when there is a lack or unavailability of real–time and high resolution data.

Testing innovations for increased energy efficiency of electric buses: evidence from the EBSF_2 project

EBSF_2 is a three-year research project which came to an end in April 2018. The project was led by the International Association of Public Transport and funded by the European Union with the aim of increasing attractiveness and efficiency of urban bus systems. Real-life demonstrations in 12 cities addressed diverse areas for innovations, but for several test sites the main goal was to deploy and test solutions for increased energy efficiency of urban bus fleets. Optimize the energy management, lower the energy needed for on-board auxiliaries, including heating, ventilation and air conditioning systems, and thus minimizing the impact on the total energy consumption of electric buses and their operation, were the tasks to be accomplished.The paper describes how the topic energy efficiency was addressed within the EBSF_2 demonstrators. The assessment methodology designed to measure the performance variations in each demonstrator and cross-case is presented, together with the testing scenarios and the main results achieved in four demonstrators impacting the energy efficiency domain.

Reducing the Energy Consumption of Electric Buses With Design Choices and Predictive Driving

Transportation electrification is increasing and recently more focus has been directed on heavy vehicles and especially on city buses. Battery electric buses are inherently more energy efficient than diesel buses and the efficiency can be further increased by different methods. This paper evaluates the energy consumption reductions that are achievable with an aluminum chassis, low-drag body, low-rolling-resistance class C tires, heat pump, and predictive driving. A simulation model of a generic electric bus was developed in the Simulink software. Simulations were carried out on various types of driving cycles in cold (−10 °C) and warm conditions (20 °C). A novel nonlinear model predictive control problem formulation was created for minimizing the energy consumption of an electric bus. Using a heat pump instead of an electric heater provided the highest energy savings in the cold conditions with an average consumption reduction of 12.7%. The results indicated that a heat pump is particularly effective on low-speed bus routes. However, the class C tires and aluminum chassis provided higher energy savings than the heat pump in the warm conditions. The low-rolling-resistance tires achieved the most robust energy savings. The aluminum chassis reduced the energy consumption more than the class C tires, but the benefit of the lighter chassis was shown to also correlate strongly with the aggressiveness of the driving. The results showed that a low-drag body is a potential method for consumption reduction on high-speed bus routes. Predictive driving was found to reduce the average consumption by 9.5% at −10 °C when using 10-second prediction and control horizons.

Energy Uncertainty Analysis of Electric Buses

Uncertainty in operation factors, such as the weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an Internet of Things (IoT) system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/km between trips. Furthermore, consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by the ambient temperature, driving behavior, and route characteristics. The internal resistance varied mainly as a result of changes in the battery temperature and charging current. The energy consumption was predicted with above 75% accuracy with a linear model. The operation factors were correlated and a novel second-order normalization method was introduced to improve the interpretation of the results. The presented models and analyses can be integrated to powertrain and charging system design, as well as schedule planning.

English

English