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In this paper, we describe a comparative analysis of a bus route scheduling problem as part of timetable trips. We consider the current uptake of electric buses as a viable public transportation option that will eventually phase out the diesel-engine-based buses. We note that, with the increasing number of electric buses, the complexity related to the scheduling also increases, especially stemming from the charging requirement and the dedicated infrastructure behind it. The aim of our comparative study is to highlight the brevity with which a multi-agent-system-based scheduling method can be helpful as compared to the classical mixed-integer linear-programming-based approach. The multi-agent approach we design is centralized with asymmetric communication between the master agent, the bus agent, and the depot agent, which makes it possible to solve the multi-depot scheduling problem in almost real time as opposed to the classical optimizer, which sees a multi-depot problem as a combinatorial heuristic NP-hard problem, which, for large system cases, can be computationally inefficient to solve. We test the efficacy of the multi-agent algorithm and also compare the same with the MILP objective designed in harmony with the multi-agent system. We test the comparisons first on a small network and then extend the scheduling application to real data extracted from the public transport of the Maltese Islands. [ABSTRACT FROM AUTHOR]

Public transport is essential to provide urban mobility, and cities need to offer a fast, comfortable, secure, and low-pollutant public transport mode to attract passengers and reduce private car use. Despite metros, trains, and light rail systems being desirable, the cost of implementation and low flexibility make bus services the best option for many cities. One solution for improving bus service systems is the implementation of bus rapid transit systems that are composed of buses running in segregated lines with pre-payment fares and level boarding platforms in stations. However, the main challenge of bus systems is the use of engines fueled by diesel, which is extremely polluted. One possible solution is the use of battery buses; but are they really feasible regarding daily operation cost? In this paper, we investigate the adoption of battery buses in comparison to diesel engines in a BRT system using municipal data from São Paulo, Brazil, as a reference. We collected operational data from the Sao Paulo city government transport agency and data from the literature to produce an analytic hierarchy process (AHP) model, which allowed us to compare both systems. The AHP model considered a triple-bottom-line perspective using social, economic, and environmental impact criteria. The result showed that the initial costs of acquisition can be a barrier, but in the long run, cities adopting battery buses in their BRT system can benefit from the effects of reduction in gas emissions and longer lifetimes of electric bus components. Moreover, the results showed that the adoption of battery buses in BRT systems depends on local government subsidies being implemented. [ABSTRACT FROM AUTHOR]

The article concerns the issue of retrofitting (i.e., the conversion of worn-out diesel buses into electric buses). As this solution is often cheaper than purchasing new electric buses, it can be attractive for low-population areas with a weaker economic infrastructure. The article aims to present an original method for rapidly selecting components for the electric traction system, such as the electric motor, inverter, and transmission systems, combined with a battery installed in a drawer. The battery swapping solution is dedicated to regions with underdeveloped power infrastructure that does not allow for fast charging of bus batteries using pantographs. A mathematical model in the form of a polynomial was developed to estimate the energy losses for a given route. This model consists of a bus physics model, an energy loss model in the propulsion system, and a battery model. The weight coefficients of the polynomials were determined based on an analytical analysis of the model dependencies. The obtained models were reduced using the Lasso regularization method in linear regression. The input data for the model includes route characteristics (or driving cycle) and technical characteristics of the traction system components. The model output provides a detailed profile of electric energy consumption and peak values of the drive system characteristics (e.g., maximum torque of the motor) which must not be exceeded. Implemented as computer software, the model—combined with a database of motors, inverters, drive transmission systems, and batteries—allows for a quick calculation of the possibilities of applying a selected configuration to cover a given route. The approach proposed in the article enables the rapid composition of electric traction devices based on required driving conditions during the initial vehicle prototyping stage. At the same time, it allows the state of the bus battery to be monitored and estimates the remaining range during the operation of upgraded buses. [ABSTRACT FROM AUTHOR]

Scheduling buses in public transport systems consists in assigning trips to vehicle blocks. To minimize the cost of fuel and environmental impact of public transport, the number of vehicle blocks used should be as small as possi-ble, but sufficient to cover all trips in a timetable. However, when solving real life transportation problems, it is difficult to decide whether the number of vehicle blocks obtained from an algorithm is minimal, unless the actual minimal number is already known, which is rare, or the theoretical lower bound on the number of vehicles has been determined. The lower bound on the number of vehicle blocks is even more important and useful since it can be used both as a parameter that controls the optimization process and as the minimum expected value of the respective optimization criterion. Therefore, methods for determining the lower bound in transportation optimiza-tion problems have been studied for decades. However, the existing methods for determining the lower bound on the number of vehicle blocks are very limited and do not take multiple depots or heterogeneous fleet of vehicles into account. In this research, we propose a new practical and effective method to assess the lower bound on the number of vehicle blocks in the Multi-Depot Vehicle Scheduling Problem (MDVSP) with a mixed fleet covering electric vehicles (MDVSP-EV). The considered MDVSP-EV reflects a problem of public transport planning encoun-tered in medium-sized cities. The experimental results obtained for a real public transport system show the great potential of the proposed method in determining the fairly strong lower bound on the number of vehicle blocks. The method can generate an estimated distribution of the number of blocks during the day, which may be helpful, for example, in planning duties and crew scheduling. An important advantage of the proposed method is its low calculation time, which is very important when solving real life transportation problems.

