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Inductive power transfer (IPT) systems offer a promising solution to multiple major hurdles associated with electric vehicles, such as the lack of widespread charging infrastructure, driving range anxiety, and significant charging time. With the aid of IPT systems, dynamic wireless charging enables electric vehicles to be charged when running over the IPT power pads. The objective of this study is to minimise the overall cost of charging infrastructure by optimising the deployment of IPT transmitters and the battery size required to support charging for electric buses. A mixed-integer linear programming model is proposed and solved using the commercial solver Gurobi. Bus route information and the characteristics of electric buses, such as weight, drag coefficient and frontal area, are used as input to run the traffic simulation in SUMO to get time-varying velocity and acceleration data during bus operations. A bus route in Auckland, New Zealand, is used as a case study in the presented numerical experiments. A strong correlation between the velocity of the bus and the state of charge is identified. The largest cost component is the charging pad, followed by the battery. [ABSTRACT FROM AUTHOR]

Current electric bus has become an important public transport for people in China, and its interior sound quality directly affects the driver's physical and mental health for a long working time and the comprehensive performance of the vehicle. Therefore, it is an inevitable and urgent trend to research electric bus interior sound quality. Aiming at the problem of subjective uncomfortable auditory sensation caused by different speeds, the noise signals near the driver's ears were collected under two constant speed conditions. On the basis of principle analysis of feed-forward adaptive control system, the primary and secondary path transfer functions were presented using Simulink module in MATLAB to construct three active noise control models for an electric bus based on least mean square (LMS), normalized LMS (NLMS) and filtered-x LMS (FxLMS). Numerical simulation results indicate that the established control models are convergent, and four acoustic parameters are effectively reduced after controls to achieve the effect of improving the sound quality. [ABSTRACT FROM AUTHOR]

Aiming at the consensus problem of slow convergence for the active noise control (ANC) model based on standard FxLMS algorithm that leads to performance degradation, this paper takes the error signal and its variation as the inputs of fuzzy logic control, and proposes an improved FxLMS algorithm by fuzzy control mechanism with two-input-two-output TSK fuzzy rules (TSK-FxLMS); In addition, the four-channel ANC models based on standard FxLMS and TSK-FxLMS are constructed using the noise signals from four measuring points inside an electric bus under uniform and variable speed conditions, respectively. Ultimately, the offline simulation and acoustic parameter calculation results indicate that the A-weighted sound pressure level (ASPL) and loudness of the two FxLMS models within the low and middle frequencies are significantly reduced, whereas the TSK-FxLMS model has faster convergence rate, higher average reduction percentage of ASPL and loudness, which proves that the established four-channel TSK-FxLMS model has a better sound quality improvement effect than the standard FxLMS. [ABSTRACT FROM AUTHOR]

The study aimed to assess the performance of electric buses that are used in public transportation, and produced and sold in Türkiye. To achieve this, multi-criteria decision-making techniques, the Entropy and WASPAS methods, were employed. The Entropy method was used to calculate the weights of the criteria, and the WASPAS method was used to rank the performance of the electric buses. During the analysis, the most important performance criteria were identified to be maximum torque and battery capacity, while the lowest performance criterion was found to be maximum speed. According to the results, the Otokar Kent Electra was the highest performing electric bus in terms of entropy weights, and the Temsa MD9 ElectriCITY was the best performer according to equal weights. On the other hand, the Otokar Doruk Electra was found to be the worst performing bus in both weight calculations. [ABSTRACT FROM AUTHOR]

The research on vehicle sound quality is an emerging and highly promising field. This paper proposes a solution for sound quality improvement based on sensitivity analysis of psycho-acoustic parameters, in which the main acoustic techniques involved include noise signal acquisition, subjective and objective evaluations, sound quality modelling and prediction, parameter sensitivity and spectral analysis, and fuzzy comprehensive evaluation. In addition, taking 64 noise samples of electric bus as an application case, the presented sound quality analysis steps are implemented successively, and the obtained test results of improving the whole vehicle sound quality verify the feasibility and effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The orography of the terrain is a key factor for the electrification of vehicles, especially regarding public transport and electric buses. This work deals with the analysis of the use of mobile laser scanning, both terrestrial and aerial, for the evaluation of the orography of urban areas. First, the minimum point density required is evaluated to estimate the slope. The results show that point densities of 1 point/m2, measured with aerial laser scanning, are adequate for the task. Based on this, the design of a route for public transport is presented including the requirements concerning key transit points, maximum slope, and others. Based on the proposed route design, the transformation to an electrified route is analyzed from an economic and environmental point of view. The results show that the implementation of electric buses vs. diesel buses in cities with steep slopes (up to 7%) reduces greenhouse gas emissions (32.59%) as well as economic costs (18.10%). [ABSTRACT FROM AUTHOR]

Auxiliary diesel heaters are commonly used in all types of vehicles in cold climates and conditions around the world. Electric buses used in public transport utilise diesel-burning auxiliary heaters to provide thermal comfort for passengers under cold weather conditions while maintaining the operational range otherwise reduced by electric heating. However, the downside of utilising diesel burners is that they cause similar exhaust pollutants to conventional diesel vehicles. Because the emission control for auxiliary heaters is lax, the diesel burners typically lack any exhaust aftertreatment (EAT), resulting in potentially high local emissions. As the public transport sectors around the world seem to transit from traditional internal combustion engine-vehicles to battery electric applications, the significance of the emissions caused by diesel auxiliary heaters is continuously increasing. EVs are generally considered zero-emission vehicles but the implementation of diesel burners is evidently conflicting with this concept. Nevertheless, publicly available experimental results from studies around this topic are surprisingly limited. The data of the few available publications are not directly comparable because there is no direct procedure or protocol for determining the exhaust pollutants from auxiliary heaters in real-world conditions at present. As a result, assessing the direct effect of the pollutants caused by electric vehicles utilising auxiliary heaters in the public transport is challenging. This study addresses this problem by introducing two methods for measuring auxiliary heater emissions; first, a field-test method that is applicable for a quick screening of the emissions of multiple heater units; secondly, a laboratory test method for a more detailed emission characterisation in a simulated real-world operation environment. In these experiments, the primarily objective was to study the emissions of the auxiliary heaters, including CO2, CO, NOx and soot. The heater operation was found to be cyclic with numerous start-ups during its typical operation. The cyclic operation resulted in concurrent emission peaks in CO and soot. Measurements of actual operation showed auxiliary heater utilisation rates similar to the controlled measurements, although the whole temperature range of the controlled measurements was not reached in real-world conditions. The measurements conducted during the field screening revealed high variations between emissions of individual units. A further screening of auxiliary heaters would provide a better outlook for the mitigation of their emissions. [ABSTRACT FROM AUTHOR]

