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This study investigates magnetic flux density (B) and radiofrequency electromagnetic field (RF-EMF) measurements on electric buses operating in Samsun, Turkey, focusing on two bus routes (called E1 and E4) during the morning and evening hours. Measurements were taken under diverse operational conditions, including acceleration, cruising, and braking, at locations of peak passenger density. Along the E1 route, the magnetic field intensity varied significantly based on the bus position, road slope, and passenger load, with notable increases during braking. In contrast, the E4 route showed a lower magnetic field intensity and RF-EMF values due to its straighter trajectory and reduced operational stops. The highest RF-EMF measurement recorded was 6.01 V/m, which is below the maximum levels established by the ICNIRP guidelines. In 11 out of the 12 different band-selective RF-EMF measurements, the highest contribution came from the downlink band of the base stations, while in only one measurement, the highest contribution originated from the uplink bands of the base stations. All data were subject to the Anderson–Darling test, confirming the generalized extreme value distribution as the best fit for both B and RF-EMF measurements. Additionally, the study assessed B levels inside and outside the bus during charging, revealing heightened readings near the pantograph. These findings significantly contribute to our understanding of electromagnetic field exposure in electric bus environments, highlighting potential health implications and informing the development of targeted mitigation strategies. [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.)

As electromobility and especially the electrification of public transportation develops, it is necessary to safeguard human health and minimize environmental impact. Electromagnetic fields generated by the current flowing through on-board batteries, installations, converters, propulsion, air conditioning, heating, lighting, or wireless communication systems in these vehicles may pose risks to drivers and passengers. This research investigates electromagnetic fields induced by extreme low-frequency currents and permanent magnets on electric and trolleybuses implanted in Lublin, Poland. The identification of electromagnetic fields concerned an electric bus model and two trolleybus models. A comparative analysis of the results obtained with the permissible limits in the environment was carried out. [ABSTRACT FROM AUTHOR]

Eco-driving aims to enhance vehicle efficiency by optimizing speed profiles and driving patterns. However, ensuring safe following distances during eco-driving can lead to excessive use of lithium-ion batteries (LIBs), causing accelerated battery wear and potential safety concerns. This study addresses this issue by proposing a novel, multi-physics-constrained cruise control strategy for intelligently connected electric vehicles (EVs) using deep reinforcement learning (DRL). Integrating a DRL framework with an electrothermal model to estimate unmeasurable states, this strategy simultaneously manages battery degradation and thermal safety while maintaining safe following distances. Results from hardware-in-the-loop simulation testing demonstrated that this approach reduced overall driving costs by 18.72%, decreased battery temperatures by 4 °C to 8 °C in high-temperature environments, and reduced state-of-health (SOH) degradation by up to 46.43%. These findings highlight the strategy's superiority in convergence efficiency, battery thermal safety, and cost reduction compared to existing methods. This research contributes to the advancement of eco-driving practices, ensuring both vehicle efficiency and battery longevity. [ABSTRACT FROM AUTHOR]

In partnership with the Southern California Regional Transit Training Consortium (SCRTTC), the California State University at Long Beach (CSULB) expanded potential audiences and offered program support for the Electric Vehicle Transit Bus High Voltage Safety Awareness class, which was previously developed under the National Center for Sustainable Transportation (NCST). CSULB expanded both the number of online offerings and the geographic reach by opening the class to transit agencies and campus fleet operators within the NCST network. The course was designed to enhance a technician's basic electrical skills and 2-circuit diagnosis, while teaching students how to work with a Digital Volt-Ohm Meter (DVOM). The effort supports the broader goal of building the workforce needed to support the transition to alternative energy and zero emission bus fleets.View the NCST Project Webpage

