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Air pollutants and greenhouse gases (GHGs) represent major challenges in our era, contributing to climate change and global health issues. These problems arise from a variety of well-known sources, including motor vehicles. Almost all nations, Thailand included, have formulated and implemented policies to curb greenhouse gas (GHG) emissions in line with the requirements and commitments of the Paris Agreement. The evaluation of specific air pollutants and GHG emissions originating from road vehicles utilises the Thailand database, referencing the year 2019. Data intersections from 2019 to 2022 are grounded in actual data collected from relevant departments in Thailand, while projections for 2023–2030 are forecasted based on the baseline year. The secondary database used in the International Vehicle Emission model is adjusted according to real-world driving data to accurately reflect country-specific emission factors. Dynamic emission factors for specific air pollutants and GHGs are evaluated and integrated with the average Vehicle Kilometres Travelled (VKT) for each vehicle category. The Business-As-Usual (BAU) scenario is then examined, based on existing policies aimed at reducing air pollutants and GHG emissions in Thailand's transport sector. These policies include strategies for the adoption of electric vehicles and the promotion of public transport to reduce VKT. Under the BAU scenario, the overall number of road vehicles in Thailand, including passenger cars, motorcycles, pickups, vans, trucks, and buses, is expected to increase by approximately 6.58% by 2030, leading to a rise in specific air pollutants and GHG emissions compared to the 2019 baseline. However, by adhering to Thailand's strategies and transitioning to new electric passenger cars and buses, greenhouse gas emissions and specific air pollutants from the road transport sector will be significantly reduced. [ABSTRACT FROM AUTHOR]

We establish a bus market evolution model to explore the implications of subsidy policies in Hong Kong (HK) for the transition to a zero-emission bus (ZEB) fleet using an unsubsidized case as the benchmark scenario. Using the system dynamics (SD) approach, we capture the intricate feedback mechanisms among market share dynamics, cost considerations, consumer preferences, and competitive interactions within the bus market. These interactions are encapsulated within three pivotal feedback loops: scale economy, public acceptance, and bus market competition. We systematically integrate the HK-specific features of hydrogen buses (H-buses), battery electric buses (E-buses), and diesel buses (D-buses) from technical and economic perspectives and compare them to the benchmark scenario. The results show that only 66% of the bus fleet would be ZEBs by 2050, and this underscores the necessity for governmental financial incentives to expedite the transition to a fully zero-emission fleet. Furthermore, we evaluate various ZEB subsidy policy alternatives in terms of timing, intensity, and recipients. The analysis suggests that policies offering long-term and low-intensity subsidies are most beneficial, taking into account the cumulative effects and inherent delays associated with policy implementation. Importantly, the study highlights the critical balance that policymakers must achieve between short- and long-term objectives, especially given the counter-intuitive outcomes observed in the early stages of policies favoring H-buses exclusively. This study provides valuable policy insights for policymakers and upstream energy companies in HK, with potential applicability in wider real-world contexts. • Zero-emission bus alternatives are analyzed from technical and economic aspects. • Various feedback loops are considered for dynamic market analysis. • A counter-intuitive effect is observed in the hydrogen bus subsidy scenario. • A long-term subsidy with low intensity is recommended. • Tradeoff between short- and long-term policy effects is identified and discussed. [ABSTRACT FROM AUTHOR]

As electric buses become increasingly popular, it is imperative to optimize the schedules of electric buses with explicit consideration of their charging requirements. Unfortunately, existing studies failed to properly model the impacts of essential operating factors, including the time‐of‐use (TOU) electricity price, partial charging, and limited chargers. Our paper proposes a mixed‐integer nonlinear program to minimize the total operating cost of an electric bus fleet for fulfilling a group of timetabled trips, considering the above realistic features simultaneously. To effectively solve the problem of global optimality, we convert this model to an equivalent set partitioning model and develop a specialized branch‐and‐price approach subsequently. Our approach's computational efficiency is verified via extensive numerical experiments. The model is applied to a real‐world case study in Nanjing, China. Results show that incorporating the TOU electricity price into the electric bus scheduling problem can produce a sizeable cost saving of up to 22%. Managerial insights unveiled by the numerical results are discussed. These insights inform the practitioners of how the operations of electric buses can be both cost‐effective and grid‐friendly. [ABSTRACT FROM AUTHOR]