Existing studies on electric bus (EB) scheduling mainly focus on the arrangement of bus charging at the bus terminals, which can result in high scheduling costs and insufficient utilization of chargers and bus batteries. This paper proposes a bus replacement strategy during the operation of EB, i.e., during the operation, a bus with insufficient battery can be driven to a charging station, and will then be replaced at the station. Its passengers will be transferred to another fully charged “standby bus” at the charging station. The “standby bus” will execute the rest of the bus trip, and the replaced bus becomes a “standby bus” after it is fully charged. In this context, we consider the electric bus scheduling and charging infrastructure planning (charger location and quantity) problem, given level of service constraints, battery power limitation, charging capacity limitation. The proposed model is a nonlinear integer programming model. We then linearize the proposed model and obtain an equivalent mixed integer linear programming (MILP) model, which can be efficiently solved by commercial solvers (e.g., CPLEX). The case study results show that bus replacement can improve the operational efficiency of the EB system when compared with battery swapping, e.g., the charger utilization rate can be increased by 14.3%, and passengers’ travel time and the total cost can be reduced by 15.5% and 7.75%, respectively.

The heavy reliance on petroleum-based fuels in the road transport industry, especially public transportation, results in substantial emissions of greenhouse gases. A significant obstacle on the path to a more sustainable public transportation sector is the employment of alternative fuels with lower environmental implications. Although there are several in-depth well-to-wheel studies for developed countries, extrapolating those results to gasoline markets in developing countries presents challenges because of significant differences in local fuel conditions and vehicle engine technologies. Notably, this study provides a national-level environmental life cycle assessment of alternative buses in Morocco and compares current diesel buses to potential hybrid diesel-electric buses, battery-electric buses, and fuel-cell buses from a well-to-wheel perspective. The model and approach used in this study applies to other countries with developing economies. Total energy consumption broken out by fuel type, greenhouse gas emissions, and criterion air pollutants is the primary outcome of this investigation. Findings highlight an energy cut of 44.7% from battery-electric buses, 36.3% from fuel-cell buses, and 31.7% from hybrid buses with regard to the current diesel buses. Additionally, alternative fuel buses proved to be less polluting in terms of greenhouse gas emissions with 50.8% less from battery-electric buses, 46.7% less from fuel-cell buses, and 26.8% less from hybrid buses; consequently, this would lead to reduced climate change, air pollution, acidification, and eutrophication, given the Egalitarian Environmental Impact Assessment. Ultimately, Morocco will have to tackle technological, financial, and institutional barriers to fully implement the change by 2030. [ABSTRACT FROM AUTHOR]

Traffic noise is a growing threat to the urban population. Prolonged exposure to traffic noise has been linked to negative health consequences such as annoyance, sleep disturbances and cardiovascular diseases. While electric vehicles are known to have lower noise profiles, the impacts of electric mobility on traffic noise, especially for electrified heavy-duty vehicles, have not been thoroughly examined. This study aims to examine the impacts of both electric light-duty vehicles and electric buses on traffic noise levels in a highly urbanized city. Traffic noise along the source line and pedestrian network was first estimated and mapped to illustrate its spatiotemporal variations. Then, scenario analysis was used to compare the impacts. Population potentially benefiting from reduced traffic noise in the neighbourhoods and the associated health impacts were also estimated. Results indicate that electric buses have a greater potential to reduce traffic noise, with a maximum reduction of 4.4 dBA during daytime in the urban cores. With all bus fleet electrified, around 60% of the population can benefit from a reduction of 1 dBA at the street environment, 15.3% for 1–2 dBA, and 4.3% for more than 2 dBA. The estimated reduction of preventable deaths and preventable cases of diseases per 100,000 population are 4.15 and 112.99 respectively. The findings shed important insights into prioritizing bus routes to be electrified in urban areas for maximizing health co-benefits.

In addition to passenger vehicles, battery-electric trucks and buses could offer substantial flexibility to the energy system. Using a Bass diffusion model, we extrapolated the unidirectional charging needs and availability of trucks in five of eleven typical applications, as well as city buses, for Germany until 2040. Combined, these heavy-duty vehicles could provide up to 23 GW of down-regulating flexibility potential (i.e., in case of excess power supply) in 2040. The resulting revenues could contribute to reducing electricity costs for depot operators. These results illustrate the need to provide easy and automated market access to heavy-duty vehicle fleets.

As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.