In recent years, fleet electrification has become a viable alternative for reducing carbon emissions through laws and government incentives around the world. Not only that, such incentives have favored the development of new technologies for the sector that have resulted in the reduction of the manufacturing cost of electric vehicles, which in turn, enabled new business models favoring the evolution of the electromobility sector. However, Brazil has also shown itself to be a market on the rise. However, electromobility in Brazil is still at an early stage of development when compared to other countries. In this sense, this paper sought to present the current state of the Brazilian sector through existing laws, regulations and incentives. In addition, it discusses and proposes alternatives for the development and maturation of the sector. Finally, technical aspects related to the standardization of communication protocols and charger installations are also presented. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article discusses the types of urban electric transport used in Belarusian cities, gives a detailed classification and comparison of available vehicles. Various options for increasing the share of environmentally friendly transport through the use of electric buses and trolleybuses are considered, an assessment and a comparative analysis of the options are given. Features of the drivers' work planning and drawing up of the schedule taking into account safety requirements are considered. A methodology for assessing the efficiency of urban passenger transport has been developed, taking into account the development of electric vehicles. [ABSTRACT FROM AUTHOR]

At present, the trend of all sectors and industries, especially the oil and gas industry, is towards reducing carbon dioxide emissions. Along with the modernization of technological processes, special attention has been paid to the reduction in greenhouse gas emissions from vehicles that run on gasoline and diesel. An effective solution in this field is the transition from vehicles with combustion engines to electric vehicles, similar to the use of the electric bus in public transportation. How to charge these electric buses is a challenge for researchers. By proposing a flash charging method for electric buses, the feasibility of using these buses is obtained. Due to the pulsating nature of the power demand in this charging method, the prevention of negative effects on the network by this type of load should be considered. These negative effects can include power quality problems, voltage drop, frequency instability, and overload of transmission lines and transformers. This paper presents a flywheel energy storage system (FESS)-based flash charging station for electric buses. The specifications of the components of this charging station are designed and sized for a case study for line 1 of Tehran Bus Rapid Transit (BRT). A charging strategy based on the proposed charging cycle is presented to minimize the high-power short-duration demand from the grid. The energy consumption of the electric bus based on the electricity consumption model is calculated. Based on theoretical calculations, for the case study, there is a need for installing 12 flash charging stations based on FESS in line 1 Tehran BRT. In this line, an electric bus with a battery capacity of 80 kWh is proposed. The power and energy capacity of these charging stations are sized to 600 kW and 3.334 kWh, respectively. Additionally, an economic comparison regarding the proposed charging station is conducted. The theoretical results of the design and sizing of the proposed charging station are validated based on simulation and experimental results for a small-scale laboratory setup. [ABSTRACT FROM AUTHOR]

Among the activities that cities are taking to reduce the emissions of harmful substances emitted by conventional engine-powered vehicles is replacing the fleet with electric vehicles in public transport that can operate in urban areas. Fleet conversion is a process in which one of the decisions that must be made is the choice of lines that should be served in the first place by electric buses. Due to the specificity of the battery charging process, the routes of lines serviced by electric vehicles should run through sections that are as little exposed to disruptions as possible, which can adversely affect the travel time of these sections. The main goal of the article was to develop a method that supports the search for bus lines that are characterized by the highest level of reliability and punctuality and therefore also by the stability of the travel time between stops. The proposed method makes it possible to indicate critical interstop sections that have the greatest impact on the sensitivity of the line to disturbances. It was assumed that the more critical sections of the route, the greater the risk of bus delay, which may be disadvantageous in the case of electric vehicles due to the need to reach the battery charging station in time. The method was implemented on the example of selected lines in the Upper Silesian region (Poland) for data covering four years (2016–2019). The proposed approach allowed for the development of the ranking of lines, corresponding to the order in which the conventional fleet should be changed to electric. [ABSTRACT FROM AUTHOR]

Urban transport in countries of East-Central Europe, including the Czech Republic, Poland and Slovakia, played an important role in the residents’ daily mobility. The underdevelopment of individual motorisation contributed to the lasting significant role of public transport. Due to the operating characteristics of electric vehicles, as well as emerging fuel crises, electric public transport played a key role, especially in large cities. Differences in the development of economies in the electrotechnical segment influenced the structure of public transport in the analysed countries. The higher level of development of the Czechoslovak transport sector contributed to a greater share of trams and trolleybuses in total transport. Despite many development plans in Poland, the process of developing traditional means of electric transport slowed down with the political change in 1989. The situation was slightly different in the Czech Republic and Slovakia, where, despite financial difficulties, the development of tramway and trolleybus networks continued. Modernisation of public transport began after joining the European Union. Technological development has led to the emergence of a new means of transport –electric buses. The apparent ease of putting them into service revolutionised the situation in Poland, thus popularising electric transport. The situation was different in the Czech Republic and Slovakia, where the main focus was on the development of tramway and trolleybus transport. The article reconstructs the development of urban electric transport after 1989 in the analysed countries and identifies factors that differentiate contemporary transport development policies in the Czech Republic, Poland and Slovakia. [ABSTRACT FROM AUTHOR]

This paper presents the problem of public transport planning in terms of the optimal use of the available fleet of vehicles and reductions in operational costs and environmental impact. The research takes into account the large fleet of vehicles of various types that are typically found in large cities, including the increasingly widely used electric buses, many depots, and numerous limitations of urban public transport. The mathematical multi-criteria mathematical model formulated in this work considers many important criteria, including technical, economic, and environmental criteria. The preliminary results of the Mixed Integer Linear Programming solver for the proposed model on both theoretical data and real data from urban public transport show the possibility of the practical application of this solver to the transport problems of medium-sized cities with up to two depots, a heterogeneous fleet of vehicles, and up to about 1500 daily timetable trips. Further research directions have been formulated with regard to larger transport systems and new dedicated heuristic algorithms. [ABSTRACT FROM AUTHOR]