In this paper, we selected representative electric buses (EBs) and diesel buses as research cases, aiming to explore a localized method suitable for various vehicle types that can be implemented in any region to measure the carbon emissions of urban buses throughout the lifecycle. We take into account the influence of seasonal factors over the lifecycle. We fully consider the production, assembly, and recycling stages of vehicle systems. We construct a model for estimating CO2 emissions during the fuel cycle. We analyze and compare the impacts of the optimized power grid structure on the lifecycle CO2 emissions of the two types of buses. The results indicate that seasonal factors affect both bus types' energy consumption, with EBs being more sensitive. Carbon emissions primarily stem from manufacturing the body, chassis, and notably, the lithium‐ion batteries, with the latter having a pronounced impact on EBs. A decrease in coal power generation from 71% to 21% in the grid reduced EB fuel‐cycle emissions by 70.7%. Although EBs are sensitive to seasonal factors, they still have the greatest potential for carbon reduction, depending on the proportion of clean energy in the power grid and the production processes of vehicle components. [ABSTRACT FROM AUTHOR]

In order to improve the control performance and reliability of the pulse-width modulation (PWM) rectifiers in electric vehicle (EV) charging systems, the evaluation of DC bus capacitor health status is critical. In order to accurately monitor the health status of DC bus capacitors, a data-driven model fusion method is developed. In the method, multi-layer perceptron, random forest, and XGBoost are adopted as the base learners that produce separate row predictions. The second-level learner, support vector machine (SVM) accepts the outputs of the previous learners and integrates them into the final health status prediction. Meanwhile, the feature vector is constructed by only collecting the grid voltage, the grid current and the AC component of DC bus voltage. With the feature vector as input, the proposed method is able to accurately predict the health status of DC bus capacitor. Finally, we built a three-phase PWM rectifier as an experimental platform for validation. The experimental results verify that the proposed method fully utilizes the advantages of data-driven and model fusion, and achieves a high accuracy in the health state evaluation of DC bus capacitor. [ABSTRACT FROM AUTHOR]

Several cities worldwide are studying the replacement of their trolleybus systems with diesel buses or battery electric buses, due to their flexibility and lower operational costs. Diesel buses are considered a major cause of gas emissions in cities, while battery electric buses employ cutting-edge technology, but there is still discussion around the topic due to their technology costs, autonomy, and the sustainability of battery packs. In this study, we evaluated the trolleybus system's potential for reducing emissions, noise pollution, and greenhouse gases (GHGs) when compared to diesel buses. Furthermore, we compared the trolleybus system with battery electric buses in terms of cost and environmental benefits. To do so, a case study was conducted in São Paulo, Brazil, the largest city in Latin America, which operates the second-highest trolleybus system on the American continent. Our results show that the trolleybus system is a feasible alternative to diesel buses when considering environmental aspects. It can be seen as a complementary service for urban transport systems in the city's transition to clean energy. Finally, the study implications indicate the need for further investigation of the benefits of in-motion-charge technology to generate flexibility in trolleybus systems, and the involvement of stakeholders in the transition matrix energy process in urban bus systems beyond the direct costs. [ABSTRACT FROM AUTHOR]

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

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

In the application of battery electric buses, cruising range anxiety, high charging cost, and unreasonable allocation of charging resources are essential factors hindering their promotion. This study aims to identify an overnight charging schedule for battery electric buses to minimize battery charging–related operation costs. Among them, the impact of battery degradation cost and nonlinear charging function on the charging schedule is mainly considered. One novel feature of the considered problem is a controllable charging completion level. Therefore, we describe the overnight charging of battery electric buses as a parallel machine scheduling problem with a controllable task completion level. To solve this problem, mixed-integer linear programming formulations are developed. An efficient heuristic algorithm is proposed for solving large-scale instances, and numerical experiments verify the effectiveness of the proposed algorithm. A case study further reveals that the optimal charging schedule can save at least 10% of operation costs and has the potential to be applied in the operation of large-scale bus fleets. [ABSTRACT FROM AUTHOR]

The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and it consists of powertrain and heating, ventilation, and air conditioning (HVAC) system submodels. The main advantage of the proposed approach is its reliance on readily available trip-related data, such as travel distance, mean velocity, average passenger count, mean and standard deviation of road slope, and mean ambient temperature and solar irradiance, as opposed to the physical model, which requires high-sampling-rate driving cycle data. Additionally, the data-driven model is executed significantly faster than the physical model, thus making it suitable for large-scale city bus electrification planning or online energy consumption prediction applications. The data-driven model development began with applying feature selection techniques to identify the most relevant set of model inputs. Machine learning methods were then employed to achieve a model that effectively balances accuracy, simplicity, and interpretability. The validation results of the final eight-input quadratic-form e-bus model demonstrated its high precision and generalization, which was reflected in the R2 value of 0.981 when tested on unseen data. Owing to the trip-based, mean-value formulation, the model executed six orders of magnitude faster than the physical model. [ABSTRACT FROM AUTHOR]