To aid in addressing issues of air quality and greenhouse gas (GHG) emissions in the South Coast Air Basin, local transit agencies are considering a shift to battery electric buses (BEBs) and hydrogen fuel cell electric buses (FCEBs). Each of these options vary in their overall effectiveness in reducing different emission types over their life cycle, associated life cycle costs, ability to meet operational needs of transit agencies, and life cycle environmental footprint. This project carried out a life cycle-based analysis and comparison of the GHG emissions, criteria pollutant emissions, and other environmental externalities associated with BEBs and FCEBs, taking into account their ability to meet operational constraints of the Orange County Transportation Authority. From an environmental footprint perspective, this study found the following. First, both FCEBs and long-range BEBs have comparable impacts for global warming potential and particulate matter formation but when the FCEBs were fueled using renewable hydrogen. Second, using electricity from the current California grid mix to drive electrolysis to produce hydrogen for FCEBs produced only marginal benefits compared to current natural-gas fueled vehicles due to the low supply chain efficiency of this pathway. Third, the mining of precious metals is a major contributor to environmental footprint categories for both BEBs and FCEBs. Fourth, both FCEVs and long-range BEBs provide significant reductions in environmental footprint compared to conventional diesel and natural gas buses. From a cost perspective, this study found the following. First, with current-day cost inputs, FCEBs and BEBs have comparable total cost of ownership, but both have slightly higher costs than diesel and natural gas buses. Second, FCEBs have an equivalent total cost of ownership to BEBs when the electricity rate for charging is $0.24/kWh. Higher values render FCEBs as the cheaper option and lower values render BEBs as the cheaper option. Second, the total cost of ownership of these technologies is highly sensitive to electricity costs, and the rapid evolution of the electricity system has strong implications for the economic comparison between BEBs and FCEBs. Overall, this study finds that while both FCEBs and BEBs provide life-cycle environmental benefits, further cost reductions in electricity rates and initial purchase costs are needed to achieve total cost of ownership parity with conventional bus powertrains. With the rapid evolution of the electricity system and falling costs for renewable electricity resources, these cost reductions may occur in the near future.

Transportation consumes approximately 29% of the world's energy and is responsible for approximately 25% of CO2 emissions worldwide. In Cuenca, Ecuador, fossil fuels for transportation represent 59.9% of total energy consumption. Ecuadorian legislation has specified gradually replacing traditional public bus transport units with electric units. Because the relevant costs include those associated with replacing equipment and improving route networks, alternatives that prioritize incorporating the first units on the best routes are being sought. This work establishes a methodological process with a set of tools to prioritize the gradual migration from conventional to electric buses according to the characteristics of each route. The Preference Ranking Organization Method for Enrichment of Evaluation (PROMETHEE) multicriteria technique is used to detect the best routes according to technical, social, environmental, economic and location criteria. A final rank of routes is obtained using Visual PROMETHEE software. After the values defining each criterion and its corresponding weight were processed, the final ranking makes it possible to prioritize one route before others. Given 23 lines and 475 bus units, the method enabled us to determine that in an initial phase, 68 electric buses are required to be used on five lines to reduce fuel consumption. The factors that most influence decision making are the number of passengers served, the length of the trip and the energy required by the route.

The penetration of electric vehicles is becoming more and more widespread and recently electric buses and trains are fetching the attention of researchers and developers. In this review, we examine the charging systems applied to the fast charging of electric vehicles and buses as well as the charging strategies, mainly applied to electric buses. We also briefly delve into power topologies for a more electrified railway system where we discuss their different supply systems as well as their motor drive systems. Problems and charge challenges for electric buses and trains are discussed too. [ABSTRACT FROM AUTHOR]

The difficulty of controlling the charging of electric buses (EBs) and their effects on network demand are discussed in this study. The solutions suggest a call for worldwide, complex infrastructures that manage EVs and EBs equally. Additionally, the Distribution Network (DN) must be prepared for an increased prevalence of reverse power flow caused by widespread distributed renewable generation. This paper focuses exclusively on EBs since they have higher capacity and predictable charging patterns, which makes them more significant for the DN in the context of a transition to complete vehicle electrification and technologies that are mature enough to be hosted. The proposed algorithm employs the Day-Ahead Energy Market (DAEM) in the Smart Charging (SC) to forecast the network operating circumstances. Additionally, the technique makes it possible to facilitate distributed photovoltaic (PV) generation, allowing network demand to be referenced depending on net demand. It also identifies an appropriate individual charger current per vehicle and per-time-step with load-levelling or peak-shaving as its primary goal. The final real demand demonstrates that a coarse correction of the demand is possible. According to the analysis of the DN voltage profile and associated line losses, the ideal node position location of the CS is dependent on PV penetration. [ABSTRACT FROM AUTHOR]

Many studies have evaluated CO2 emission from batteries. However, the impact of Li-ion battery (LiB) degradation on the CO2 emissions from the material through operation phases has not been sufficiently examined. This study aims to clarify the dominant CO2 emission phase and the impact of the degradation of general industrial LiBs from repetitive cycle applications. We developed a model common to general LiB composition and calculated CO2 emissions by the LCA method using the IDEA database. Our model simplifies the degradation process, including capacity decrease and internal resistance increase. We used it in a sensitivity analysis of the carbon intensity of electricity charged to a LiB. The loss mechanism was determined by experimental data for an electric bus with an industrial LiB. The results illustrate that the carbon intensity of electricity affects CO2 emissions dominance, the operation phase for mix (71.3%), and the material phase for renewables (70.9%), and that battery degradation over six years increases the total amount of CO2 emissions by 11.8% for mix and 3.9% for renewables equivalent. Although there are limitations regarding the assumed conditions, the present results will contribute to building a method for monitoring emissions and to standardizing degradation calculations. [ABSTRACT FROM AUTHOR]