The energy consumption profiles of conventional fuelled and electric vehicles are different due to the fundamental differences in the driving characteristics of these vehicles, which have been actively researched elsewhere but mostly on the basis of uncommon geographical contexts. This study, therefore, collected driving data on electric and conventional diesel buses running along exactly the same set of bus routes in Hong Kong during normal daily revenue operations. This enabled a fair comparison of driving characteristics for both types of bus under identical real-life, on-road driving conditions, which highlighted the originality and contributions of this study. A three-step approach was adopted to carry out detailed driving pattern analyses, which included key driving parameters, speed–acceleration probability distributions (SAPDs), and vehicle-specific power (VSP) distributions. Results found that route-based comparisons did highlight important differences in driving patterns between electric and diesel buses that might have been smoothed out by analyses with mixed-route datasets. In particular, the spread, intensity, and directions of these differences were found to be exaggerated at the route-based level. The differences in driving patterns varied across different routes, which has significant implications on vehicle energy consumption. Government agencies and/or bus operators should make references to these results in formulating electric bus deployment plans. [ABSTRACT FROM AUTHOR]

The transport sector plays a key role in the development of modern cities, but at the same time it is one of the main sources of air pollution. In cities like Sofia, where air pollution is a serious problem, the modernization of public transport is imperative. This article analyzes the impact of the implementation of electric buses on the amount of emitted harmful emissions in Sofia, considering both the current data until 2024 and the forecasts for the next 3 – 7 years. A comparative analysis of data on harmful emissions before and after the introduction of electric buses is used to assess the impact. The data used are based on operating indicators from “Stolichne Autotrans” EAD and other sources such as “Center for Urban Mobility” EAD and the Metropolitan Municipality. The amounts of emissions of fine dust particles (PM10), carbon monoxide (CO), hydrocarbons (CxHx) and nitrogen oxides (NOx) were analyzed. A forecast scenario until 2030 is also made, based on current trends and planned initiatives [ABSTRACT FROM AUTHOR]

Copyright of Journal of Public Economy & Public Financial Management (JPEPFM) is the property of Journal of Public Economy & Public Financial Management (JPEPFM) 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.)

Calculations show that the daily maximum electrical power for charging electric vehicles in the Moscow energy system when transitioning from gasoline and diesel fuel to electricity can range from 9.6 to 28.4 GW. The additional volume of fuel for electricity generation in the Moscow energy system may amount to 35% of the current consumption. An increase in electrical energy consumption for charging electric vehicles leads to a deterioration in the electrical load conditions of generating equipment. Currently, when assessing the environmental efficiency of electric vehicles, emissions of harmful substances resulting from wear of tires, pads, and road surfaces are not taken into account. The transition to electric vehicles will increase emissions rather than reduce them, even without taking the additional emissions from increased fuel consumption at power plants into account. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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 Revista Fatec Zona Sul (REFAS) is the property of Revista Fatec Zona Sul (REFAS) 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.)

This paper presents a framework for the efficient management of renewable energies to charge a fleet of electric buses (eBuses). Our framework starts with the prediction of clean energy time windows, i.e., periods of time when the production of clean energy exceeds the demand of the country. Then, the optimization phase schedules charging events to reduce the use of non-clean energy to recharge eBuses while passengers are embarking or disembarking. The proposed framework is capable of overcoming the unstable and chaotic nature of wind power generation to operate the fleet without perturbing the quality of service. Our extensive empirical validation with real instances from Ireland suggests that our solutions can significantly reduce non-clean energy consumed on large data sets. [ABSTRACT FROM AUTHOR]

Across the globe, the adoption of electric vehicles (EVs), particularly in mass transit systems such as electric buses (E-bus), is on the rise in modern cities. This surge is attributed to their environmentally friendly nature, zero carbon emissions, and absence of engine noise. However, the charging of E-bus batteries could impact the peak demand on the main grid and its overall serviceability, especially when numerous batteries are charged simultaneously. This scenario may also lead to increased energy costs. To address the previously mentioned issue, battery swapping is employed at the charging station in lieu of conventional battery charging. In this paper, the battery swapping approach is utilized to establish the optimal battery charging schedule for E-buses, taking into account both energy costs and the peak-to-average ratio (PAR). The E-bus battery swapping stations incorporate photovoltaic (PV) power generation as their energy source. Three metaheuristic algorithms–namely, the binary bat algorithm (BBA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO)–are employed to identify the optimal conditions. The simulation results demonstrate that integrating the optimal battery charging schedule with a PV power generation system in an E-bus battery swapping station can effectively lower energy costs and the PAR when compared to traditional battery charging methods at charging stations. The optimal charging schedule derived through the GWO technique outperforms those obtained from the WOA and BBA techniques. This resulted in a notable reduction in peak demand from 758.41 to 580.73 kW, corresponding to a 23.43% decrease in peak demand. The integration of the GWO with battery charging scheduling and PV installation resulted in a significant 27.63% reduction in energy costs. As per the simulation results, an optimized battery swapping schedule has the potential to lower energy costs and enhance serviceability for the E-bus battery swapping station.