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

Five typical in-use city buses in Tangshan with different emission standards, fuel power types, and emission control technology routes were selected as the research objects. PEMSs (Portable Emissions Measurement Systems) and OBD (On-Board Diagnostic) remote monitoring technologies were used to conduct research on actual road conditions and emission characteristics. The research aimed to analyze the driving characteristics of different power types of bus engines and after-treatment systems, as well as their impact on emissions. The results indicated that the actual road driving is mainly in the urban and suburban areas of the local region. Additionally, the VSP (Vehicle Specific Power) was found to be distributed in the low- and medium-speed range of mode Bin0~Bin24. The emission rates of each pollutant showed an increasing trend with the increase in VSP. THC and NOx emissions of CNG and LNG buses are significantly higher than those of gas–electric hybrid buses. However, the PM emissions are lower. Therefore, it is necessary to strengthen the NOx emission control of CNG and LNG buses. With the improvement in emission standards and the use of hybrid technology, CO2 and NOx emissions of China IV buses can be reduced by 13% and 53.5% compared with China III buses of the same tonnage. The CO2 and NOx emissions of China V buses using natural gas combustion and an electric mixture are 20% and 18.8% lower than those of China V buses of the same tonnage using only natural gas. In the actual operation of the diesel–electric hybrid bus equipped with the SCR (Selective Catalytic Reduction) system, the working time of the engine is about 35.5 ± 5%, and the working time of the SCR system only accounts for 65.5 ± 12%. The low working conditions of the SCR system and the low temperature of SCR system are the reasons for the high NOx concentration at the rear end of the SCR system when the hybrid electric bus is running at low speeds. [ABSTRACT FROM AUTHOR]

Environmental working conditions have a great impact on the power demand and fuel consumption of vehicles, and the rational use of road terrain information plays an important role in the improvement of vehicle economy and energy sources durability. In this paper, a fuel cell hybrid electric bus (FCHEB) energy management strategy (EMS) based on deep deterministic policy gradient (DDPG) algorithm taking into account future terrain information is proposed based on cyber-physical system (CPS) to reduce the economic cost of FCHEB. First, this paper implements the information exchange between the vehicle system and the network layer through CPS to obtain the environmental working conditions of the vehicle operation. Second, future terrain information is introduced into the framework for the first time with hydrogen consumption, battery degradation and fuel cell durability constraints to rationally allocate the power of the FCHEB. The results show that the proposed strategy improves the power battery durability by 7.39% and reduces the total operating cost by 5.76% compared to the EMS that ignores the future terrain information. • An energy management strategy that considers future terrain information is proposed. • The proposed strategy achieves an effective combination with cyber physical system. • A trade-off between hydrogen consumption and energy sources durability is achieved. • The proposed strategy reduces the total cost by 7.81% compared to NTI EMS. [ABSTRACT FROM AUTHOR]

With the prosperity of artificial intelligence in recent years, energy management strategies (EMSs) based on deep reinforcement learning (DRL) have become the mainstream approach to ensure efficient energy distribution of hybrid electric vehicles (HEVs). Moreover, in terms of developing sustainable urban public transportation systems, zero-emission fuel cell hybrid electric buses (FCHEBs) are more promising than hybrid electric buses (HEBs). Given that, this article proposes a novel data-driven EMS based on DRL to enhance the training efficiency of the proposed EMS while improving the fuel economy of an FCHEB. In this article, to optimize the power allocation of the FCHEB, an improved twin delayed deep deterministic policy gradient (TD3) algorithm combined with prioritized experience replay is innovatively formulated, and then a promising EMS based on it is proposed. Furthermore, to enhance the adaptability and improve the training efficiency of the proposed EMS, a stochastic training environment is established with massive real-world velocity data, and a pre-training method using the global optimal experience is designed. Simulation results show that the proposed EMS improves fuel economy by 5.87% compared with the EMS based on original TD3, reaching 97.15% of the global optimal dynamic programming method. • TD3 algorithm combined with prioritized experience replay is innovatively designed. • A stochastic training environment is designed based on massive real driving data. • A pre-training method is proposed to provide a heuristic effect for the TD3 agent. • The proposed strategy improves fuel economy by 8.03% than the DDPG-based strategy. • The robustness and computational efficiency of the proposed strategy are verified. [ABSTRACT FROM AUTHOR]