The need to reduce greenhouse gas emissions from power generation has led to more and more installation of renewable energies such as wind and solar power. However, the high intermittency of these generators poses a threat to electrical grid stability. The power output of solar photovoltaic (PV) installations, for instance, depends on the solar irradiance, and consequently on weather conditions. In order to mitigate the adverse effects of solar intermittency, storage such as batteries can be deployed. However, the cost of a stationary energy storage system (SESS) is high, particularly for large PV installations. Battery electric vehicles (BEVs) are an alternative to SESS. With increasing number of BEVs, more and more storage capacity becomes available while these vehicles are charging. In this paper, we compare stationary batteries to mobile batteries of battery electric buses (BEBs) in a public bus terminus for balancing fluctuations of solar PV installations. Public buses have been chosen due to their large batteries and because they are more easily manageable than private cars. An optimisation model has been developed considering both the bus operator's and the PV operator's objectives. Cycle ageing of batteries is included in the investigation. Our analysis reveals that utilising public BEBs with high battery capacity to balance solar PV fluctuations can present a positive financial case. [ABSTRACT FROM AUTHOR]

Nowadays, the implementation of smart charging concepts and management strategies with vehicle-to-everything (V2X) functionalities, is required to address the increasing number of battery electric buses (BEBs) in cities. However, the introduction of these new functionalities to the charging systems might affect the lifetime of the charging infrastructure. This has not been investigated yet, although it is an important aspect for the BEB operators. Therefore, this paper performs a detailed reliability assessment to study the impact of smart and bidirectional (V2X) charging on the lifetime of SiC-based high-power off-board charging infrastructure used for BEBs in a depot for overnight charging. In this paper, four different charging current profiles, generated by a smart charging algorithm, are considered. In addition, an electro-thermal model of the charging system is developed to accurately estimate the junction temperature of the switching devices when subjected to the applied charging current profiles. The thermal stress is converted into a number of cycles to failures and accumulated damage by means of a rainflow cycle counting algorithm, a lifetime model and Miner's damage rule. Finally, a Monte Carlo analysis and a Weibull probability function fit are applied to obtain the system reliability. The results have demonstrated that smart charging strategies can improve the lifetime of the charging system by at least a factor of three compared to conventional uncoordinated charging. Moreover, an uncoordinated charging strategy fails to fulfill the lifetime requirements in the parts per million range, while bidirectional charging could even further enhance the lifetime with a factor of one and a half. [ABSTRACT FROM AUTHOR]

• Real option framework to support bus fleet owners in fleet replacement decisions. • Real option flexibility in fleet management translates into cost savings. • Different bus power technologies have different uncertainties. • Electric battery buses represent the winning technology in the long term. • Congested urban routes are the most suitable for electric battery buses. Fleet owners deal with various and critical uncertainties while planning bus fleet replacement, encompassing macroeconomic and technological factors, such as fuel and electricity prices, energy consumption, purchasing costs of electric batteries and powertrains, and bus salvage value upon resale. This paper proposes a Real Option-based framework to evaluate investment choices in different bus technologies over time (diesel, hybrid electric, compressed natural gas, and full-electric), where the proposed options usually involve replacing existing diesel fleets with alternatively powered buses. In particular, we provide a multi-option least squares Monte Carlo simulation to help fleet owners in making cost-effective investment decisions, resulting in potential cost savings of up to 10% of the annual total cost of ownership. By applying the proposed approach to different bus route types (inner-city, urban, suburban, commuter), the results show how the optimal investment choices for fleet replacement change over time. Initially, diesel buses or, at most, compressed natural gas buses are more cost-effective compared to electric technology. However, as electric technology becomes increasingly competitive, internal combustion buses are phased out. Inner-city and urban routes are better suited for the electric transition, whereas the transition may progress more slowly on other route types. Finally, we show that, under technological and macroeconomic uncertainties, the duration of contracts can strongly affect the investment decisions, in absence of specific guarantees on the new fleet. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive approach for sizing grid-connected hybrid renewable energy systems tailored for electric bus fleet operations. The study involves two main steps. First, a mathematical model that optimizes the configuration of such systems by considering daily electric bus consumption, solar irradiance, wind speed, and biomass potential is formulated. The model utilizes Pareto frontier multi-objective optimization to minimize the net present cost, the cost of energy, and greenhouse gas emissions. Second, the model is rigorously applied and tested in a real-world case study in Fez, Morocco, using HOMER Pro; the case study centers on the daily energy requirements of the buses, estimated at 2.5 megawatt hours per day, with a peak demand of 345 kilowatts. Two scenarios are explored, revealing a discernible trade-off dilemma between the full hybrid renewable energy scenario (Scenario 1) and the grid-connected hybrid renewable energy scenario (Scenario 2). In Scenario 2, the grid-connected hybrid renewable energy system demonstrates a notable 42.8% reduction in the net present cost, totaling USD 984,624. Similarly, the levelized cost of energy experiences a significant decrease, reaching approximately 0.08 USD/kWh, marking a 38.1% reduction. However, this apparent economic advantage is juxtaposed with a critical consideration—an increase in greenhouse gas emissions from null to 330,418 kg/year.

