Electric Bus Operation Research Articles

Breaking through the Key Challenges to Further Electrification of Public Transport in Beijing

In recent years, with the improvement of people's awareness of environmental protection and the increasingly serious problem of urban traffic congestion, the Beijing Municipal Government has actively promoted the development of green transportation, among which electric buses, as a low-carbon, environmentally friendly and energy-saving mode of transportation, are regarded as an important measure to improve air quality and reduce pollution emissions, which has attracted much attention. As electric buses grow, so do the operational challenges of the promoted electric buses, including high initial investment costs, inadequate charging infrastructure, technical limitations in terms of vehicle range and charging speed, and a lack of more supportive policies and regulations. This paper summarizes and analyzes the current situation of electric buses in Beijing. And proposes to solve the challenges encountered in the operation of electric buses by implementing targeted strategies. For example, optimize government financial subsidy measures and implement diversified financial support; The government supports companies to carry out technological innovation and invest in R&D to enhance electric vehicle technology; further promote the construction of charging infrastructure and improve the level of charging services; Effectively deploy battery recycling; promote the integrated development of intelligent vehicles and networking, and accelerate the construction of intelligent public transportation systems; strengthening international cooperation and resource sharing; and the development of a comprehensive policy to promote and facilitate the electrification of public transport in Beijing. Inject new impetus into Beijing's sustainable urban development.

Decarbonizing Public Transportation: A Multi-Criteria Comparative Analysis of Battery Electric Buses and Fuel Cell Electric Buses

To combat global warming, many industrialized countries have announced plans to ban vehicles powered by fossil fuel in the near future. In alignment with this global initiative, many countries across the globe are committed to decarbonizing their public transportation sector, which significantly contributes to increased greenhouse gas emissions. A promising strategy to achieve this goal is the adoption of electric buses, specifically battery electric buses and fuel cell electric buses. Each technology offers distinct advantages and drawbacks, making the decision-making process complex. This research aims to answer two critical questions: What is the optimal choice for decarbonizing the bus transportation sector—electric battery buses or fuel cell electric buses? And what are the best energy carrier pathways for charging or refueling these buses? We propose a methodological framework based on multi-criteria decision-making to address these questions comprehensively. This framework utilizes the entropy weighting and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) methodologies to rank alternative bus technologies along with energy carrier pathways. The framework evaluates a range of criteria, including economic viability, energy demand, and environmental aspects. To illustrate the framework, we considered Qatar as a case study. Our results indicate that, with respect to economic viability and energy consumption, the operation of battery electric buses is favored over fuel cell electric buses, regardless of the energy pathway utilized during both the energy production and bus operation phases. However, from an environmental perspective, operating both bus alternatives using energy from green sources provides superior performance compared to when these buses are powered by natural gas sources.

Research on charging scheduling optimization of pure electric buses considering peak-valley time-of-use electricity prices and battery loss costs

In recent years, with the accelerated progress of pure electric buses in my country, the charging scheduling and operation plan of pure electric buses urgently needs to be rationalized and scientific. This study takes the charging scheduling operation in the daily operation of pure electric buses as the starting point, and effectively describes the battery loss cost function by analyzing the optimal fluctuation range of the state of charge of the power battery of pure electric buses. Combined with the analysis of the characteristics of peak and valley time-of-use electricity prices during the charging period, the problem is refined into a pure electric bus charging scheduling decision with controllable operation completion, and the parallel machine scheduling theory is used to characterize and solve the mathematical optimization model, with the aim of minimizing the operating cost of bus companies. At the same time, an immune optimization algorithm based on random key encoding and a greedy algorithm based on the idea of avoiding peaks and filling valleys are developed to solve large-scale problems. In addition, the effectiveness of the algorithm and the applicability of the method are verified through the real bus network in Shanghai.