The paper presents a proposed system that supplies a 400 Hz single-phase onboard grid from the DC onboard bus. This system enables independent compensation of reactive power in the AC grid. Independent control of active and reactive power flow requires the decomposition of current in the grid into active and reactive components. Independent control of active and reactive power requires the use of synchronizers that operate in the dq frame system. If synchronization is performed with a single-phase grid, the transformation of dq requires the virtual quadrature signals. Standard quadrature signal generation systems use a second-order generalized integrator. To improve the dynamics of the system, the paper proposes a new quadrature generator that operates on the basis of trigonometric calculations instead of a second-order integration system. The developed system was implemented in a proportional-resonant current control system. Tests carried out in steady state and in dynamic states related to typical grid disturbances proved significantly better dynamic properties than those of a standard integrator-based system. [ABSTRACT FROM AUTHOR]

This study investigates the CO2 reduction potential of powertrain hybridisation on heavy-duty lorries and city buses. The analysis considers modern parallel and serial hybrid architectures, assessing their efficiency and limits in CO2 emission reduction through vehicle simulation in VECTO, which is the official tool of the European Commission for calculating heavy-duty vehicle fuel and energy consumption. The results reveal distinct trends for each vehicle type and architecture. In lorries, more significant improvements are observed in urban delivery profiles, reaching up to ~16%, indicating the benefits of hybridisation in transient conditions with energy recuperation opportunities. City buses, particularly those with serial architectures, exhibit significant emission reductions that reach 36%, making them suitable for urban environments. The optimisation of electric motor size and performance plays a crucial role in achieving emission reductions, while battery capacity must be carefully considered to avoid adverse effects. For lorries in urban delivery use, further improvements of 17.5% can be achieved by utilising a 160 kW engine motor and 30 kWh battery. Buses are already quite well optimised, with serial architecture presenting the highest benefits with a 120 kW electric motor and a battery of 11 kWh. Future research should focus on supercapacitors and gearboxes to improve efficiency at higher vehicle speeds and assess hybridisation potential in interurban coach travel. The heavy-duty vehicle sector can make significant strides towards low-carbon transport by maximising hybrid powertrain efficiency and emission reductions. [ABSTRACT FROM AUTHOR]

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

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]

Summary: Accurate state of charge (SOC) estimation is based on a precise battery model and is the focus of the battery management system (BMS). First, based on the second‐order RC equivalent circuit model and Grunwald–Letnikov (G‐L) definition, the high‐precision fractional‐order hysteresis‐equivalent circuit model (FH‐ECM) is established considering the open‐circuit voltage hysteresis effect. Then, the global parameters of the battery model are identified using a particle swarm algorithm optimized by the genetic algorithm (GA‐PSO). Third, a fractional‐order adaptive unscented Kalman filter (FOAUKF) algorithm is derived to estimate the SOC of lithium‐ion batteries. Finally, the feasibility of the model and algorithm is verified under complex working conditions. Under the dynamic stress test (DST) condition, the accuracy of model terminal voltage has been improved by 37.83%, and the error of SOC estimation has been reduced by 11.28%. Under Beijing bus dynamic stress test (BBDST) condition, the model terminal voltage accuracy has been improved by 51.44%, and the SOC estimation error has been reduced by 35.71%. The experimental results fully confirm the accuracy of the fractional‐order hysteresis‐equivalent circuit modeling method. [ABSTRACT FROM AUTHOR]

In light of growing global concern regarding environmental pollution, greenhouse gas emission and fast-depleting petroleum resources, electric buses are being considered as an integral part of future urban transportation system. However, substantial financial resources required for complete transition from conventional fuel-driven buses to electric buses means that this process will take place progressively. We have developed an optimization framework to assist in decision-making during the initial phase of transitioning to electric buses while working within financial constraints. Our approach focuses on a route-specific performance metric, the benefit-to-cost (B/C) ratio, which maximizes fuel savings and emissions reduction when transitioning to electric buses, all while respecting the limitations of available resources. The model identifies the set of bus routes to be electrified, along with the locations of electrified terminals. The proposed model was tested on the extensive public bus network of Delhi, India for two scenarios: compressed natural gas (CNG)-to-electric and diesel-to-electric bus transitions. Additionally, sensitivity analysis was conducted to evaluate how the available electrification budget and charging power affect the benefit-to-cost ratio and decisions related to route and terminal electrification. To fully realize the environmental benefits of electric buses, it is essential for the bus electrification policy to align with a shift from carbon-intensive energy sources to cleaner alternatives. Our study for Delhi suggests that a minimum of 30% of the electricity should come from clean energy sources to maximize the environmental benefits of bus electrification. This framework can thus serve as a useful tool to public transit agencies when planning the initial phases of the bus electrification. • Bus electrification should proceed in a phased manner due to financial challenges. • Benefit of bus electrification calculated using fuel saving and emissions reduction. • CNG-to-electric and diesel-to-electric bus transitions modelled using B/C ratio. • Optimization model prioritizes routes and terminals electrification under budget. • 30% clean energy grid mix needed for bus electrification's environmental benefits. [ABSTRACT FROM AUTHOR]