In electric buses, heating, ventilation and air conditioning are responsible for up to 50% of the energy consumption. It is therefore necessary to identify improved thermal settings to minimize the energy consumption, while guaranteeing good thermal comfort. Hence, an accurate prediction of the passengers’ thermal sensation (TS) is needed. One of the most widely used models for TS prediction is the PMV-PPD model, which has been shown to provide reliable results in uniform, steady-state climatic conditions. Since these are not present in an urban bus, the accuracy of the PMV-PPD model diminishes. Additionally, some of the parameters needed are difficult to obtain (i.e., clothing insulation). This paper presents seven different machine learning models (ML) for the prediction of TS using three different sets of parameters. The first set comprises five parameters similar to the PMV-PPD model, the second uses only two, and the third uses all parameters available. To obtain the necessary data, climatic measurements in an electric bus in Berlin, Germany, were made. These measurements were performed in summer for ambient temperatures between 14.7 °C and 32.0 °C. Person-related information as well as the thermal comfort assessment were obtained via surveys. Despite the relatively small data set, four of our seven ML models performed well with a median accuracy between 70.3% and 69.4%. This could also be observed when using only two parameters. Hence, the efforts to gain experimental data can be reduced significantly. For the PMV-PPD model, a median shift of +1 was observed for mild and warm TS. The median accuracy rises from 48.8% without shift to 68.8% with shift.

The implementation of a sustainable and efficient electric bus (eBus) transportation network requires addressing multiple concerns, such as limited driving range and battery charging/discharging time. Currently, eBuses can travel between 200 to 300 km on a single charge, and fast charging stations can fully recharge a battery in a matter of minutes. However, a failure in a charging station might negatively impact the operation of the system with unnecessary delays for the users. Taking this into account, we propose and implement a model for the Robust eBuses Charging Location problem that takes into account potential vulnerabilities of the transportation system. Our model incorporates a protection mechanism that allows eBuses to reach a backup charging station in case the regular one is down. We propose a MIP model to tackle this problem with minimal disruptions in the regular operation of the eBuses. Furthermore, we also present a Large Neighbourhood Search framework to efficiently tackle the problem. Our empirical evaluation suggests that our framework can operate a robust service with a small number of charging stations for three Irish cities and our Large Neighbourhood Search approach largely outperforms a popular commercial MIP solver.

This paper presents a novel mathematical model, integrating timetabling and vehicle scheduling problems for electric buses. The objective is to minimize the number of buses while satisfying constraints concerning routing and charging, including design choices for where to install charging stations. The aim of the paper is to illustrate and discuss the effects of solving the timetabling and vehicle scheduling of electric buses (including where to install charging infrastructure) separately, compared to solving them jointly in one single step. For that purpose, we perform tests with: i) given timetable, that is, solving only the vehicle scheduling problem, ii) fixed headways for each line, and iii) variable headways. A small test case based on actual bus lines from Västra Frölunda, Gothenburg, Sweden, is used. From the numerical experiments, we verify that combining the two planning steps can significantly reduce the number of vehicles needed.

Due to the environmental protection of electric buses, they are gradually replacing traditional fuel buses. Several previous studies have found that accidents related to electric vehicles are linked to Unintended Acceleration (UA), which is mostly caused by the driver pressing the wrong pedal. Therefore, this study proposed a Model for Detecting Pedal Misapplication in Electric Buses (MDPMEB). In this work, natural driving experiments for urban electric buses and pedal misapplication simulation experiments were carried out in a closed field; furthermore, a phase space reconstruction method was introduced, based on chaos theory, to map sequence data to a high-dimensional space in order to produce normal braking and pedal misapplication image datasets. Based on these findings, a modified Swin Transformer network was built. To prevent the model from overfitting when considering small sample data and to improve the generalization ability of the model, it was pre-trained using a publicly available dataset; moreover, the weights of the prior knowledge model were loaded into the model for training. The proposed model was also compared to machine learning and Convolutional Neural Networks (CNN) algorithms. This study showed that this model was able to detect normal braking and pedal misapplication behavior accurately and quickly, and the accuracy rate on the test dataset is 97.58%, which is 9.17% and 4.5% higher than the machine learning algorithm and CNN algorithm, respectively.

Current electric vehicle solutions offer the possibility of a fully electrified bus fleet, although due to financial constraints, most cities cannot afford it. Therefore, the possibility of battery replacement is a needed alternative to the electrification process of a city’s bus fleet. The aim of this study is to investigate the needs of cities and present the concept of battery replacement in an electric bus. The research was based on two groups of selected Polish cities: (1) up to 150,000 inhabitants, and (2) up to 1 million inhabitants. The research part includes an analysis of the means of transport in provincial cities in Poland, an analysis of the kilometers covered by the city fleet, the average distances covered by buses per day, and an estimate of the number of battery replacements. The concept is based on current technological solutions. The description of the concept includes the proposed battery and the technology used, the placement of the battery in the vehicle, and the replacement scheme. Research indicates that the concept can be used with existing technology but will be more justifiable for a larger city due to the higher fleet load. The paper shows the importance of researching bus electrification solutions and that modern solutions can improve existing urban networks in cities.