Analysis of green energy regeneration system for Electric Vehicles and Re estimation of carbon emissions in international trade based on evolutionary algorithms

Abstract In order to achieve maximum carbon reduction during the operation of pure electric buses, the author proposes a re estimation of carbon emissions in international trade based on evolutionary algorithm analysis of electric vehicle green energy regeneration systems. On the basis of analyzing single line scheduling, the author focuses on studying the regional scheduling mode of pure electric buses, and establishes a multi-objective optimization model for pure electric bus regional scheduling considering carbon benefits with the goals of minimizing the number of operating vehicles, minimizing the empty distance, and maximizing carbon benefits. Based on the actual operation data of pure electric buses 146 and 149 in a certain city, the author used an improved particle swarm optimization algorithm to solve the regional scheduling problem of pure electric buses according to the characteristics of the model. The results indicate that assuming other conditions remain unchanged, when the price of diesel rises to around 7.9 yuan, its operating costs will exceed those of pure electric buses, and the cost advantage of diesel vehicles will gradually decrease. Other conditions remain unchanged, and when the battery price per vehicle drops to around 300,000 yuan, the operating cost of pure electric buses will be lower than that of diesel vehicles. Conclusion: Under the premise of considering carbon benefits, adopting regional dispatch mode for pure electric buses has better economic efficiency and is more conducive to the promotion of pure electric buses.

Simulation of fuel cell membrane durability under vehicle operation

In this work, a statistical fuel cell chemo-mechanical membrane degradation model is developed based on the ionomer fibrillar morphology as a framework for use-level membrane durability prediction for fuel cell electric vehicles. The mechanical and chemical degradation modes are separately calibrated with pressure differential-accelerated mechanical stress tests and accelerated membrane durability tests, respectively. Finite element simulations are used to estimate the initial stress distribution across the membrane, while the genetic algorithm with the least squares method is employed to calibrate the model parameters with experimental results, thus reaching a good agreement. Next, the validated model is utilized for a case study of fuel cell electric transit bus operation in the city of Victoria, B.C., Canada. The initial cell voltage profile is obtained using a dynamic fuel cell power system model applied to the transit bus drive cycle recorded during real-world operation. According to the model predictions, reducing the stack nominal power from 396 to 132 kW results in a 148% membrane lifetime enhancement, whereas decreasing the cell temperature from 90 to 70°C results in an 11-fold increase in the membrane lifetime under simulated transit bus operation, thereby exceeding the 25,000-h lifetime target for this application.

Use of simulation modeling in the modernization of the Moscow district transport network for the operation of electric buses

The paper presents the development of simulation model of public transportation system of Alekseevsky district of North-Eastern administrative district of Moscow. Such model allows estimating the possibility of replacement of the existing rolling stock by electric buses. The analysis of existing transport flows is made for this purpose. Key objects, their quantitative and qualitative characteristics, features of interaction are allocated in the form of parametric models. All these are input parameters and interaction rules. The obtained results allow evaluating the options for modernization of the infrastructure necessary to ensure the operation of electric buses, and the corresponding traffic of vehicles.

Real-world Electric Bus Operation: Trend in Technology, Performance, Degradation, and Lifespan of Batteries

The working paper, identifies specific battery technologies that are preferred for powering e-buses in different geographical locations across the world and assesses their performance under different environmental conditions. The paper studies how different stress factors contribute to the ageing of batteries and their overall impact on the automotive life of e-bus batteries. It also provides insights on how battery degradation, can affect the battery and consequently the performance of an e-bus performance in the near term, and the battery life and economic viability of an e-bus in the long term. The analysis is followed by a series of recommendations to adopt best practices, improve planning, and devise policy, which will be instrumental in improving battery lifespan in countries like India, which are planning for large-scale adoption of e-buses for public transport. The working paper is supported by the Transformative Urban Mobility Initiative (TUMI), a leading global implementation initiative on sustainable mobility. The TUMI E-Bus Mission, supported by the Federal Ministry for Economic Cooperation and Development (BMZ), is working in 20 deep-dive cities to advance electric bus adoption across leading cities in the Global South. In India, the Mission is focused on Delhi, Mumbai, Bengaluru and Ahmedabad.

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.