Due to their efficient and environmentally friendly features, electric buses have become the key to solving urban transportation and environmental problems. However, existing studies primarily concentrate on optimizing schedules under fixed operational scenarios. This paper proposes a scheduling optimization approach to investigate the impact of stochastic traffic conditions on electric bus operations. First, a static dispatching interval optimization model is established, balancing the interests of the service and operation sides by measuring the passenger and company costs. Then, stochastic model formulas are designed considering three stochastic traffic conditions. Finally, a probabilistic evolution model is developed to improve the genetic algorithm. Based on the bus operation data in Harbin, we validate the model’s performance in enhancing operational benefits. Results reveal that the impact of stochasticity is nonlinear and more significant on passenger travel quality. Furthermore, retrofitting buses and upgrading routes are beneficial to reducing costs and improving travel experience. [ABSTRACT FROM AUTHOR]

Copyright of Notaire is the property of Universitas Airlangga 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.)

This study aimed to improve the efficiency of government regulations and promote the market applications of electric buses. Based on evolutionary game theory, with electric bus manufacturers, passenger transport enterprises, and the government as the evolutionary game subjects, a tripartite evolutionary game model of production-use management is established. An equilibrium stabilization strategy was derived through game equilibrium and evolutionary stability analysis, and the influence of changes in key variables on the choice of promotion behavior of decision subjects was quantitatively analyzed. The results of the empirical analysis showed that when the government adjusts the policy subsidy between 6–10, the changes in the sensitivity of manufacturers and passenger transport enterprises are relatively high, and the decisions of both sides tend to be 1; if the government penalty is adjusted to 4.8–6.8, the government strictly regulates the promotion of electric bus policy, and the manufacturers and passenger transport enterprises choose to actively participate in the promotion strategy, effectively constraining the decision-making behavior of all parties, which verifies the validity and consistency of the model analysis conclusions. The conclusions will provide theoretical reference for the formulation of government regulatory policies and the development of low-carbon transportation. [ABSTRACT FROM AUTHOR]

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. [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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Electric buses (EBs) have attracted more and more attention in recent years because of their energy-saving and pollution-free characteristics. However, very few studies have considered the impact of stochastic traffic conditions on their operations. This paper focuses on the departure interval optimisation of EBs which is a critical problem in the operations. We consider the stochastic traffic conditions in the operations and establish a departure interval optimisation model. The objective function aims at minimising passenger travel costs and enterprise operation costs, including waiting time costs, congestion costs, energy consumption costs and operational fixed costs. To solve this problem, a genetic algorithm (GA) based on fitness adjustment crossover and mutation rate is proposed. Based on the Harbin bus dataset, we find that improved GA performance is 4.481% higher, and it can solve the models more accurately and efficiently. Compared with the current situation, the optimisation model reduces passenger travel costs by 20.2% and helps improve passenger travel quality. Under stochastic traffic conditions, total cost change is small, but passenger travel costs increase significantly. This indicates the high impact degree of random traffic conditions on passenger travel. In addition, a sensitivity analysis is conducted to provide suggestions for improving the EBs operation and management.