Electric buses (e-buses) have been increasingly introduced to urban transit systems in recent years. E-bus scheduling has become a critical problem to ensure cost-efficient transit service. To support continuous e-bus operation, charging facilities are established at the depots to provide charging service. In real-world scenarios, charging facility capacity measured by the number of chargers is always limited. To reduce charging congestions, it is critical to consider the charging facility capacity in the e-bus scheduling problem. In this study, we addressed a multidepot e-bus scheduling problem considering limited charging facility capacity and vehicle-depot constraint. A mixed-integer programming model based on discrete-event optimization method and a large neighborhood search (LNS) heuristic are proposed to solve the problem. Comprehensive numerical experiments based on the real-world operation cases in Shenzhen were conducted. The results verify the model and show that the LNS heuristic can generate near-optimal solutions for large-scale problem instances. Lack of charging facilities at the depots can lead to more e-buses charging at the peak time regarding the time-of-use tariff, resulting in increased total operational cost. [ABSTRACT FROM AUTHOR]

Considering the battery-failure-induced catastrophic events reported frequently, the early fault warning of batteries is essential to the safety of electric vehicles (EVs). Motivated by this, a novel data-driven method for early-stage battery-fault warning is proposed in this paper by the fusion of the short-text mining and the grey correlation. In particular, the short-text mining approach is exploited to identify the fault information recorded in the maintenance and service documents and further to analyze the categories of battery faults in EVs statistically. The grey correlation algorithm is employed to build the relevance between the vehicle states and typical battery faults, which contributes to extracting the key features of corresponding failures. A key fault-prediction model of electric buses based on big data is then established on the key feature variables. Different selections of kernel functions and hyperparameters are scrutinized to optimize the performance of warning. The proposed method is validated with real-world data acquired from electric buses in operation. Results suggest that the constructed prediction model can effectively predict the faults and carry out the desired early fault warning. [ABSTRACT FROM AUTHOR]

Battery-Electric Buses (BEBs) are expensive compared to diesel buses and the biggest problem in the battery operated electric vehicle is always the range. The electric consumption is very high during extreme cold and extreme hot climates. Around the world, the developing economies are deploying BEBs on a large scale with the idea of sustainable green mobility. In this context it is required to reduce the energy consumption required for BEBs during extreme cold and hot weather. For this purpose, it is required to implement energy conservation procedures to prevent the heat entry and heat loss in the HVAC system of the BEBs. For simulating the transient effect of external solar load on the air-conditioning system of the vehicle, commercial Computational Fluid Dynamics software is used. In this project, simulation of energy consumption in a BEB is carried out using ANSYS FLUENT software. An energy conservation parametric study with different types of glass and wall materials has been carried out. There is a reduction in energy consumption of 20.52 % when using hard-coated solar glass and 26.07 % reduction when polyethylene insulation is used. [ABSTRACT FROM AUTHOR]

The technological development of buses among the new alternative concepts is evaluated in this paper. Bus transportation is an important system in the public transportation, which is cheap, flexible and, in many cases, in terms of capacity and speed. But increasing car traffic in the city centre and increasing the emission such as Carbon Dioxide (CO2) in the air are some of the dangerous problems for urban life. Therefore, it is needed the public transportation to stop increasing car traffic and needed the cleaner technology for air and environmental quality. Electric Buses (EBs) can play an important role for resident's life quality with improving the urban air quality. However, planners and managers have difficulty in decision-making due to diversified EBs together with the developing technology. Multi-criteria decision-making (MCDM) methods that are analytic decision processes, prepare a good solution for this problem. In this study, 5 EBs are assessed under the special criteria with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Optimization on the basis of the Ratio Analysis (MOORA) methods. These 2 methods are MCDM methods that are used to aim of ranking of alternatives in the complex decision problem. These methods are applied to select the best EB under the 6 criteria. Finally, E5-Bus is selected as the best option that rank of the 1st at all the 3 methods. Besides, MOORA and TOPSIS methods were compared. The results are shown alongside the best bus selection for public transportation that MOORA method is also a strong tool for solving vehicle selection problems in transportation. The proposed model has been validated using existing real applications. The proposed multi-criteria analysis can be used for advising decision-makers in their decision-making process for Electric Vehicles (EVs) in the area of clean transportation. [ABSTRACT FROM AUTHOR]

• Created a MILP model to address the electric bus en-route charging problem. • Developed robust optimization counterparts for energy consumption uncertainty. • Considered conflicting charging demands and time-of-use electricity prices. • Quantified the impact of battery type, fleet composition, and depth-of-discharge. • Provided policy insights for electric transit system planning and operating. Bus electrification, with its high energy conversion efficiency and zero tailpipe emissions, has a significant impact on addressing the petroleum crisis and lowering carbon emissions in urban transportation. However, long charging times and uncertain operational risks hinder their wider adoption. This paper explores the robust en-route charge scheduling problem for electric buses to overcome uncertain energy consumption while ensuring charging accessibility. The problem is first described as a deterministic mixed-integer linear programming model that employs a time-of-use electricity price to balance the charging demand. Additionally, a robust counterpart model is formulated to account for stochastic energy. Numerical studies are designed to evaluate the proposed model in a real network, followed by a sensitivity analysis to investigate the impact of battery type, fleet composition, and depth of discharge on the charging schedule. The results show that the robust optimization model provides system feasibility against uncertainty with a comparable price and emissions. [ABSTRACT FROM AUTHOR]

The uneven distribution of public charging infrastructure poses significant challenges for private electric vehicles (EVs). Many Chinese city bus depots have abundant chargers for electric buses (EBs), often unused during daytime operations. This study proposes an innovative mechanism to utilize bus depot chargers for private EVs, prioritizing the charging demands of EBs. Uncertainty in EB and EV charging demand, EV arrival time are considered. The objective is to maximize bus company revenue through a two-stage stochastic model. Practical applicability is demonstrated through a real-world bus depot case in Shanghai with 127 EBs and 376 private EVs. The results show that allocating 58% of the charging power and 30% of the chargers to the public from 10:00 to 24:00 meets 97.54% of the private EVs' charging demand. The bus operator faced a charging cost of 10,747 CNY, while now it expects a profit of 1733 CNY with the project running. [ABSTRACT FROM AUTHOR]