Public transport can play a central role in representing a viable and sustainable mobility solution, especially in urban areas. Average energy consumption and emissions per passenger are much lower than for private cars. At the same time, current buses often mostly rely on diesel, and there are different solutions that can contribute to public transport decarbonization. Biomethane is among the options to exploit local low-carbon resources to decrease the emissions of public transport in urban environments. This paper presents the analysis of a real case study considering real data on the fuel consumption and mileage of the existing bus fleet in the city of Turin, Italy, composed by diesel and natural gas buses. The aim of this study is to estimate the effect of different penetration levels of biomethane in substitution of the current fuels. The results show that the use of biomethane in urban buses could save to up to 71% of emissions compared to the current situation, and savings would increase to 75% when deploying biomethane and electric buses together. Average emissions per pkm could decrease from a current level of 85.5 gCO2/pkm to 21.3–63.4 gCO2/pkm depending on the penetration of biomethane and electric buses. The sensitivity analysis shows even higher savings when accounting for the future decrease of the electricity carbon intensity in Italy and for the additional benefits related to avoided emissions from manure disposal. The results of the analysis demonstrate the potential contribution of biomethane in decarbonizing urban buses, and the findings presented for this case study can be of use for policy makers and researchers that deal with a similar situation in other cities and countries. [ABSTRACT FROM AUTHOR]

The presence of shadows is one of the main disadvantages of photovoltaic solar panel installations in urban environments. This article analyses their effect on solar installations performed on urban elements where the use of solar energy can be considered novel: bus shelters. For this purpose, the PVGIS tool is used in combination with a new methodology for the extraction of the shadow horizon profile from LiDAR point clouds. The results show a 29.90% and 37% reduction of calculated solar radiation using horizon profiles derived from LiDAR point clouds of 0.5 pts/m2 and 1 pt/m2, respectively, versus no horizon profile. By taking shading into account, thanks to this study it is possible to make a more realistic prediction of the use of the electrical energy generated by the sun to cover urban energy consumption in bus shelters. Results show that the energy surplus produced overall allows these elements to be converted into charging points for light electric vehicles, allowing up to 35 units per day/bus shelter to be charged. The use of bus shelters as a place to generate clean energy through solar energy and charge light vehicles makes it possible to move towards sustainable and environmentally friendly cities. [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]

Vehicle and crew scheduling is vital in public transit planning. Conventionally, the issues are handled sequentially as the vehicle scheduling problem (VSP) and crew scheduling problem (CSP). However, integrating these planning steps offers additional flexibility, resulting in improved efficiency compared with sequential planning. Given the ever-growing market share of electric buses, this paper introduces a new model for integrated electric VSP and CSP, called EVCSPM. This model employs the minimum cost flow formulations for electric VSP, set partitioning for CSP, and linking constraints. Due to the nonlinear integer property of EVCSPM, we propose a method that hybrids a matching-based heuristic and integer linear programming solver, GUROBI. The numerical results demonstrate the efficiency of our methodology, and the integrated model outperforms the sequential model in real-life scenarios. [ABSTRACT FROM AUTHOR]

