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An essential part of electrifying bus networks is deciding on the appropriate charging strategy among the many available alternatives, such as opportunistic (rapid) charging techniques and overnight (slow) charging. The broad usage of electric buses in public transportation networks and the increasing demand for environmentally friendly transportation options have elevated the significance of this step. This research establishes a multi-criteria decision-making (MCDM) fusion method for choosing the optimal electric bus charging strategy by considering various variables, including operational, quality-of-service, social, environmental, and economic factors. To determine what matters most for making decisions in this field, we surveyed electric bus specialists and reviewed the literature extensively. We used the TOPSIS method as an MCDM fusion method to combine the criteria and alternatives. We compute the weights of criteria by the average method. Then, the TOPSIS fusion method selects and ranks the alternatives. We collected the 20 criteria and five alternatives in this study. We show that overnight is the best charging strategy in the electric bus system. We performed a sensitivity analysis to show the different cases in criteria weights, then ranked the alternatives under different weights to establish the stability of the results. [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]

Battery states are very important for the safe and reliable use of new energy vehicles. The estimation of power battery states has become a research hotspot in the development of electric buses and transportation safety management. This paper summarizes the basic workflow of battery states estimation tasks, compares, and analyzes the advantages and disadvantages of three types of data sources for battery states estimation, summarizes the characteristics and research progress of the three main models used for estimating power battery states such as machine learning models, deep learning models, and hybrid models, and prospects the development trend of estimation methods. It can be concluded that there are many data sources used for battery states estimation, and the onboard sensor data under natural driving conditions has the characteristics of objectivity and authenticity, making it the main data source for accurate power battery states estimation; Artificial neural network promotes the rapid development of deep learning methods, and deep learning models are increasingly applied in power battery states estimation, demonstrating advantages in accuracy and robustness; Hybrid models estimate the states of power batteries more accurately and reliably by comprehensively utilizing the characteristics of different types of models, which is an important development trend of battery states estimation methods. Higher accuracy, real-time performance, and robustness are the development goals of power battery states estimation methods. [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]

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]

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]

• 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]

In the Greater Houston Area, mobile sources contribute to the highest share of NOx emissions and the second-highest share of VOC emissions. The Houston METRO system that operates public buses is a key element of Houston's infrastructures that could reduce the emissions of criteria air pollutants (CAPs) and greenhouse gases (GHGs), thus improving the regional air quality. We used life-cycle assessment (LCA) and life-cycle cost analysis (LCCA) to evaluate the environmental sustainability of electric buses and compared it to diesel buses. The LCA simulations demonstrate that life-cycle emissions of GHGs, VOCs, NOx, and black carbon associated with electric buses are lower than conventional diesel and diesel hybrid buses. These lower emissions are mainly attributed to the fact that natural gas currently makes up about 50% of the fuel used to generate electricity in Texas. However, other emissions such as PM10, PM2.5, SOx, N2O, and primary organic carbon are higher and would lead to the higher environmental cost of electric buses than diesel buses. The environmental cost analysis estimates that the annual cost savings of electric buses in 2040 would significantly support the long-term goal of environmental sustainability in the Greater Houston area. [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]

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]

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]

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]

• Solved the multiple depot bus scheduling problem with multiple vehicle types. • Formulated the problem as an integer linear program to achieve global optimality. • Developed the time-space-energy network to address the range and refueling issues. • The locations of the refueling stations are optimized simultaneously. • Developed a simplified formulation for solving larger-scale problems. This paper develops a formulation for the multiple depot (MD) vehicle scheduling problem with multiple vehicle types (MVT), including electric buses (EBs), under range and refueling constraints. A novel approach is developed to generate the feasible time-space-energy (TSE) network for bus flow and time-space (TS) network for passenger flow, where the range and refueling issues can be precisely addressed. We then introduce the external cost associated with emissions, and investigate the minimum total system cost to operators and passengers by scheduling the bus fleet and locating the refueling stations. The problem is formulated as an integer linear program (ILP) to find the global optimal solution. For computational efficiency, we develop a simplified formulation based on the TS bus flow network to handle larger-scale problems for approximate solutions. We apply the methods to bus services in Hong Kong to analyze the bus fleet size needed, operational cost, passenger cost, and emissions generated by buses with multiple energy sources. Through the formulation, we study the implications of government subsidy for EBs, bus scheduling in Low Emission Zone (LEZ), and safe driving ratio design for bus refueling. [ABSTRACT FROM AUTHOR]