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

This study investigates magnetic flux density (B) and radiofrequency electromagnetic field (RF-EMF) measurements on electric buses operating in Samsun, Turkey, focusing on two bus routes (called E1 and E4) during the morning and evening hours. Measurements were taken under diverse operational conditions, including acceleration, cruising, and braking, at locations of peak passenger density. Along the E1 route, the magnetic field intensity varied significantly based on the bus position, road slope, and passenger load, with notable increases during braking. In contrast, the E4 route showed a lower magnetic field intensity and RF-EMF values due to its straighter trajectory and reduced operational stops. The highest RF-EMF measurement recorded was 6.01 V/m, which is below the maximum levels established by the ICNIRP guidelines. In 11 out of the 12 different band-selective RF-EMF measurements, the highest contribution came from the downlink band of the base stations, while in only one measurement, the highest contribution originated from the uplink bands of the base stations. All data were subject to the Anderson–Darling test, confirming the generalized extreme value distribution as the best fit for both B and RF-EMF measurements. Additionally, the study assessed B levels inside and outside the bus during charging, revealing heightened readings near the pantograph. These findings significantly contribute to our understanding of electromagnetic field exposure in electric bus environments, highlighting potential health implications and informing the development of targeted mitigation strategies.

As electromobility and especially the electrification of public transportation develops, it is necessary to safeguard human health and minimize environmental impact. Electromagnetic fields generated by the current flowing through on-board batteries, installations, converters, propulsion, air conditioning, heating, lighting, or wireless communication systems in these vehicles may pose risks to drivers and passengers. This research investigates electromagnetic fields induced by extreme low-frequency currents and permanent magnets on electric and trolleybuses implanted in Lublin, Poland. The identification of electromagnetic fields concerned an electric bus model and two trolleybus models. A comparative analysis of the results obtained with the permissible limits in the environment was carried out.

Purpose: The purpose of this article is to present implemented innovative investments and changes in management forms in Municipal Transport Company Sp. z o. o. in Zielona Góra (hereinafter: MZK). These changes contributed to improving the operation of the company and providing services at a global level from the point of view of low-emission public transport and the concept of sustainable development. Design/methodology/approach: The presented innovations are implemented investments and management changes that operate in MZK in Zielona Góra and bring measurable economic, ecological and social benefits. The co-authors of the article are employees of MZK and students of the Faculty of Economics and Management of the University of Zielona Góra. The research methods used in the article are the analysis of the company documents and a review of the subject literature in the field of: eco-innovation, communication services, electromobility, management and ecology. Findings: Based on the analysis of documents and implemented investments, ecological, economic and social benefits were identified that MZK in Zielona Góra can boast of. Currently, it is the most innovative public transport company in Poland in terms of sustainable development and electromobility. Research limitations/implications: Analysis of the company's documents allows us to show the benefits that innovations bring to passengers, the company and the environment. They indicate directions for further changes in order to provide even better service quality and company management. Practical implications: The described innovative investments and implemented organizational changes in MZK make it possible to provide other plants in the industry with an incentive to take specific actions towards sustainable development and, as a result, to achieve such good results and satisfied customers as the company in question has. Originality/value: MZK in Zielona Góra is currently the most modern enterprise in Poland, whose fleet consists mainly of electric buses. Innovative solutions were initiated through the implementation of the project entitled: ‘Integrated system of low-emission public transport in Zielona Góra’ in 2014-2020. [ABSTRACT FROM AUTHOR]

Transition to electrified transit vehicles has attracted a great public attention to achieve a greener public transport service. This work develops a methodology for multi-type electric buses (EBs) accommodating spatio-temporally imbalanced passenger demand to improve significantly the operating efficiency. However, a new complexity of this multi-type EB scheme in contrast to conventional diesel buses occurs because multi-type EBs are characterized by different capacities, limited driving ranges, decisions on recharging time and/or locations and high initial investment costs. This work proposes a new, integrated timetabling and vehicle scheduling problem with shifting departure time to attain an even-load timetable using different types of EBs at a route's max-load stop, considering the use of fast/opportunity charging strategy. A genetic algorithm associated with right shifting of departure time has been developed to solve the resulting formulation, which is shown to be an NP-hard problem. A numerical example is used to illustrate the developed methodology, and a case study based on a scenario in the city of Dandong, China shows that the scheme of combining multiple vehicle types for a bus route not only can reduce the total cost but also bring out greater benefits than the single vehicle-type operation. From the operator viewpoint, it reduces passenger load surplus cost by approximately 11.2% for small Type A and 14.8% for large Type B. Moreover, the value of leftover pax unit cost has a significant effect on the selection of vehicle types, but has little effect on the number of trips or departures. This work shows that the higher the leftover pax unit cost is, the higher the number of large vehicle types is. [ABSTRACT FROM AUTHOR]