This paper assesses the transport system of Addis Ababa, Ethiopia, taking factors such as the number of vehicles, roadway width, speed of vehicles, longitudinal grade, and proportion of both fuel and electrical vehicles by dividing vehicles into seven classes, namely, car, minibus, small bus, coach, small truck, heavy truck, and truck trailer, to determine CO2 emission, CO emission fuel consumption, and electric consumption in addition to the percent to replace ICE vehicles. After selecting eight representative road sections in Addis Ababa city, input data was collected from both primary and secondary sources. Simulation of urban mobility (SUMO) is used to model the existing road transport system and two other scenarios, cases being 20% and 40% replacement of internal combustion engine vehicles by electric vehicles. Among the vehicle types studied under this paper, the SUMO results show that coaches are with the highest CO2 emission, releasing an average amount of 28.442 grams of CO2 every time step, while cars are with the lowest CO2 emission value of 6.542 grams. Minibuses are the top CO emitters, releasing an average of 0.420 grams of CO every time step, and truck trailers emit the smallest CO emission, 0.025 grams. Regarding electric consumption, the truck trailer is the vehicle type with the highest electric consumption, with a value of 2.282 kwh (watthour) consumption every time step, and cars are the least electricity-consuming vehicles, with a value of 0.151 kwh. The fourth point is fuel consumption; besides the high CO2 emission, coaches' consumption of fuel is leading by 8.946 grams, and cars use 2.087 grams of fuel every time step. Totally, public transport vehicles are responsible for higher emissions and huge fuel consumption. Therefore, if our transport system encourages the penetration of electric vehicles into the road transport system, a healthy and energy-efficient environment is reserved. Again, from a financial and environmental standpoint, the replacement of 40% of ICE vehicles by EVs enhances us with reduced costs and a green environment. [ABSTRACT FROM AUTHOR]

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

Battery electric buses (BEBs) are being introduced to substitute for diesel buses worldwide owing to their zero emissions. Investigating BEB scheduling problems is crucial for minimising charging costs and satisfying passenger demands. In addition, photovoltaic and energy-storage system (PESS) adoption in public transport could offer a promising alternative towards reducing charging costs and carbon dioxide emissions. This paper presents a novel BEB scheduling problem in which a PESS is deployed at bus-charging stations. The proposed problem is formulated as mixed-integer linear programming in which BEB scheduling and photovoltaic (PV) energy scheduling are optimised. To improve solution efficiency, a two-step solution approach is proposed: a BEB scheduling problem without considering the PESS is solved in the first step, and then a PV energy scheduling problem is solved in the second step. A case study is performed using the operational data of buses 404 and 109 in Beijing, China. The results show that the sum of charging and carbon dioxide emission costs will be reduced considerably if the PESS is introduced in a transit system. This study provides new insights for engineering a sustainable future in green transportation. [ABSTRACT FROM AUTHOR]

Environmental issues such as air pollution and abnormal climate are global concerns. To overcome these problems, the automobile industry is prioritizing the development of eco-friendly vehicles that reduce greenhouse gas emissions Among these, fuel-cell electric vehicles (FCEVs) use hydrogen as a fuel and do not emit exhaust gas and their higher mileage and shorter fuel charging time compared to electric vehicles make them promising next-generation eco-friendly vehicles. However, conventional energy management strategies have not effectively implemented both real-time capability and optimal energy distribution in FCEVs. To address these issues, a powertrain utilizing multi-energy sources is utilized, and a real-time energy control strategy based on long short-term memory (LSTM) is proposed. The training data for LSTM is obtained from the results of dynamic programming, utilizing six-city bus driving cycles, and the Braunschweig city driving cycle is chosen for test simulation. As a result, the LSTM prediction performance is evaluated, resulting in the development of an effective algorithm for real-time energy management of multi-energy sourced FCEVs. [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]

A Hybrid Electric Ship (HES) is investigated in this work to improve its dynamic response to sudden power demand changes. The HES system is based on a Variable-Speed Diesel Generator (VSDG) used for long-term energy supply, with Two Energy Storage Systems (TESSs) using Batteries and supercapacitors for transient power supply. The TESS mitigates the power demand fluctuations and reduces its impact on VSDG, which is linked to a DC-bus through a controlled rectifier. Batteries and Supercapacitors (SCs) are connected in a DC-bus using the bidirectional DC/DC converters to manage the transient and fluctuating components. Two thrusters (one in the front and the second in the back of the Ship) are considered for the propulsion system. The HES power demand includes the requirement of the thrusters and embedded power consumers (elevator, package lifting, air conditioning, onboard electronics devices, etc.). The highlight of this paper is based on the HES fast response improvement in sudden power demand situations via TESS-based batteries and supercapacitors. The other highlight concerns the SCs' electrothermal modeling using an extension of the SCs' current ripples' frequency range (0 to 1 kHz), considering parameter evolution according to using the temperature and current waveform. This energy management-based dynamic power component separation method is tested via simulations using a variable operating temperature scenario. [ABSTRACT FROM AUTHOR]