Study on Optimization of Departure Interval of Pure Electric Bus in Airport Area

As a zero-emission, pollution-free and pure green public transport, pure electric bus has received more and more attention from society and enterprises. At present, most urban public transport lines in China have basically realized pure electric bus. With the popularization of pure electric bus, the operation and management of pure electric bus in specific areas are required. How to reasonably set the departure time interval, configure the number of vehicles and reduce the charging cost while meeting the travel needs of passengers and the enterprise cost as low as possible has become a hot issue in the research of pure electric bus operation management. In this paper, from the perspective of system cost, a pure electric bus departure interval optimization model is constructed, which considers the comprehensive cost of passengers and enterprises to be minimum. The load factor constraint and departure interval are considered as the main constraints, and the optimal departure interval is obtained.

Double-battery configuration method for electric bus operation in cold regions

Managing the energy supply of electric buses (EBs) is critical to balance operational reliability and cost reduction. However, ambient temperature significantly affects energy consumption, posing challenges in providing adequate energy supply, especially in cold regions. In this study, we propose a double-battery configuration approach for EBs operating in cold regions with substantial temperature variations between seasons. The method involves utilizing a higher-capacity battery during winter months and a lower-capacity battery for summer operations. To determine the optimal fleet size, battery capacities, and EB scheduling plans in both winter and summer seasons, we formulate an integer programming model, focusing on minimizing the fleet's average annual operating costs (OC) and carbon emissions (CE). The model is solved using the branch and bound method. A case study is conducted, examining EB routes in 11 cold cities. Results reveal that the double-battery configuration method effectively reduces the fleet's average annual OC and CE. Specifically, the reduction ratio range for OC is from 3.34% to 5.18%, while the reduction ratio for CE ranges from 4.06% to 5.16%.

Exploring trade-offs in public bus electrification under stochastic conditions

In this article, we address the question of electric bus planning and operation under stochastic travel time and energy consumption. Uncertainties in the environment may cause disruptions to the planning and operation of electric buses, and a transportation planner must anticipate such conditions and be able to respond appropriately. One of the preconditions for planning robust strategies is understanding the existence and impact of multiple trade-off scenarios, which is the basis for this study. We model the travel time delays and trip energy consumption using estimated probability density functions and use a stochastic, mixed-integer formulation with chance constraints to evaluate several trade-off scenarios for electric bus fleets under uncertainty. The results show the existence of trade-off scenarios that lead to varying degrees of impacts related to network and environment. Careful fleet planning, dispatch, and charge control enable us to make the balance between these trade-offs and achieve better operational performances under uncertainty.

Prediction of Arrival Time of Pure Electric Bus Based on FA-BP Algorithm

To establish a suitable pure electric bus arrival time prediction model, this paper takes pure electric bus as the research object. Based on the analysis of the influencing factors of the arrival time of the pure electric bus, the BP neural network arrival time prediction model optimized by the firefly algorithm (FA-BP prediction model) is established by selecting vehicle type, SOC value, battery age, and time as input conditions. The model is trained and tested by using bus operation data. The root mean square error of the Kalman filter model is 0.351, of the BP neural network model is 0.059, and of the FA-BP prediction model is 0.04. The results show that the model in this paper effectively improves the prediction accuracy and has good reliability and feasibility. It can provide some theoretical references for pure electric bus operation and managers and provide some basis for improving bus reliability.

Spatial–Temporal Scheduling of Electric Bus Fleet in Power-Transportation Coupled Network

With large passenger and battery capacity, electric buses can play dual roles – commuting tools in transportation networks and mobile energy storage units in power networks. To promote the beneficial synergy between power-transportation coupled networks, it is necessary to properly optimize the operation of electric buses in both networks. This paper proposes a spatial-temporal scheduling framework for the electric bus fleet in a power-transportation coupled network. By scheduling the plug-in locations and charging/discharging profiles of the electric buses, the model not only minimizes the total operational costs of the fleet (i.e., the electricity consumption and battery degradation costs), but also maximizes the revenue by providing flexibility to the power network. The coupled power and transportation constraints are explicitly described in the model. To address the uncertainty from fleet’s energy consumption, the chance-constrained programming is adopted that can be reformulated into second-order cones and efficiently solved by off-the-shelf solvers. Numerical experiments are conducted to validate the superiority of the proposed method based on the IEEE 33-bus distribution network and the Sioux Falls transportation network.