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Earlier studies have mainly focused on technology, economy, and advantages of electric buses, and they have largely shown that electric buses could be one of the solutions for sustainable public transport. Despite this, the present procurement process for public transport in Sweden is not suitable for including support systems for electric buses. This study was aimed to find a stakeholder collaboration model that would allow electric bus systems to be more effectively included in the procurement process for public transport. The results were achieved by several multi-stakeholder collaboration seminars and meetings that included representatives from regional public transport authorities, bus operators, bus manufacturers, energy companies, municipalities, and experts involved in bus transport. The study primarily developed two stakeholder collaboration models, suggesting that charging infrastructure should be designed separately from the common procurement process. In these models, energy companies, electric grid owners, charging infrastructure operators, regional public transport authorities, and municipalities need to collaborate. The first model is designed for a system that includes chargers at certain locations along a route and/or stakeholders with a low level of experience of electric bus systems, while the second is designed for a system that includes bus charging at the depot and/or stakeholders with a high level of experience of electric bus systems. [ABSTRACT FROM AUTHOR]

The public transport market in most global cities is dominated by diesel buses, which can have significant adverse impacts on the environment and human health. To reach carbon neutrality, many public transport operators are attempting to replace their existing bus fleets with electric buses (EBs). This paper develops a generic bus replacement model to help public transit (PT) operators progressively renew their existing bus fleets so as to reach particular electrification goals within a given planning horizon. Unlike existing studies, our model more comprehensively considers the external costs, which include external climate costs, external health costs and used battery recycling costs/profits. Furthermore, by incorporating different incentives, variants on the model can be adopted to investigate how different government incentives can affect the replacement behaviors of PT operators. Finally, our proposed approach is applied to real-world data from a bus operator in Singapore which is aiming to achieve a 100% cleaner energy bus fleet within 20 years. The experiments indicate that an EB subsidy can effectively accelerate the electrification process, and a 25% EB subsidy could achieve full electrification eight years earlier than if no EB subsidy were applied. Moreover, we explore how the diesel tax and diesel bus (DB) tax affect EB replacement. Our experiments suggest a 10% diesel tax rate can achieve good results but imposing a DB tax will not encourage PT operators to purchase EBs earlier. • A novel model for the bus fleet replacement problem is proposed. • Various external costs that borne by the society and environment are considered. • Our approach evaluates different policy incentives. • Our real case study provides managerial insights for PT operators and policymakers. [ABSTRACT FROM AUTHOR]

The use of electric buses (EBs) can help reduce vehicle emissions and improve our environment. However, it brings new challenges in transit operations due to the limited driving range of EBs. This study proposes a new mathematical programming model for optimizing the single-line EB vehicle scheduling by considering the use of a skip-stop operation strategy. The objective of the model is to minimize both operator cost and passenger cost. A genetic algorithm is proposed to solve the model and generate the quasi-optimal results, including the required fleet size, vehicle trip chains, charging decisions, and stopping and stop-skipping plans. A case study of a real-world bus line in the city of Dandong, China, is conducted. The experimental results show that compared to using a full-stop strategy, the use of a skip-stop strategy can reduce the total system cost by 15.09% and improve the average energy utilization rate by 9.02%. [ABSTRACT FROM AUTHOR]