In this paper, a high gain nonisolated three-port bidirectional DC–DC converter is proposed to interface solar photovoltaic and battery energy storage system to DC bus with a reduced number of components. Four modes of operation based on the power flow and load demand are identified. The operating principle of the proposed converter and its operational waveform for all four modes of operation is described in this paper. The steady-state analysis of the proposed converter is performed to determine the voltage gain of the converter in all four modes of operation. The steady-state analysis is done for both continuous conduction mode and discontinuous conduction mode; and the boundary condition. The reduced number of components to achieve high-voltage gain ensures the lowering of the cost and weight of the converter. The analytical results are validated utilizing the simulation results from PSCAD and hardware results. Also, the results indicate that the ripple in the current and voltage is reduced significantly. [ABSTRACT FROM AUTHOR]

As part of the United Nations Global Technical Regulation No. 13 (UN GTR #13), vehicle fire safety is validated using a localized and engulfing fire test methodology and currently, updates are being considered in the on-going Phase 2 development stage. The GTR#13 fire test is designed to verify the performance of a hydrogen storage system of preventing rupture when exposed to service-terminating condition of fire situation. The test is conducted in two stages—localized flame exposure at a location most challenging for thermally-activated pressure relief device(s) (TPRDs) to respond for 10 min. followed by engulfing fire exposure until the system vents and the pressure falls to less than 1 MPa or until "time out" (30 min. for light-duty vehicle containers and 60 min. for heavy-duty vehicle containers). The rationale behind this two-stage fire test is to ensure that even when fire sizes are small and TPRDs are not responding the containers have fire resistance to withstand or fire sensitivity to respond to a localized fire to avoid system rupture. In this study, appropriate fire sizes for localized and engulfing fire tests in GTR#13 are evaluated by considering actual fire conditions in a hydrogen-powered electric city bus. Quantitative risk analysis is conducted to develop various fire accident scenarios including regular bus fire, battery fire, and hydrogen leak fire. Frequency and severity analyses are performed to determine the minimum fire size required in GTR#13 fire test and the design guides to ensure hydrogen storage tank safety in hydrogen-powered electric city buses. [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]

Bus terminals function as both the origins of multiple routes and charging places for electric buses (EBs) in large cities. Coordinated scheduling of routes starting from the same terminal can avoid the simultaneous charging demands of EBs, reducing queuing times for charging. The synchronous degradation in the state of health of EB batteries can also be realized with the coordinated scheduling, so as to lighten the burden of public transit corporations caused by frequent battery retirements. This study proposes a multiroute coordinated scheduling method where an electric bus is not fixed to serve on a certain route but runs different routes based on requirements. Utilizing chance constraint programming, an optimization model was formulated considering the stochastic volatility in trip travel times and time-ofuse tariff, addressing objectives to minimize the average queuing time for EB charging, the difference in operating intensities, and the charging cost. The Big M method was applied to convert the chance constraint programming model to a deterministic model, which is specifically a 0-1 integer programming model, and then solved by employing the branch-and-price method. Numerical tests were carried out using data from three real EB routes. Results indicate that the proposed scheduling method can reduce the average queuing time for charging, the daily charging cost, and the difference in operating intensities while maintaining the synchronous degradation of all EB batteries. [ABSTRACT FROM AUTHOR]

In order to improve the path following performance and solve the stability problems of autonomous distributed drive electric buses, this paper proposed a path following and lateral-yaw-roll stability integrated control method. Firstly, to cope with the varying speed during different maneuvers and roll motion of autonomous distributed drive electric buses, a nonlinear 4-DOF vehicle dynamic model which includes rolling and longitudinal movement is established. Secondly, a hierarchical control method is built, in the upper layer, a model predictive controller (MPC) is designed to generate the control inputs, which is obtained through an optimization problem including path following error and vehicle stability condition, especially the roll stability for buses, and for the lower layer, the torque of each drive wheel is distributed through solving an optimization problem about drive requirement and tire utilization. Finally, the validation of the proposed method is carried out through TruckSim-Simulink co-simulation, the results illustrate that both following accuracy and vehicle stability are obtained more effectively than the popular linear MPC method, especially roll stability is guaranteed under extreme condition. [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]