The rhythmic and stable operation of trolleybuses and autonomous trolleybuses or urban electric buses, depends to a large extent on the reliability of the equipment installed on the trolleybus. The actual operational reliability of trolleybus electrical equipment (EE) depends on its technical condition. Under the influence of external factors and specific operating modes, the technical condition of the equipment is continuously deteriorating, reliability indicators are decreasing, and the number of failures is increasing. Using the mathematical theory of reliability, probability theory and mathematical statistics, numerical methods of solving nonlinear and transcendental equations, this article defines the conditions of diagnostics depending on the intensity of failures and the given probability of failure-free operation of the equipment. Additionally, the inverse problem of determining the current reliability of electrical engineering systems depends on the terms of diagnostics and the intensity of failures being solved. As a result of the processing of statistical information on failures it is established that for the electrical equipment of a trolleybus, after a number of repair measures, the maximum density of failures occurs at a lower mileage, and the probability of failure-free operation can vary depending on the degree of wear of the equipment, i.e., on the number of previous failures. It is theoretically substantiated and experimentally confirmed that the reliability of trolleybus electrical equipment changes according to the exponential law of distribution of a random variable. It has been established that the real averaged diagnostic terms regulated by instructions are not optimal in most cases and differ several times from those defined in this paper. The dependence of switching equipment run-in on time has been clarified, which served as a prerequisite for specifying the inter-repair period for various types of trolleybus electrical equipment. A method of adjustment of the inter-repair time for the electrical equipment of trolleybuses is proposed.

The subject of this study was a distribution substation that feeds 14 fast DC chargers (80 kW) located at the bus depot in Lublin, Poland. The voltage variations were determined to be within the PN-EN 50160 standard limit values (±10% Un). There were several events registered when 4th, 6th, 8th, and 10th voltage harmonics were above the PN-EN 50160 limit during the charging of the electric buses. The obtained maximum 10 min average values of the total voltage harmonic distortion (THD) were 3.36%, 2.27%, and 2.89% for the first, second, and third phase, respectively, i.e., below the limit value of 8% required by PN-EN 50160. Due to the exceedance of the 6th voltage harmonic, the PN-EN 50160 requirements were not met. [ABSTRACT FROM AUTHOR]

The problems of bus bunching mitigation and the energy management of groups of vehicles have traditionally been treated separately in the literature and been formulated in two different frameworks. The present work bridges this gap by formulating the optimal control problem of the bus line eco-driving and regularity control as a smooth, multi-objective nonlinear program. Since this nonlinear program has only a few coupling variables, it is shown how it can be solved in parallel aboard each bus, such that only a marginal amount of computations need to be carried out centrally. This procedure leverages the structure of the bus line by enabling parallel computations and reducing the communication loads between the buses, which makes the problem resolution scalable in terms of the number of buses. Closed-loop control is then achieved by embedding this procedure in a model predictive control. Stochastic simulations based on real passengers and travel times data are realized for several scenarios with different levels of bunching for a line of electric buses. Our method achieves fast recoveries to regular headways as well as energy savings of up to 9.3% when compared with traditional holding or speed control baselines. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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The emission of GHG has been steadily increasing in the last few decades, largely facilitated by the transport sector, which has been responsible for more than two-thirds of the manmade emissions in Europe. In cities, one of the possible solutions to decrease the emissions from fossil fuel engines is to replace vehicles with electric ones. This solution can be applied to the urban public fleet, namely by replacing urban buses with electric vehicles. Thus, this research work focuses on the Portuguese case study, which serves as an example of achieving zero CO2 emissions from buses by 2034. This timeframe of replacing the current bus fleet, mostly powered by fossil fuels, with a fully electric fleet is proven to bring financial, environmental, and health benefits to the population. The pathway to the decarbonization of urban public transport will unequivocally contribute directly to the accomplishment of several UN Sustainable Development Goals (SDGs), such as the promotion of affordable and clean energy and sustainable cities and communities, as well as to the increasing climate action (SDGs 7, 11, and 13, respectively). In addition, it will provide an opportunity for the replacement of existing buses that are generally less efficient than electric buses, from both an energy and an environmental point of view. As a result of the methodology, the Portuguese urban bus fleet would be totally replaced by electric buses by 2034 (83% battery-electric and 17% hydrogen-electric), which results in zero CO2 emission from this type of public transport. [ABSTRACT FROM AUTHOR]

To improve the air quality in urban areas, diesel buses are getting replaced by battery electric buses (BEBs). This conversion introduces several challenges, such as the proper control of the charging process and a reduction in the operational costs, which can be addressed by introducing smart charging concepts for BEB fleets. Therefore, this paper proposes a real-time scheduling and optimization (RTSO) algorithm for the charging of multiple BEBs in a depot. The algorithm assigns a variable charging current to the different time slots the charging process of each BEB is divided to provide an optimal charging schedule that minimizes the charging cost, while satisfying the power limitations of the distribution network and maintaining the operation schedule of the BEBs. A genetic algorithm is used to solve the formulated cost function in real time. Several charging scenarios are tested in simulation, which show that a reduction in the charging cost up to 10% can be obtained under a dynamic electricity price scheme. Furthermore, the RTSO is implemented in a high-level charging management system, a new feature required to enable smart charging in practice, to test the developed algorithm with existing charging infrastructure. The experimental validation of the RTSO algorithm has proven the proper operation of the entire system. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the work is to present selected research results of the first stage of work, the so-called pre-design research related to the implementation of electric vehicles into the company's fleet. Design/methodology/approach: the article presents inconsistencies and problems related to the use of electric buses as well as problems related to point infrastructure. The results of the research were obtained from the observations of the analyzed transport processes, the process of fleet management, from interviews with employees, as well as from the analysis of documentation and comparison of records with the actual state. Findings: The results confirm that the effective elimination of nonconformities leads to the improvement of the process quality and the reduction of costs related to vehicle breakdowns. Research limitations/implications: The research indicates the need for continuous improvement of the process of using electric vehicles. Validation studies have confirmed that it is impossible to eliminate all problems, therefore the process should be constantly improved. Originality/value. The article indicates the need to amend the subsequent projects and the current methodology of the procedure. [ABSTRACT FROM AUTHOR]