In this paper, a modeling framework to optimize electric bus recharging schedules is developed, which determines both the planning and operational decisions while minimizing total annual costs. The model is demonstrated using a real-world transit network based in Davis, California. The results showed that range anxiety can be eliminated by adopting certain recharging strategies. Sensitivity analyses revealed that the model could provide transit agencies with comprehensive guidance on the utilization of electric buses and development of a fast charging system. The comparative analyses showed that it was more economical and environmentally friendly to utilize electric buses than diesel buses. [ABSTRACT FROM AUTHOR]

Currently, there is serious political support for the decarbonization of transport locally, nationally and even internationally. Public transport operators are focusing on the use of electric buses as an opportunity to reduce greenhouse gas emissions and improve air quality. However, using electric buses requires a functional infrastructure of urban charging points. Fast-charging can be made available thanks to the progress made on the major technological charging devices in recent years. In this study, we consider an optimization problem of the design of an infrastructure for a fast-charge city electric bus service. The decisions which have to be made include determining a mixed fleet of conventional and electric buses, points for electric chargers and power stations, quantities of charging plug devices, a distribution of electric buses between the routes, and matching chargers with power stations. The objective is to maximize the route-weighted total passenger capacity of electric buses. An Integer Linear Programming model has been developed to complement the existing non-linear model. The new model is efficient if the number of possible charging spots is small, which is natural and frequent in practice. Extensive computer experiments demonstrate that our approach delivers near-optimal solutions of the studied problem in ten minutes for real-world instances on a standard PC and it outperforms the earlier approach on every instance. • A real-life problem of designing fast-charge urban electric bus services is considered. • The goal is to maximize the route-weighted total passenger capacity of electric buses. • A complex and powerful ILP model and ILP based heuristics have been developed. • Extensive computer experiments are reported for real-world instances. • The approaches and algorithms developed have proved their performances. [ABSTRACT FROM AUTHOR]

• Electric buses are employed for the restoration of distribution systems. • A distributed restoration framework for distribution systems is proposed. • A novel electric bus scheduling model incorporating passenger demand is built. • An improved ADMM is used to solve the restoration problem efficiently. Electric buses can enhance the restoration of distribution systems. However, existing studies have focused on centralized restoration between the electric bus and the distribution system, ignoring the subjectivity and privacy of the electric bus company. This paper proposes a distributed restoration framework for distribution systems incorporating electric buses to address the problem. Firstly, a mixed-integer linear scheduling model of electric buses is constructed considering the bus service requirements, space–time characteristics, and energy characteristics. Secondly, a bi-level distributed restoration framework is proposed. The upper level aims to decide the energy restoration tasks of the electric bus company, and the lower level introduces a control center, using a sequential iterative approach to determine each electric bus's energy and bus service tasks. Thirdly, the distributed restoration model is developed using the augmented Lagrangian method and solved via the improved alternating direction multiplier method (ADMM). The improved ADMM can adaptively change the penalty factor to speed up the convergence. Finally, numerical simulations are performed by the IEEE 33-node distribution system. The results show that the participation of electric buses can enhance the restoration of distribution systems and the proposed distributed restoration framework has good convergence and high optimization efficiency. [ABSTRACT FROM AUTHOR]

Switching diesel buses (DBs) to electric buses (EBs) has been a global trend to reduce the use of fossil fuels and improve air quality. However, buses electrification may lead to additional vehicle weight, which may emit more non-exhaust particulate matter (PM) emissions. It remains debatable whether buses' electrification will successfully improve air quality as excepted. To assess the effect of the buses' electrification on the levels of PM emissions, PM emission factors (EFs) were evaluated from EBs and equivalent DBs. In addition, the total mass of PM emissions from EBs and equivalent DBs in 2021 was calculated in Xi'an using the real-world number and mileage of EBs. The non-exhaust PM EFs from EBs were larger than total exhaust and non-exhaust PM EFs from DBs, indicating that the electrification of buses would cause an increase in the level of PM emissions. The total annual mass of PM emissions from EBs was apparently higher than that from DBs. Moreover, a sensitivity analysis showed that tire wear, brake wear, and road wear PM emissions were more reliant on vehicle mileage, whereas resuspension of road dust was more dependent on vehicle weight. This finding can serve as a guideline for policymakers to design mitigation strategies for reducing extra PM emissions due to the electrification of buses by reasonably reducing vehicle weight and annual mileage. [Display omitted] • Real-world bus weight and mileage data in 2021 from Xi'an was counted. • PM emission factors from EBs and their corresponding DBs were calculated. • Total mass of PM emissions in 2021 from EBs and corresponding DBs were evaluated. • A sensitivity analysis between the bus weight/mileage and PM mass was assessed. • PM EFs from EBs were higher than total DBs' exhaust and non-exhaust PM EFs. [ABSTRACT FROM AUTHOR]