Modelling of Electric Bus Operation and Charging Process: Potential Contribution of Local Photovoltaic Production

The transition from diesel to electric buses allows the reduction of greenhouse gas emissions. However, the impacts of charging strategies on the quality of bus services and the utility grid must be assessed to ensure the feasibility of the energy transition in the public transportation sector. This study investigates the performances of different locations and sizes of charging infrastructures by presenting the comprehensive modelling of a bus network. It also estimates the potential benefits of a local photovoltaic (PV) production to reduce negative impacts on the utility grid. The presented approach is used for modelling one urban bus line in Compiègne, France, and simulations are performed for various case studies. The results demonstrate that the proposed method allows analysing the impact of the charging process on the quality of bus services by determining the delays of arrivals. The simulations also show the impacts of charger placement on bus on-board battery capacity, total peak power demand of battery charging, and PV self-consumption ratio. The amount of PV energy used directly to charge buses remains low, although it varies between scenarios. PV energy during winter is not sufficient to fully charge buses; however, it can be enough with additional stationary storage in the summer.

Influencing factors on electricity consumption of electric bus in real operating conditions

The research of influencing factors on the electricity consumption of electric buses Higer KLQ6125GEV3 in real working conditions is the result of experimental measurements conducted on the EKO 1 line in Belgrade over a long period which included typical seasonal periods (winter, summer, transitional) of electric bus operation as well as characteristic periods of work during the day (peak load, intermediate period, first/last departures). The results of this research are the basis for reviewing and ranking the influential factors on the electricity consumption of electric buses that are of great importance in planning operations of the line, analysis of energy efficiency and operating conditions.

Safety and Economic Evaluations of Electric Public Buses Based on Driving Behavior

The widespread adoption of electric public buses has a positive effect on energy conservation and emission reduction, which is significant for sustainable development. This study aims to assess the safety and economy of electric buses based on drivers’ behavior. To this end, based on the remotely acquired travel data of buses, the driving operation behavior is analyzed, and four safety and four economic characteristic indicators are respectively extracted via safety analysis, correlation examination, and an R2 test. Then, the extreme learning machine (ELM) is leveraged to establish the safety evaluation model, and Elman neural network is employed to construct the economic evaluation model. A comparative analysis and a comprehensive evaluation are conducted for the behaviors of ten drivers. The results highlight that the proposed evaluation model that us based on the ELM and Elman neural network algorithm can efficiently distinguish the safety and economy of driving behavior. Furthermore, a graph of driving operation behavior is constructed and the analysis results also manifest that the change of driving operation behavior of buses with higher safety and economy lead to relatively stable characteristics. When the fluctuation of vehicle speed is smooth, the driver can implement moderate driving operation in real-time. One critical conclusion that was revealed through the study is that there exists a certain correlation between driving safety and economy, and buses with higher safety tend to be more economical. This study can provide a theoretical basis for planning the maneuvering and operation of electric buses, including driving speed, braking, and acceleration operations.

UK battery electric bus operation: Examining battery degradation, carbon emissions and cost

This paper examines the impact of geographical location on the energy consumption of double deck battery electric buses operating in four UK cities. By considering seasonal variation in ambient temperature and an assumed battery degradation process, the implications on operational cost and carbon emissions were investigated. Using a MATLAB/Simulink model the total energy demand of a vehicle operating on the UKBC was simulated. Energy consumption was combined with hourly seasonal averages of carbon intensity (gCO2/kWh) and wholesale electricity costs (£) to assess charging at different times of the day and year. At the beginning of life, the range could decrease by up to 17.3% due to heating load requirements. From beginning of life to end of life, range was predicted to decrease by up to 26%. Whilst the average operational costs are relatively consistent across the four regions, the average carbon emissions can be five times higher depending on location.