This case study presents an assessment of the efficiency and risk of the vehicle fleet renewal in the public transport company. The investment project is solved in two alternatives. The first alternative consists of vehicle replacement by only electric buses. In the second alternative, a combination of electric buses and CNG buses is considered. Assessment of the economic efficiency of the investment project is based on the net present value (NPV). Due to lower initial and ongoing capital expenditures, the second alternative is more cost-effective than the first one although the profitability of the investment project is low. Within risk assessment, sensitivity analysis points out the subsidy and personal costs as the risk factors influencing variance of NPV the most. Finally, the effectiveness of the investment project is assessed at different levels of the use of financial support from EU funds and the state budget. In that case, even a low level of financial support significantly increased the profitability of the project.

Electrifying a commercial fleet while concurrently adopting distributed energy resources can significantly reduce the cost and carbon footprint of its operation. However, coordinating fleet operations with distributed resources requires an intelligent system to determine joint dispatch. In this paper, we propose a 24/7 Carbon-Free Electrified Fleet digital twin framework for the coordination of an electric bus fleet, co-located photovoltaic solar arrays, and a battery energy storage system. The framework optimizes electric bus and battery storage operations to minimize costs and emissions with the consideration of on-site solar generation, hourly marginal grid emissions factors, and predictions of bus energy consumption through a surrogate model. We evaluate the framework in a case study of Stanford University's Marguerite Shuttle electric bus fleet for both a campus depot, whereby non-controllable loads are coupled behind-the-meter, and a stand-alone depot. In a techno-economic analysis, we find that joint optimization of a campus depot's battery storage and bus operations saves at least $1.79M USD in electricity costs over a 10-year horizon while also reducing 98% of carbon emissions associated with the depot. For a stand-alone depot, sensitivity analyses show that 100% elimination of depot emissions is achievable without any trade-off with bill savings, whereas for depots with reduced on-site solar capacity, using an emissions-aware optimization model can reduce the depot's carbon footprint by an additional 17% at a carbon abatement cost of $66 USD/tCO 2 compared to a model that only minimizes electricity costs. Furthermore, optimized bus and battery operations have even greater impact in reducing electricity costs under new net billing tariff policies ("net energy metering (NEM) 3.0") compared to previous NEM 2.0 policies. As adoption of electric buses continues to grow, fleet operators may leverage our flexible framework to ensure smart, low-cost, and low-emissions fleet operations. • A digital twin framework for the coordination of solar, battery, and EV fleets. • Case studies using real electric bus transit system data to evaluate the framework. • Techno-economic analysis of a battery storage system in an electric bus depot. • Sensitivity analyses of the framework under varying solar and pricing scenarios. [ABSTRACT FROM AUTHOR]

The California Air Resources Board is considering regulatory changes to require an increasing share of public transit buses to produce zero-emissions by 2040. This report attempts to identify and assess critical drivers of electric bus adoption costs, characterize uncertainty in forecasting agency transition costs, and provide an approach to support agencies’ assessment of strategic investments in new vehicle technologies. While current purchase costs for electric buses are 40% higher compared to conventional diesel or clean-natural gas buses, by 2030, electric buses are likely to become the most cost-effective option. Taking total cost of ownership into consideration, replacing a bus fleet by 2030 with a 100% electric fleet is estimated to decrease overall costs by $0.1 to $3.6B compared to replacing the current fleet. Results are likely to vary depending on agency size as small and rural agencies have orders of magnitude smaller fleets than the largest agencies and the estimates take into account current subsides for purchases electric buses. The report concludes that total costs of ownership for electric buses are likely to be lower than current fleets and that agencies need better tools to be able to evaluate integrated technology and systems planning, particularly as it relates to transit bus electrification.

Public transport is essential to provide urban mobility, and cities need to offer a fast, comfortable, secure, and low-pollutant public transport mode to attract passengers and reduce private car use. Despite metros, trains, and light rail systems being desirable, the cost of implementation and low flexibility make bus services the best option for many cities. One solution for improving bus service systems is the implementation of bus rapid transit systems that are composed of buses running in segregated lines with pre-payment fares and level boarding platforms in stations. However, the main challenge of bus systems is the use of engines fueled by diesel, which is extremely polluted. One possible solution is the use of battery buses; but are they really feasible regarding daily operation cost? In this paper, we investigate the adoption of battery buses in comparison to diesel engines in a BRT system using municipal data from São Paulo, Brazil, as a reference. We collected operational data from the Sao Paulo city government transport agency and data from the literature to produce an analytic hierarchy process (AHP) model, which allowed us to compare both systems. The AHP model considered a triple-bottom-line perspective using social, economic, and environmental impact criteria. The result showed that the initial costs of acquisition can be a barrier, but in the long run, cities adopting battery buses in their BRT system can benefit from the effects of reduction in gas emissions and longer lifetimes of electric bus components. Moreover, the results showed that the adoption of battery buses in BRT systems depends on local government subsidies being implemented.