Widespread use of electric vehicles (EVs) and electric buses (EBs) adds new challenges to short-notice evacuations. Studies modeling EV behavior suggest that the power grid in most cases cannot handle EV demand during the evacuation preparation stage, but ignore the potential efficiencies of coordinated EBs. To overcome this shortcoming, a generalized framework based on three multi-trip EB routing models is developed to a) estimate the feasibility of using EBs during short-notice evacuations by examining evacuation efficiency, b) analyze EB charging behavior considering evacuation efficiency and path assignment equity, and c) explore the economic advantages of using EBs by comparing EBs with conventional liquefied natural gas (LNG) buses in terms of efficiency and operational cost. A genetic algorithm with an improved recombination strategy (GA-IR) is developed to solve these models. Computational results show that GA-IR outperforms GA in terms of convergence speed and solution quality, as GA-IR takes into account the empty trip removal process and the charging station re-insertion process, which is an innovation of GA-IR. Sensitivity analysis shows that using k-means clustering to position charging stations, and increasing the number of charging stations and EBs can improve evacuation efficiency. [ABSTRACT FROM AUTHOR]

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. • En-route top-up charging schedule at selected bus stops for electric buses. • Optimal deployment of fast chargers at selected bus stops for electric buses. • System cost minimisation via charger location, battery size and charging schedule. • Uncertain passenger demand and travel time between stops during bus operation. • Sensitivity analysis of various factors on the charging locations and schedule. [ABSTRACT FROM AUTHOR]

[Display omitted] • Develop an optimal deployment strategy of PV and batteries to power electric buses. • Propose a GA-ILP approach for high-dimensional and nonlinear optimization problems. • Adopt 3D-GIS and DL integrated approach to accurately characterize solar potential. • Verify and analyze strategy performance by comparing to vast alternative designs. Using rooftop solar photovoltaics (PV) and batteries together to power electric buses is considered a novel and feasible approach to reducing carbon emissions and tackling street-level air pollution in high-density cities like Hong Kong. However, associated optimal deployment is highly challenging due to the involved high-dimensional system planning, non-linear component sizing, and complex solar potential accurate characterization. To address this challenge, this study proposed a strategy to minimize the payback period of the deployed rooftop PV and batteries for achieving net-zero energy of electric bus transportation by taking associated grid impacts into consideration. In this strategy, a genetic algorithm-integer linear programming (GA-ILP) approach was developed to solve the high-dimensional rooftop PV planning problem and the non-linear PV and battery sizing problem. Our 3D-GIS (geographic information system) and DL (deep learning) integrated approach was used to attain accurate spatiotemporal rooftop solar power generation profiles. To verify the proposed strategy, a case study covering 5 bus terminuses with 28 bus routes and a total of 1,224 building rooftops surrounding the terminuses was conducted in a real region of Hong Kong. The effectiveness of the developed strategy was verified by comparing the optimized deployment with 150 million Monte-Carlo-generated solutions. The optimized deployment achieved the shortest payback period of 3.98 years by effectively tackling design issues such as battery oversizing, PV misallocation, and battery misallocation. To meet the increasing requirement on peak export power reduction (representing the grid impact constraint in the study), the proposed strategy can find a cost-effective balance between shifting PVs from higher-density solar potential terminuses to lower ones and increasing battery capacity. The proposed strategy can be adopted in practice to facilitate the development of cost-effective and grid-friendly community-solar-powered electric bus networks in high-density cities. [ABSTRACT FROM AUTHOR]