Electric vehicles are becoming increasingly popular to reduce environmental impact and to make logistics systems more cost-effective. This trend is affecting not only passenger cars but also public transport, especially in developed countries. For electric buses, the recycling of high-capacity batteries is becoming an increasingly important issue, as no generally applicable recycling model has yet been developed in this area. The paper presents a literature analysis in this field and identifies the main trends in battery recycling. In essence, it opens the way for future research, i. e. the elaboration of different operational models. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

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

Climate disruptions have prompted institutions to invest in zero-emissions technologies, in recent years. As a result, the transportation sector has witnessed a shift from internal combustion engines to electric. Several public transport companies have initiated the “Zero-emissions” project to introduce alternatives to diesel in their bus service. This paper delves into the impacts of transitioning from diesel-powered to electric buses. It starts by estimating emissions produced by buses and comparing them. Subsequently, the analysis evaluates the implications of renewing the fleet on the service, considering the change in bus capacity. Additionally, a profitability analysis assesses the Total Cost of Ownership, factoring in helpful life and average distance (kilometres) driven annually. Overall, the findings indicate that switching to electric buses is a promising approach towards achieving environmental objectives. The study shows that investing in electric buses, particularly those measuring 9-m, offers significant economic benefits while aligning with sustainability goals. The research demonstrates that electric buses yield a substantial reduction in global and local emissions when compared to their diesel counterparts. Adopting a comprehensive “well-to-wheel” perspective, electric buses achieve an impressive 68 % reduction in emissions. However, concerning local emissions, certain specific lines recorded values exceeded legal limits. While the initial investment costs for electric buses may surpass diesel buses, the total cost of ownership analysis conducted over 15 years indicates that electric buses can become more cost-effective over time. This cost-effectiveness and their environmental advantages strengthen the case for adopting electric buses to pursue sustainable transportation systems.

Eco-driving aims to enhance vehicle efficiency by optimizing speed profiles and driving patterns. However, ensuring safe following distances during eco-driving can lead to excessive use of lithium-ion batteries (LIBs), causing accelerated battery wear and potential safety concerns. This study addresses this issue by proposing a novel, multi-physics-constrained cruise control strategy for intelligently connected electric vehicles (EVs) using deep reinforcement learning (DRL). Integrating a DRL framework with an electrothermal model to estimate unmeasurable states, this strategy simultaneously manages battery degradation and thermal safety while maintaining safe following distances. Results from hardware-in-the-loop simulation testing demonstrated that this approach reduced overall driving costs by 18.72%, decreased battery temperatures by 4 °C to 8 °C in high-temperature environments, and reduced state-of-health (SOH) degradation by up to 46.43%. These findings highlight the strategy’s superiority in convergence efficiency, battery thermal safety, and cost reduction compared to existing methods. This research contributes to the advancement of eco-driving practices, ensuring both vehicle efficiency and battery longevity.

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. [ABSTRACT FROM AUTHOR]

The transformation of diesel buses into battery-powered electric buses for public transportation has become a global trend. The ability to evaluate the energy consumption of electric buses is critical in bus scheduling for alleviating range anxiety. In this study, an energy consumption estimation model for electric buses was proposed based on actual bus operation data. The operating states of an electric bus were categorized into four types: depressed accelerator pedal, depressed brake pedal, vehicle sliding, and vehicle idle states. Based on the bus state, two models were constructed to estimate the energy consumption. A multivariate linear model based on vehicle speed, accelerator pedal position, and instantaneous power was constructed to estimate the energy consumption of buses in the depressed accelerator pedal state. Combining that model with a long short-term memory (LSTM) algorithm, machine learning algorithms were calibrated to estimate bus energy consumption in the other three states over the four seasons. A comparative analysis was conducted for the different algorithms. The root-mean-square errors of the estimation results based on LSTM for vehicles in the depressed brake pedal, vehicle sliding, and vehicle idle states were 0.12%, 0.03%, and 27.27% lower than those of the artificial neural network, respectively. Accurate estimations of bus energy consumption during the four seasons allow bus operation companies to adjust the bus charging schedule to reduce the operating costs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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.