In this paper, we describe a comparative analysis of a bus route scheduling problem as part of timetable trips. We consider the current uptake of electric buses as a viable public transportation option that will eventually phase out the diesel-engine-based buses. We note that, with the increasing number of electric buses, the complexity related to the scheduling also increases, especially stemming from the charging requirement and the dedicated infrastructure behind it. The aim of our comparative study is to highlight the brevity with which a multi-agent-system-based scheduling method can be helpful as compared to the classical mixed-integer linear-programming-based approach. The multi-agent approach we design is centralized with asymmetric communication between the master agent, the bus agent, and the depot agent, which makes it possible to solve the multi-depot scheduling problem in almost real time as opposed to the classical optimizer, which sees a multi-depot problem as a combinatorial heuristic NP-hard problem, which, for large system cases, can be computationally inefficient to solve. We test the efficacy of the multi-agent algorithm and also compare the same with the MILP objective designed in harmony with the multi-agent system. We test the comparisons first on a small network and then extend the scheduling application to real data extracted from the public transport of the Maltese Islands.

The heavy reliance on petroleum-based fuels in the road transport industry, especially public transportation, results in substantial emissions of greenhouse gases. A significant obstacle on the path to a more sustainable public transportation sector is the employment of alternative fuels with lower environmental implications. Although there are several in-depth well-to-wheel studies for developed countries, extrapolating those results to gasoline markets in developing countries presents challenges because of significant differences in local fuel conditions and vehicle engine technologies. Notably, this study provides a national-level environmental life cycle assessment of alternative buses in Morocco and compares current diesel buses to potential hybrid diesel-electric buses, battery-electric buses, and fuel-cell buses from a well-to-wheel perspective. The model and approach used in this study applies to other countries with developing economies. Total energy consumption broken out by fuel type, greenhouse gas emissions, and criterion air pollutants is the primary outcome of this investigation. Findings highlight an energy cut of 44.7% from battery-electric buses, 36.3% from fuel-cell buses, and 31.7% from hybrid buses with regard to the current diesel buses. Additionally, alternative fuel buses proved to be less polluting in terms of greenhouse gas emissions with 50.8% less from battery-electric buses, 46.7% less from fuel-cell buses, and 26.8% less from hybrid buses; consequently, this would lead to reduced climate change, air pollution, acidification, and eutrophication, given the Egalitarian Environmental Impact Assessment. Ultimately, Morocco will have to tackle technological, financial, and institutional barriers to fully implement the change by 2030.

The transport sector accounts for around one-quarter of the GHG emissions in Europe, and, in Portugal, it represents almost one-third of the total emissions to the atmosphere. Unfortunately, these emissions have increased in recent years. Hence, cities and countries need plans to decarbonize their public transport fleets, and, more specifically, to replace fossil-fueled buses with electric buses that produce zero CO2 emissions. Thus, the main objective of this paper is to present a method to decarbonize the bus fleet in Portugal by a scheduled replacement of the current fleet, which is fueled by fossil fuels, with a completely electric fleet, in fourteen years. The study shows that it is possible to replace all Portuguese urban bus fleets with electric vehicles considering that all vehicles will be replaced when reaching the age of 14 years. Replacing the urban bus fleet with zero-emission buses would aid policymakers and bus companies to reduce the GHGs, and therefore contribute to fulfilling the Sustainable Development Goals of the United Nations 2030 Agenda for Sustainable Development, namely, Goal 13—take urgent action to combat climate change and its impacts. [ABSTRACT FROM AUTHOR]

One of the crucial steps for a successful integration of electric bus fleets into the existing electric power systems is the active and intelligent usage of their flexibility. This is important not only for reducing the eventual negative effects on the power grid but also for reducing energy and infrastructure costs. The first step in the optimal usage of flexibility is its quantification, which allows the maximum provision of flexibility without any negative effects for the fleet operation. This paper explores the available flexibility of large-scale electric bus fleets with a concept of centralized and unidirectional depot charging. An assessment of available positive and negative flexibility was conducted based on the data from two real bus depots in the city of Hamburg, Germany. The analysis shows the biggest flexibility potential was in the period from 16:00 h to 24:00 h, and the smallest one was in the periods from 08:00 h to 16:00 h, as well as from 02:00 h to 08:00 h. The paper also gives an overview of the possible markets for flexibility commercialization in Germany, which can provide an additional economic benefit for the fleet operators. A further analysis of the impact of parameters such as the timeline (working day or weekend), charging concept, ambient temperature, and electrical preconditioning provides an additional understanding of available flexibility. [ABSTRACT FROM AUTHOR]