The transportation sector accounts for a significant share of greenhouse gas emissions. Hence, the electrification of this sector is a crucial contributor to the mitigation of global warming. Recent studies suggest that electric vehicles will be economically paired with internal combustion engine vehicles in the near future. However, relying on private vehicle decarbonization only cannot deliver comprehensive space management efficiency solutions in urban environments. Therefore, it is essential to invest in the technological development and deployment of electric buses for public transportation, directly enhancing the quality of life in large cities. From this perspective, this review examines a wide range of scientific literature on electric bus research using science mapping methods and content analysis to support critical thinking unveiling the main research streams, methods, and gaps of the field. The analysis indicates that future research on electric buses will be mainly devoted to sustainability (encompassing economic, environmental and quality of service dimensions), energy management strategies, and fleet operation. • Electric bus research is assessed using science mapping and content analysis. • The main electric bus research streams are recognized. • Content analysis of relevant articles is carried out to assess the methods used. • Future research is mainly aimed at energy management strategy and fleet operation. • Recent research gaps are identified. [ABSTRACT FROM AUTHOR]

• Energy consumption of eight car and bus technologies are modelled (450 simulations) • Powertrains include conventional, hybrid, electric, and fuel cell electric. • Car-share, ride-share, private, and bus service modes WTW trip-emissions are estimated. • Local operating contexts sway the environmental benefit of a mobility solution. • Vehicle occupancy critically impacts passenger-trip-level environmental benefit. • Occupancy thresholds for technology/service mode are defined. This paper estimates the environmental impact of alternative and conventional transportation technologies across the dimensions of service mode and power source pathway. We simulate the Well-to-Wheel energy consumption and GHG emissions of eight transit buses and passenger car powertrains. Vehicles are simulated under three generalized North American operating contexts (450 operating scenarios) using Autonomie and the GREET database. All technologies are normalized by passenger-service-mode-trip-km-travelled GHG emissions to facilitate equivalent comparison. The results indicate that all simulated mobility solutions carry a large variability; however, the most environmentally competitive solutions are fuel cell electric car-share, battery electric car-share, and battery-electric bus, all powered by low-carbon intensity power sources at average occupancy (0.23–19.7 g CO 2 e passenger-service-mode-trip-km-travelled-1). Furthermore, transit bus technologies have the potential to reduce up to 2.3 times more GHG per passenger-trip than comparable ride-share technologies. Overall, this paper defines occupancy GHG emission thresholds for mobility solutions to inform environmental decision-making processes. [ABSTRACT FROM AUTHOR]

This paper shows the results of an in-depth techno-economic analysis of the public transport sector in a small to midsize city and its surrounding area. Public battery-electric and hydrogen fuel cell buses are comparatively evaluated by means of a total cost of ownership (TCO) model building on historical data and a projection of market prices. Additionally, a structural analysis of the public transport system of a specific city is performed, assessing best fitting bus lines for the use of electric or hydrogen busses, which is supported by a brief acceptance evaluation of the local citizens. The TCO results for electric buses show a strong cost decrease until the year 2030, reaching 23.5% lower TCOs compared to the conventional diesel bus. The optimal electric bus charging system will be the opportunity (pantograph) charging infrastructure. However, the opportunity charging method is applicable under the assumption that several buses share the same station and there is a "hotspot" where as many as possible bus lines converge. In the case of electric buses for the year 2020, the parameter which influenced the most on the TCO was the battery cost, opposite to the year 2030 in where the bus body cost and fuel cost parameters are the ones that dominate the TCO, due to the learning rate of the batteries. For H2 buses, finding a hotspot is not crucial because they have a similar range to the diesel ones as well as a similar refueling time. H2 buses until 2030 still have 15.4% higher TCO than the diesel bus system. Considering the benefits of a hypothetical scaling-up effect of hydrogen infrastructures in the region, the hydrogen cost could drop to 5 €/kg. In this case, the overall TCO of the hydrogen solution would drop to a slightly lower TCO than the diesel solution in 2030. Therefore, hydrogen buses can be competitive in small to midsize cities, even with limited routes. For hydrogen buses, the bus body and fuel cost make up a large part of the TCO. Reducing the fuel cost will be an important aspect to reduce the total TCO of the hydrogen bus. [ABSTRACT FROM AUTHOR]