The increasing frequency of the occurrence of high impact low probability (HILP) disruptive events has posed huge threats to the power system. Therefore, power system resilience improvement has drawn world-wide attention. Electric buses (EB) are equipped with a large capacity of batteries which could provide sufficient energy and capacity values to run islanded micro-grids (MG) for hours, with a particular focus on V2G, which will deliver system resilience during extreme operational conditions. In this paper, an innovative reciprocal transport-power system coordination scheme is proposed to simultaneously mitigate the adverse impacts on both systems. By using this economic incentive-free approach, EBs are endogenously motivated to provide resilience-oriented V2G service to the power system, while increasing the transport service survivability during HILP disruptive events. Additionally, a novel EB dispatch rescheduling method considering the trade-off of both the requirement of energy supply security and transport service fulfilment is proposed. By using this method, the essential load curtailment of local MGs can be further alleviated, while the impacts of reduced charging service on transport service can also be further mitigated. Through a series of case studies, we illustrate that EBs have significant potential to provide resilience enhancement to the urban energy system based on appropriate dispatch strategies. Meanwhile, the interests of the power system and the transport system can be well reconciled by using the proposed approach during HILP disruptive events. • Present an algorithm for resilient operation of power system and transport system. • Propose a reciprocal coordination scheme reconciling the interests of both systems. • Investigate the values of strategic EB dispatch rescheduling in improving resilience. • Quantify energy values and capacity values of EBs in enhancing system resilience. [ABSTRACT FROM AUTHOR]

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. • Motor efficiency effect is introduced into the energy consumption estimation model. • A nonlinear programming for energy-saving speed profile optimization is developed. • A real bus rapid transit route is used to validate the proposed optimization method. • The optimal number of driving stages within each segment is recommended. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• A mixed-integer non-linear and nonconvex programming (MINL&NCP) model, which captures the unique feature of the EBCS problem is developed. We further approximate it by two novel MILP models in a smart way. • Non-linear charging profile and battery degradation effect are considered. • Three tailored valid inequalities are proposed to enhance the solution efficiency. • Extensive numerical experiments are carried out to seek valuable managerial insights for the public transport operators. This study deals with a fundamental electric bus charging scheduling (EBCS) problem for a single public transport route by considering the nonlinear electric bus (EB) charging profile and battery degradation effect under the partial charging policy, which allows EBs to be charged any length of time and make good use of dwell times between consecutive trips. Given a group of trip tasks for an EB fleet and charger type, the problem is to minimize the total cost for a public transport operator of providing peak-hour bus services for a focal single public transport route by simultaneously determining the EB-to-trip assignment and EB charging schedule with charger type choice subject to the necessary EB operational constraints. We first build a mixed-integer nonlinear and nonconvex programming (MINL&NCP) model for the EBCS problem. To effectively solve the MINL&NCP model to global optimality, we subsequently develop two mixed-integer linear programming (MILP) models by means of linearization and approximation techniques. To accelerate the solution efficiency, we further create three families of valid inequalities depending on the unique features of the problem. A real case study based on the No.171 bus route in Singapore is conducted to demonstrate the performance of the developed models. Extensive numerical experiments are carried out to seek valuable managerial insights for public transport operators. [ABSTRACT FROM AUTHOR]

Heat pump technology has gained surging interests due to its abilities in reducing the battery burden of the electric bus. Here, three heat pump systems for an 8 m bus are compared, including a R407C system, a basic transcritical CO 2 system (CO 2 -BASE), and a transcritical CO 2 system with an expansion-compression integrated machine (CO 2 -Advanced). The results demonstrate that the optimal ratio of the theoretical volume of the expander to the compressor in expander-compressor machine can improve the coefficient of performance (COP) of the CO 2 -BASE system in all cooling conditions, with a range of 0.165–0.190. The COP of the CO 2 -Advanced system has 13.59% improvement than that of the R407C system at most in cooling mode. In addition, the performances of the three systems run in five typical climates in China are compared. The comparison result shows that the CO 2 -BASE system is more suitable for Severe Cold Zone and Mild Zone, and the R407C system performs better in Hot summer and Warm winter Zone. However, the CO 2 -Advanced system, which has the best energy-saving effect in all climate zones in China, has a great potential to replace the existing R407C system for electric buses. • The optimal volume ratio (expander to compressor), η V , decreases as the ambient temperature increases. • The value of η V between 0.165 and 0.190 is recommended. • Basic CO2 system and R407C system perform better in heating mode and cooling mode, respectively. • The coefficient of performance in cooling mode of advanced CO 2 system is higher than that of R407C under certain conditions. • The advanced CO 2 system performs better than 407C system in China. [ABSTRACT FROM AUTHOR]

In urban corridor areas, profound congestion creates a pressing need for public transit to deliver efficient, flexible, and sustainable "door-to-door" services. This paper introduces a modular electrified transit system tailored for urban corridor areas (MET-CA), with a primary focus on enhancing transportation efficiency and reducing electrical energy consumption. The MET-CA system comprises two core components: mainline transit, dedicated to providing rapid services along the corridor, and feeder transit, catering to individual service zones. Seamless en-route transfers are facilitated through designated docking sections along the corridor. To rigorously address the system's optimization, we formulate it as a mixed-integer linear programming (MILP) problem on a time-expanded network. Subsequently, an inter-transit iterative heuristic with a problem-customized rolling method is developed within a bi-level framework to address the large-scale complexity. Its performance is validated through extensive numerical experiments, substantially compared to exact solutions obtained from the Cplex solver, showcasing an impressive average computation time savings of 49.7% with a minimal quality decrease of 0.18%. Through a real-world case study, Pareto optimality analysis underscores MET-CA's 24.4% average electricity savings and highlights its advantages in efficiency and flexibility over bus rapid transit and demand responsive transit in urban corridor areas. • This paper introduces a Modular Electrified Transit system tailored for Corridor Areas (MET-CA). • MET-CA is modeled as a mixed-integer linear programming and a bi-level heuristic is customized. • Numerical experiments and a real-world case study showcase crucial insights of MET-CA. [ABSTRACT FROM AUTHOR]

Since some fuel buses have not yet reached their retirement age, many cities still have a mix of fuel buses and electric buses (EBs) operating. This study focuses on multiple bus routes with fuel-electric mixed fleet. To address operation problems arising from separately scheduling vehicles and crews (known as the two-stage method) of each route, we propose a collaborative approach to optimize the vehicle dispatching plan and crew scheduling plan. Initially, we establish a nonlinear programming model by minimizing the overall operating costs of bus companies, encompassing vehicle purchase costs, EB charging costs, fuel bus refueling costs, and driver labor costs. Next, we devise an improved simulated annealing algorithm to solve the model. The proposed method is validated through a case study involving three real-world routes. A comparison with the traditional two-stage method reveals promising outcomes. Results indicate that the proposed method can decrease the number of vehicle changes by 21.43 %, driver labor costs by 2.04 %, and daily operating costs for the bus company by 0.82 %. • A vehicle-crew scheduling method is proposed for fuel-electric mixed bus fleet. • Optimization model is established to minimize the energy costs of multiple routes. • An improved simulated annealing algorithm is developed to solve the model. • Numerical experimentation is conducted using real bus routes. [ABSTRACT FROM AUTHOR]