Trip energy consumption estimation for electric buses

This study aims to develop a trip energy consumption (TEC) estimation model for the electric bus (EB) fleet planning, operation, and life-cycle assessment. Leveraging the vast variations of temperature in Jilin Province, China, real-world data of 31 ​EBs operating in 14 months were collected with temperatures fluctuating from −27.0 ​to 35.0 ​°C. TEC of an EB was divided into two parts, which are the energy required by the traction and battery thermal management system, and the energy required by the air conditioner (AC) system operation, respectively. The former was regressed by a logarithmic linear model with ambient temperature, curb weight, travel distance, and trip travel time as contributing factors. The optimum working temperature and regression parameters were obtained by combining Fibonacci and Weighted Least Square. The latter was estimated by the operation time of the AC system in cooling mode or heating mode. Model evaluation and sensitivity analysis were conducted. The results show that: (i) the mean absolute percentage error (MAPE) of the proposed model is 12.108%; (ii) the estimation accuracy of the model has a probability of 99.7814% meeting the requirements of EB fleet scheduling; (iii) the MAPE has a 1.746% reduction if considering passengers’ boarding and alighting.

A joint optimisation model for charger locating and electric bus charging scheduling considering opportunity fast charging and uncertainties

The continuous improvement of charging technologies makes fast charging applicable to overcoming the limited driving range of electric vehicles by providing en-route opportunity charging. Fast chargers can be deployed at bus stops to top up the battery for an electric bus during its dwelling time when passengers board and alight, which allows the usage of small size battery and avoids deadheading trips to depot or charging station for recharging. This paper examines the joint optimisation problem of locating fast chargers at selected bus stops and developing charging schedule to support electric bus operation considering time-varying electricity price and penalty cost if passengers’ extra waiting time, the delay time of starting a trip or the excessive travel time of completing a trip is caused by charging activities. To handle the uncertainties associated with the travel time and passenger demand, a robust model is proposed and formulated. The proposed methods are applied to multiple bus routes in Sydney in the numerical studies. The numerical results show that frequent charging activities occur at both intermediate and last stops during bus service trips, indicating that it is beneficial for buses to utilise passengers’ boarding and alighting time at intermediate stops and resting time at the last stop between two adjacent trips to top up battery. Sensitivity analysis on a series of system parameters, such as the acquisition cost of chargers, amortized battery price and charging power, is conducted. The numerical results verify the feasibility of deployment of fast chargers and corresponding top-up charging activities at both intermediate and last stops. Electric buses outperform the conventional diesel buses regarding energy consumption cost, especially considering the increasing diesel fuel price and its costly environmental impacts. Moreover, charging activities at intermediate stops will become more common when passenger demand (the volume of boarding and alighting passengers) increases at intermediate stops.

Data cleaning and restoring method for vehicle battery big data platform

Battery is one of the most important and costly devices in electric vehicles (EVs). Developing an efficient battery management method is of great significance to enhancing vehicle safety and economy. Recently developed big-data and cloud platform computing technologies bring a bright perspective for efficient utilization and protection of vehicle batteries. However, a reliable data transmission network and a high-quality cloud battery dataset are indispensable to enable this benefit.This paper makes the first effort to systematically solve data quality problems in cloud-based vehicle battery monitoring and management by developing a novel integrated battery data cleaning framework. In the first stage, the outlier samples are detected by analyzing the temporal features in the battery data time series. The outlier data in the dataset can be accurately detected to avoid their impacts on battery monitoring and management. Then, the abnormal samples, including the noise polluted data and missing value, are restored by a novel future fusion data restoring model. The real electric bus operation data collected by a cloud-based battery monitoring and management platform are used to verify the performance of the developed data cleaning method. More than 93.3% of outlier samples can be detected, and the data restoring error can be limited to 2.11%, which validates the effectiveness of the developed methods. The proposed data cleaning method provides an effective data quality assessment tool in cloud-based vehicle battery management, which can further boost the practical application of the vehicle big data platform and Internet of vehicle.