The adoption of electric buses in public transport requires careful planning for the bus fleet and charging infrastructure. A mathematical model of an urban bus service was developed to support the deployment of charging infrastructure. The novelty of the model is that it incorporates infrastructure elements for both static and dynamic charging technologies at the same time. The model supports the electrification of the bus lines without route and schedule adjustments. The volume of charged energy at charging units is considered as a variable in the objective function to determine the location of charging units at minimum cost. The model was verified by a case study based on actual bus service data. It was found that the use of static chargers is more favorable if the cost of a static charging unit is less than the cost of a dynamic charger with a length of 1600 m and the charging power of static chargers is three times greater than the charging power of dynamic chargers. The relationship between charging power and the length of the dynamic charging unit was analyzed. It was noted that the use of charging power higher than 162.5 kW at dynamic charging units is not necessary. [ABSTRACT FROM AUTHOR]

The reduction of pollutant emissions in the field of transportation can be achieved by developing and implementing electric propulsion technologies across a wider range of transportation types. This solution is seen as the only one that can offer, in areas of urban agglomeration, a reduction of the emissions caused by the urban transport to zero, as well as an increase in the degree of the health of the citizens. This paper presents an analysis of the direct and indirect environmental aspects of a fleet of real electric buses under service in the city of Cluj-Napoca, Romania. The solution of using 41 electric buses to replace Euro-3 diesel buses (with high pollution levels) in the city's transport system eliminates a local amount of 668.45 tons of CO2 and 6.41 tons of NOx—pollutant emissions directly associated with harmful effects on human health—annually. [ABSTRACT FROM AUTHOR]

• Adopted bus operation in bi-level optimisation to understand bilateral interactions. • Distribution Locational Marginal Pricing is applied to guide charging scheduling. • Restricted flexibility of e-bus leads to 8% energy demand change in the demand response. • The impacts of declining battery cost on grid-interactive operation are quantified. Electric Vehicles are expected to play a vital role in the transition of smart energy systems. Lots of recent research has explored numerous underlying mechanisms to achieve the synergetic interactions in the electricity balancing process. In this paper, the grid-interactive operation of electric buses is first time integrated within a dynamic market frame using the Distribution Locational Marginal Price algorithm for load congestion management. Since the defined problem correlates the opportunity charging flexibility with the bus mobility over a network, the tempo-spatial distribution of energy needs can be reflected in the dynamic of service planning. The interactions between bus operators and suppliers are quantitatively modelled by a bi-level optimisation process to represent the electric bus service planning and electricity market clearing separately. The effectiveness of the proposed load management has been demonstrated using data collected from an integrated real-world bus network. Experiments show that engagement of electric bus charging load in demand response is helpful to alleviate the network congestion and to reduce the power loss by 7.2% in the distribution network. However, alleviated charging loads have exhibited counter-intuitive capability for load shifting. The restricted electric bus operational requirements leads to a 8.17% loss of charging demand, while the reliance on large batteries has increased by 10.57%. However, the sensitivity analysis also shows that as the battery cost declines, the such discourage implications on grid-interactive electric bus operation will decrease once the battery cost below 190/kWh. The optimal grid-ebus integration have to consider the trade-off between range add-up, reduced battery cost and additional benefits. [ABSTRACT FROM AUTHOR]