This study aims to understand the difference in operational performance between electric, hybrid, and diesel buses in Portland, OR. We estimated a running time model, and a distance until a breakdown occurs model using ordinary least squares and multilevel regression frameworks. The results of the running time model suggest that electric buses are faster than diesel buses while controlling for all other operating characteristics. Whilst electric buses do break down sooner if the ramp is used more often, more stops made along the route served by the electric buses increase the distance traveled by the electric buses until a breakdown occurs. Public transport operators are encouraged to continue deploying electric buses, especially along shorter routes with numerous stops, and to undergo preventive maintenance every two weeks. It is also recommended to deploy electric buses to routes where the ramp use is known to be historically infrequent in the short term. [ABSTRACT FROM AUTHOR]

Installing dynamic wireless charging (DWC) facilities on dedicated bus lanes endows electric buses (EBs) with the ability to charge while in motion, effectively overcoming the limitations of short driving ranges. However, if the facilities are deployed along the entire EB route, it would incur significant economic costs and result in resource wastage. In this study, we divide the EB route into segments including station segments, intersection segments, and elementary segments of road section. The fluctuations in travel times of different trips on these segments are described. To optimize the DWC deployment plan, a chance constraint programming model is established with the objective of minimizing the costs of purchasing power transmitters and inverters. A combined algorithm using stochastic simulation and particle swarm optimization is designed to solve the model. A real EB route is used to conduct the case study, and results indicate that it is more suitable to deploy DWC facilities in adjacent elementary segments of road section compared to individually distributed station segments or intersection segments. Additionally, as wireless charging power or battery capacity increases, the number of required inverters and the length of power transmitters decrease, resulting in a nonlinear decrease in the deployment costs of charging facilities. • Stochastic fluctuations in travel time and energy consumption are considered. • A chance constraint programming model for DWC facility deployment is developed. • A combined algorithm using stochastic simulation and PSO is designed. • A real electric bus route is used to validate the proposed optimization method. [ABSTRACT FROM AUTHOR]

With the development of low-carbon energy, the use of thermal and hydropower units for regulation in the power system dominated by new energy (PSDNE) is decreasing. Electric buses (EBs) are expected to become the primary dispatching objects in this context. Accurately evaluation EB's response capability (RC) is vital for the scheduling operation of the PSDNE. However, current evaluation methods for EB's RC are relatively crude, lacking detailed and quantifiable measures. This paper proposes a novel multi-dimensional evaluation method to provide the aforementioned RCs. The EB operational schedule revise model considering traffic and temperature is proposed to obtain more accurate EB charging load by revise arrival charging time and remaining battery energy. The proposed EB charging model considering carbon emissions contributes to revealing the impact of various charging modes, such as low-carbon and electricity price, on the Eb's RC. The EB multi-dimensional evaluation model is established to obtain quantified and detailed RCs to support the operation of NEDPS. These capabilities include fast response within various periods, sustained response over extended durations, and remaining RC after executing scheduling instructions. Finally, using the actual data from an EB station of a city in China, the effectiveness of the proposed evaluation model is verified. [Display omitted] • The impact mechanisms of traffic and temperature on the EB's RC are revealed. • The accuracy of the revised EB's operating schedule is greatly enhanced. • The impact mechanisms of carbon emissions reduction on the EB's RC are revealed. • The EB's RC under different charging modes is quantitatively evaluated. • The fast RC, sustained RC, and remaining RC of the EB are quantitatively evaluated. [ABSTRACT FROM AUTHOR]

With the global acceleration of the carbon-neutral process and the widespread adoption of electric buses in various countries, the placement of charging stations plays a crucial role in promoting low-carbon development in public transportation systems. This paper proposes a two-stage approach for siting electric bus charging stations considering future-present needs. Initially, perspectives on future and current needs, a community discovery model is constructed using bus space-time swipe card data to identify the municipal bus travel link network. This model considers the OD distribution of bus passenger flow, which is essential for determining the line network and capacity layout. Additionally, a bus first and last station hotspot identification-Voronoi diagram model is established based on the service area partition of bus charging stations, incorporating the weight of charging demand. The model assesses the aggregation of charging demand in each service area using demand aggregation and spatial aggregation indicators. It utilizes the current bus charging requirements aggregation pattern to determine the siting of bus charging stations in the sub-regions, resulting in a data-driven two-stage method for siting electric bus charging stations considering future and current demand. The city is divided into 11 bus charging station subzones using the community discovery model, and sites are selected within each subzone. The results indicate that the demand aggregation degree has increased to some extent in 10 service areas compared to the initial sub-district and an overall increase of 22.79% for the entire city, from 0.487 to 0.598. Among them, 8 service areas have a demand aggregation degree greater than 0.5, indicating that charging demand in most sub-districts is concentrated in high-value aggregation areas. The spatial aggregation degree of all service areas is less than 1, with a decrease in the spatial aggregation degree of 7 service areas and an overall decrease of 11.47% for the entire city, from 0.680 to 0.602. Most service areas experience a decrease in spatial aggregation degree while the demand aggregation degree increases, optimizing the structure of the service areas and reducing the time cost of charging the buses. The model demonstrates good applicability to charging stations and offers an effective approach to siting them. • Use of residents' travel data to delineate initial service areas for charging stations. • Consider the longevity of charging station use in terms of the stability of the spatial distribution feature of travel OD. • Introducing demand aggregation and spatial aggregation to measure the aggregation structure of charging demand. • Site selection based on the distribution of charging demand hotspots at bus route terminals. • Considered the implementability of bus charging station deployment. [ABSTRACT FROM AUTHOR]

Electric buses are usually designed with various energy management features to overcome the limited cruising range problem. This paper reveals the impact of different energy management systems applied in the electric bus intended for rapid transit usage on battery state of charge and energy consumption. Different systems can be distinguished based on combinations of range extender usages and regenerative braking strategies. Simulations have been done using models developed from the fundamental theory of vehicle engineering and the bus's design parameters. The results show that the minimum energy consumption can be achieved by an energy management system, adopting a serial regenerative braking strategy without the use of a range extender. It is also found that the serial regenerative braking strategy can save up to 15 % energy compared to the absence of the regenerative braking system. In comparison, the absence of a range extender contributes to a 7 % reduction in energy consumption. However, considering the range anxiety of the driver, the designed electric bus for rapid transit is recommended to utilize both the range extender and parallel regenerative braking strategy. The developed model in this work can be implemented to redesign or further optimize specific components and operating procedures of the bus. [ABSTRACT FROM AUTHOR]

• This study aims to explore the factors affecting bus drivers' satisfaction with E-buses and the impact of overall satisfaction on driving behaviour. • The survey about E-bus drivers' satisfaction and driving behaviour was conducted by an online questionnaire. • Perceived usefulness, perceived ease of use, technology acceptance and environmental performance are significantly related to overall satisfaction. • The more satisfied the driver is with an E-bus, the lower the tendency and probability of aberrant driving behaviour is. The aggressive promotion of electric buses (E-buses) is receiving increasing attention as an important measure to reduce carbon emissions in the transport sector. Electric buses (E-buses) still have some defects, which may reduce driver satisfaction and affect a drivers' driving behaviour (such as frequent attention to the instrument panel given a lack of energy and overspeeding given the time pressure caused by a lack of energy). In this study, the influencing factors of drivers' overall satisfaction (perceived usefulness, perceived ease of use, environmental performance, technology acceptance and line environment) were investigated using the bus driver satisfaction questionnaire. The overall satisfaction level was measured directly by the overall satisfaction scale. Aberrant and positive driving behaviours were observed by the professional driver driving behaviour questionnaire. Taking overall satisfaction as the intermediary variable and driving behaviour (aberrant and positive driving behaviours) as the dependent variable, we established the hypothetical relationships among overall satisfaction, driving behaviour, perceived usefulness, perceived ease of use, environmental performance, technology acceptance, and line environment and constructed an E-bus driver driving behaviour structural equation model (SEM). Through an online questionnaire survey, the survey data of 313 E-bus drivers were obtained and verified for analysis. The results show that perceived usefulness, perceived ease of use, environmental performance and technology acceptance have a positive impact on overall satisfaction, and environmental performance has the greatest impact on overall satisfaction; overall satisfaction has a negative impact on aberrant driving behaviour and a positive impact on positive driving behaviour; and the line environment has a positive impact on aberrant driving behaviour. From the driver's point of view, this study plays a positive role in a more comprehensive understanding of the advantages and disadvantages, and the factors restricting the application, of E-buses. Meanwhile, it provides a strong basis for government departments, industry management departments and bus companies to formulate policies (such as E-bus driver training and management strategies). [ABSTRACT FROM AUTHOR]

• A mixed bus fleet location-routing-scheduling framework under driving range uncertainty is proposed. • A range reliability (RR)-based two-stage stochastic program is formulated. • An adaptive time-space-energy network is proposed to address diverse forms of the refueling problem. • The vehicle routing, scheduling, and refueling station location problem are solved simultaneously. • Regular services ensure to cover the scheduled trips up to a certain reliability level with minimal total cost. This paper develops a framework to address the multi-depot vehicle location-routing-scheduling problem with multi-vehicle types, including electric buses, under range uncertainty. Three major issues in bus routing and scheduling are addressed in this framework, i.e., uncertain driving range, refueling or charging need, and locating refueling or charging facilities. Mathematically, the problem is formulated as a two-stage stochastic program. Two types of services, regular services running on fixed schedules and ad hoc services to cover incomplete scheduled trips arising from energy shortage of certain buses, are considered in this paper. An adaptive time-space-energy network is developed to model the refueling issues and location problem. We introduce the notion of range reliability to decompose and solve the two-stage stochastic formulation under range uncertainty. The regular services schedule is determined in stage-one to cover the demand under a certain range reliability level. Upon realization of the random driving range, the deployment of ad hoc services is made in stage-two to address the occurrence of energy shortage of certain buses. A range reliability-based gradient algorithm is developed to minimize the expected total cost of the system. We then apply the proposed method to bus services in Hong Kong. The range reliability-based approach shows promising results, leading to substantial cost savings as compared with the traditional methods that ignore the effects of driving range uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, a simulation model of an electric bus Higer KLQ6125GEV3 is developed in IGNITE software taking into account technical-operational characteristics and drive cycle which is achieved during real operating conditions. The goal of this paper is a validation of the considered model based on the comparison of the generated power from the electric motor, which was recorded in real operating conditions, with the generated power from the electric motor obtained in the simulation program. Through multiple simulations with different parameters, an almost identical drive cycle was obtained in the simulation compared to the recorded drive cycle in real operating conditions, which further resulted in very high degree of correlation between recorded power and power obtained in the simulation. In this way, the simulation model of Higer KLQ6125GEV3 bus was validated and can be used for examination of the technical-operational characteristics of the considered bus on other lines of public transport in order to determine the convenience of its application on them. [ABSTRACT FROM AUTHOR]

In recent years, due to factors such as climate change, energy costs and exhaust emissions, the issue of whether electric buses can be an alternative to diesel buses has been come into question. In this study, first general characteristics of electric buses are examined. Based on the utilization data of an electric bus fleet in İzmir, collected over a period of 2.5 years from 20 identical vehicles with 12 m length, a comparative analysis is made with respect to alternative diesel bus performances in terms of energy and fuel consumption. In addition, detailed comparisons are made with a diesel bus on a specific line. The biggest bottleneck related with electric buses is their having relatively low ranges compared to diesel buses'. However, in the light of the data and results presented, it seems that electric buses might be considered as a serious alternative to diesel buses in the near future. [ABSTRACT FROM AUTHOR]

We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment factors such as unpredictable traffic congestion, different passenger demands, and even the changing weather can significantly affect electric bus charging efficiency and potentially hinder the further promotion of large-scale electric bus fleets. To address these issues, in this article, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines, and 5,562 bus stops. Then, we investigate the electric bus network to understand its operating and charging patterns, and further verify the necessity and feasibility of a real-time charging scheduling. With such understanding, we design busCharging, a pricing-aware real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of busCharging, we implement it with the real-world data from Shenzhen, which includes GPS data of electric buses, the metadata of all bus lines and bus stops, combined with data of 376 charging stations for electric buses. The evaluation results show that busCharging dramatically reduces the charging cost by 23.7% and 12.8% of electricity usage simultaneously. Finally, we design a scheduling-based charging station expansion strategy to verify our busCharging is also effective during the charging station expansion process. [ABSTRACT FROM AUTHOR]

• A double-battery configuration method is proposed for EBs in cold regions. • An integer programming model is established to optimize the scheduling plans. • Numerical tests are conducted by taking bus routes in 11 cold cities as examples. • Average annual operating costs and carbon emissions of the fleet can be reduced. 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%. [ABSTRACT FROM AUTHOR]

Combined decisions on equipment and charging schedule for electric buses (e-buses) are studied in this paper. The most representative day is considered, in which each e-bus fulfills a transfer task composed of a series of passenger transportation trips. Each trip starts and terminates at given times in the same single depot where e-buses re-charge their batteries. The problem is to determine battery variants for the e-buses, number of chargers of different types and daily cyclic charging schedule of e-buses such that a linear combination of (a) the total daily cost of the charging equipment, batteries wear and consumed energy, and (b) the total number of e-bus charging interruptions (switchings) is minimized. The constraints include an upper bound on the supplied electric power, restoration of the initial battery energy state before the next day transfer task for each e-bus and an upper bound on the maximal number of switchings for each e-bus in each depot visit over all e-buses and visits. A bi-level decomposition approach is proposed, in which decisions and constraints related to the detailed charging schedule and the number of switchings are moved to the lower-level problem. The lower level problem is formulated as Mixed Integer Linear Programming (MILP) problem. The upper-level problem is reduced to a MILP problem using linearization techniques. An exact decomposition scheme is developed for the upper-level MILP problem. A case study and comprehensive computer experiments are provided. • Combined decisions on equipment and charging schedule for electric buses are studied. • Daily cost of chargers, batteries wear, energy and charging switchings is minimized. • A bi-level decomposition approach is proposed. [ABSTRACT FROM AUTHOR]

This paper proposes a coordinated electric bus (EB) charging scheduling approach for hybrid charging stations that support plug-in charging and battery-swapping simultaneously. To minimize the total charging cost under time-of-use electricity prices, both a deterministic optimization (DO) model and a robust optimization (RO) model are developed to design the EBs' charging schedule. In the deterministic model, the remaining capacity upon arrival at the station is assumed to be known. However, in practice, the remaining capacity of the EB is typically uncertain due to unpredictable traffic conditions. Therefore, this paper proposes an RO model to address this uncertainty, utilizing a budgeted uncertainty set and developing the affinely adjustable robust counterpart. The duality theorem is employed to convert the RO model into a computationally tractable formulation. The proposed approach is illustrated with two numerical examples, including a random instance and a real-world case study of Nanjingnan Station in Nanjing, China. [ABSTRACT FROM AUTHOR]

To limit greenhouse gas emissions in the public transportation sector, transit authorities are constantly increasing the proportion of electric buses (EBs) in their fleets. Due to space and budget constraints, more often than not, the number of chargers that can be installed at the depots is less than the number of EBs in the fleet. Moreover, even if there is one charger for each EB, it is virtually impossible to use all the chargers at the same time due to maximum power grid constraints. Thus, there is a need to devise optimization techniques that help to recharge the buses in an efficient and acute way, taking into account these charging infrastructure capacity constraints. In this study, we consider a multi-day electric bus assignment and overnight recharge scheduling problem that can be briefly stated as follows. Given a set of vehicle blocks (sequences of timetabled bus trips performed by the same bus on a given day) to be operated over several days, a set of identical EBs, and a set of chargers available at the depot whose charging function is a piecewise linear function, the problem consists in assigning an EB to each block and to schedule the overnight recharging operations at the depot such that the EBs never run out of energy when performing their blocks and the depot charging capacity is never exceeded. The objective is twofold: minimizing the total charging costs and minimizing the impact of the charging decisions in the long-term health of the batteries. We first model this problem as a mixed integer linear program (MILP) that can be solved with a commercial solver. Then, to yield faster computational times, we develop two multi-phase matheuristics based on the MILP. To show the interest of considering a multiple-day horizon, we introduce two other matheuristics that mimic current industrial practices and solve the problem sequentially, one day at a time. To evaluate all these algorithms, we use a set of 264 instances generated from data of an EB operator. • We introduce the multi-day electric bus assignment and overnight recharge scheduling problem. • We provide a (non-trivial) mixed integer programming formulation. • We introduce two matheuristics based on mathematical programming. • We report experiments on real-world instances provided by an industrial partner. [ABSTRACT FROM AUTHOR]

• Address charging strategy selection for electric buses using a multi-criteria decision-making (MCDM) method. • Conduct comprehensive literature review and expert survey to identify economic, environmental, social, operational, and quality-of-service criteria. • Weigh criteria by fuzzy BWM and rank charging strategies by fuzzy RAFSI. • Economic aspect is the most crucial for decision-makers, followed by operational and environmental aspects. • Results show preference for overnight charging over opportunity charging. Selecting the best type of charging strategy, among a variety of options such as overnight (slow) charging and opportunity (fast) charging methods, is a crucial step in electrifying bus networks. This step has gained increased importance with the growing need for sustainable transportation solutions and the widespread adoption of electric buses (EBs) in public transport systems. This study develops a multi-criteria decision-making (MCDM) approach for selecting the best EB charging strategy, considering a comprehensive range of criteria, including economic, environmental, social, operational, and quality-of-service criteria. A thorough literature review and a survey of EB experts are conducted to identify key decision-making factors in this area. A Fuzzy Best-Worst Method (FBWM) is designed to determine the weight of criteria, and a Fuzzy Ranking of Alternatives through Functional mapping of criterion subintervals into a Single Interval (FRAFSI) method is designed to rank available charging strategies for EB systems in Munich, Germany. Results show that the most crucial consideration for decision-makers is the economic aspect, followed by operational and environmental factors. The infrastructure cost is the most crucial factor in the economic category, followed by battery cost and operational cost. The driving range is the most crucial factor in the operational category, followed by charging duration and energy monitoring. The experts' assessments indicate a preference for overnight charging over opportunity charging. The results of the FRAFSI are in line with other methods (fuzzy TOPSIS and fuzzy EDAS). Our findings offer valuable insights and guidance for transportation authorities and decision-makers in charge of selecting charging strategies for EBs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Energy regenerative shock absorber was proposed for applications in electric bus. • A slotted link mechanism was designed to convert vibrations into oneway rotations. • Theoretical model was validated through experimental tests under similar parameters. • The proposed ERSA attained the maximum and average efficiencies of 67.7% and 39.37% • Harvested energy is stored and used to power the auxiliaries of the electric bus. In recent years, using electric buses based on sustainable energy technologies has become very popular due to their advantages of protecting the environment from hazardous pollutants. However, the power supply of electric buses is the most challenging issue and hinders it from making efficient electric buses. In this study, an energy regenerative shock absorber (ERSA) was designed to be employed in the electric bus to harvest kinetic energy from rough road profiles and enhance the efficiency of electric buses. The proposed ERSA is based on a slotted link conversion mechanism to convert linear vibrations to unidirectional rotations of the generator shaft for energy harvesting and comprises four modules: the energy input model, the motion converter model, the model of a generator, and the electrical energy storage model. Firstly, the energy input model induced vibration into the proposed shock absorber; the motion converter model converts linear vibrations to unidirectional rotations using a slotted link conversion mechanism to drive the generator. Subsequently, the generator model generated electric energy stored in the power storage model. The theoretical model was developed, and simulations were performed using MATLAB software; after that, the real-scale prototype was evaluated through an experimental study using a mechanical bench test (MTS machine) under similar parameters. An average output power of 6.591 W with an efficiency of 39.37% was achieved. Moreover, the ERSA system showed a maximum efficiency of 67.7% during sinusoidal excitations of 7.5 mm and 2.5 Hz. Furthermore, the feasibility analysis was conducted to verify the suitability of the proposed system for powering the auxiliary devices in the electric bus. The suggested ERSA can reasonably power light detection, range sensors, stereo vision cameras, an inertial measurement unit, an accelerometer, and a global navigation satellite system. [ABSTRACT FROM AUTHOR]

Electric buses are growing in numbers in Sweden, which contributes to the development of a fossil fuel free society and a reduction of emissions. Earlier studies of bus systems have identified a need to further investigate societal costs, total cost of ownership, energy use on a yearly basis to account for seasonal variations, and noise during acceleration. Addressing those needs was the purpose of this study. Investigations were made in five cities in Sweden that have recently implemented different electric buses in their respective public transport system. Based on results from these investigations and earlier studies, new and developed models where designed and applied on electric buses on route 1 in Karlskrona, as a representative example. It was found that there were significant savings in societal costs and total cost of ownership when compared to diesel and biogas powered buses, mainly due to decreased noise, no emissions in the use phase, and decreased energy use. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

For a battery swapping station (BSS), the stochastic operation of electric buses (EBs) and the uncertainty of electricity prices cause unnecessary economic losses. To minimize the operating costs of the BSS, this article applies the deep reinforcement learning (DRL) and proposes a BSS model to determine the optimal real-time charge/discharge power of the charging piles. The predictability of bus operation and the uncertainty of price can be directly captured from historical data without any assumption in the model. Moreover, deep deterministic policy gradient (DDPG), as the DRL algorithm, is implemented in the model to simultaneously control multiple charging piles. Numerical results illustrate that the proposed approach can bring less operating cost than the existing benchmark control methods for a BSS while providing adequate batteries ready for swapping. [ABSTRACT FROM AUTHOR]

The paradigm shift in adopting electrified public transportation, enabled by long-range battery electric buses (BEBs) and associated charging infrastructure, inflict operational challenges to both power distribution and transit systems. This article takes an opportunistic look at the paradigm shift and develops a model for optimally scheduling the spatio-temporal charging flexibility of BEBs as a source of operational flexibility for power distribution systems. The proposed model co-optimizes the timing and location of BEBs’ charging in the transit system with the operation of the power distribution system, while respecting the operational constraints and requirements of both power distribution and public transit systems. In the proposed model, the BEBs transit system is mathematically modeled by a graph and the associated BEB charging stations are mapped into the power distribution nodes. The BEBs’ transit schedule constrains the spatio-temporal charging opportunities to all BEBs over the scheduling horizon. The proposed model is implemented on the IEEE 33-bus power distribution system coupled with the transit system of Park City, UT, USA, with BEB routes. The numerical results showcase the economic and operational benefits of the proposed model for the power distribution system operator and transit system operator. [ABSTRACT FROM AUTHOR]

Taking the independently actuated in-wheel motor driven electric bus as the research object, an integrated control strategy of yaw stability and roll stability is proposed. On the basis of the upper and lower layered structure, the stability monitoring layer is added to the control strategy, which is responsible for calculating the desired value of the vehicle's critical state and determining the vehicle stability control mode. The upper controller is the yaw moment decision layer. To improve the robustness, the sliding mode control is used to calculate the additional yaw moment required for yaw and roll stability, and the Lyapunov method is used to prove the convergence of the algorithm. The lower controller is the torque distribution layer, which uses the active-set algorithm and combines the motor torque and the braking torque of the braking system to optimally distribute the yaw moment. The proposed control strategy is verified through the co-simulation platform of Trucksim and Simulink. The results show that the control strategy can coordinate the various control modes well, improve the tracking ability of the vehicle, and reduce the risk of side slip and rollover of the vehicle. [ABSTRACT FROM AUTHOR]

This paper designs a refueling system for battery electric buses (BEBs) by applying battery-swapping technologies together with a local charging system. The service capability of battery-swapping stations (BSSs) is restricted by the number of installed swapping robots. Depleted batteries are charged at BSSs which is equipped with a local charging system having a number of standby batteries and chargers in various types. This study intends to answer four fundamental questions: How many BSSs should be installed? Where should they be? How to assign the depleted electric buses to BSSs? What is the service capability of the BSS? An integer linear program is first formulated to represent the problem with deterministic demand. A two-stage stochastic programing model is then formulated to handle the demand uncertainties, which is then solved by a customized gradient algorithm. The test on a real network of the southeast region of Melbourne in Australia verifies the feasibility of the proposed model and draws managerial insights for the location and configuration of BSSs. [ABSTRACT FROM AUTHOR]

The increasing number of electric vehicles induces a new relationship between the electric vehicles, transportation network and electric network. The deployment of the charging infrastructure is a prerequisite of the widespread of electric vehicles. Furthermore, the charging process and energy management have a significant influence on the operation of both the transportation and electric networks. Therefore, we have elaborated novel operational methods that support the deployment of charging infrastructure for electric cars and buses operating in public bus service, and the energy management. Weighted sum-models were developed to assess candidate sites for public charging stations. The mathematical model of public bus services was elaborated that supports the optimization of static charging infrastructure at bus stops and terminals without schedule adjustments. The flexibility and predictability of charging sessions were identified as the main differences between charging infrastructure deployment for cars and buses. Furthermore, the flows of energy, information and value have been revealed among the components of charging with a focus on commercial locations, which is the basis of energy flow optimization on the smart grid. [ABSTRACT FROM AUTHOR]