Battery electric buses are used for operation on urban bus routes. The main disadvantage of battery electric buses is their limited range that depends on energy consumption. This paper presents a new approach to the estimation of energy consumption on urban bus routes based on driving and energy profiles. The energy consumption results from the travel parameters along the bus route. The travel parameters are described by driving profiles. The determination of driving profiles is based on GPS location data recorded by a receiver on a bus. Location data are recorded at consecutive track points at a constant frequency. For each track point, the distance to the preceding track point is determined using the location data, and then the speed and acceleration are calculated. The analyzed bus route is divided into sections. For each section, travel parameters consisting of travel time, speed parameters, and acceleration parameters are determined. Using travel parameters, the energy consumption is estimated for individual sections and the entire bus route. The estimated energy consumption is described by energy profiles. Experimental results have been obtained for the selected urban bus route under various traffic conditions. For the assumed model of energy consumption, the energy consumed on the entire bus route is 1.8 KWh/km at off-peak hours and 2.1 KWh/km at peak hours. The driving and energy profiles describe the urban bus routes well and allow evaluation of the suitability of the bus route for operation with battery electric buses. [ABSTRACT FROM AUTHOR]

This research focuses on the measurement of the solar generation potential on the roads of the Andean city of Cuenca, Ecuador, and its application in electric vehicles. The tests were conducted in real environments, whereby natural and artificial structures obstruct direct radiation to the panel during the trajectory. An initial study is presented with daily operating conditions, using an urban bus route as a case study. The methodology used consists of taking measurements on different days and weather conditions to evaluate the photovoltaic generation and its contribution to the energy autonomy of the electric vehicle. Additionally, the energy autonomy between the electric vehicle with its factory configuration versus the one equipped with the solar panel is compared. For this purpose, a photovoltaic panel is installed on the roof of the vehicle, connected to a control system that monitors the radiation and current data, regulating the charging and discharging of the batteries. The aim is to demonstrate that the installation of solar panels on electric vehicles can significantly increase their energy autonomy. The contribution of this research could serve as an initial guide for governments and private companies to make decisions on the deployment of electric buses, electric vehicles and other vehicles integrated with solar photovoltaic energy, taking into account their routes. The findings of the study reveal that the implementation of the mobile charging system improves the range of the electric vehicle used in this study. In detail, an average increase of 40% in range was achieved in favorable environmental conditions and an increase of 14% in unfavorable environmental conditions. It is important to highlight that Cuenca has favorable conditions for solar systems due to its geographical location: altitude, hours of radiation and angle of incidence. [ABSTRACT FROM AUTHOR]

A main restriction of renewables from intermittent sources is the mismatch between energy resource availability and energy requirements, especially when extensive power plants are producing at their highest potential causing huge energy surpluses. In these cases, excess power must be stored or curtailed. One alternative is increasing urban solar potential which could be integrated to feed electric buses directly or alternatively through hydrogen (H2) as an energy vector. H2 from renewable electricity can be stored and used directly or through fuel cells. This study aims to determine the H2 capability that could be achieved when integrating large-scale photovoltaic (PV) generation in urban areas. This analysis was carried out by determining the PV energy potentially generated by installing PV in Cuenca City downtown (Ecuador). Cuenca is in the process of adopting renewal of the public transport vehicle fleet, introducing a new model with an electric tram main network combined with "clean type buses". The conventional diesel urban transport could be replaced, establishing a required vehicle fleet of 475 buses spread over 29 routes, emitting 112 tons of CO2 and burning 11,175 gallons of diesel daily. Between the main findings, we concluded that the electricity that could be produced in the total roof area exceeds the actual demand in the study area by 5.5 times. Taking into account the energy surplus, it was determined that the available PV power will cover from 97% to 127% of the total demand necessary to mobilize the city bus fleet. The novelty of this work is the proposal of a combined methodology to find the potential to feed urban transport with urban solar power in cities, close to the equatorial line. [ABSTRACT FROM AUTHOR]

This article suggests a hybrid DC microgrid (HDCMG) with different levels of DC bus voltages to use for various types of loads. The available sources in the HDCMG are wind generating systems (WGSs), photovoltaic (PV) systems, battery banks, and the AC grid for emergencies. The various levels of the DC bus voltages are 760 V, 380 V, and 48 V for different application uses such as electric vehicles and home applications. In addition, the controller plays an important role in the proposed system to achieve the desired DC bus voltage levels and extract the maximum power point (MPP) from the WGS and PV systems. In order to check the power continuity for the critical loads and improve the overall system performance, a suggested energy management strategy (SEMS) is developed. The SEMS is based on the optimum generated power and the state-of-charge (SOC) of the battery banks. Further, the SEMS is developed as a way to prevent battery storage from overcharging and deep discharging. The mathematical relations of the proposed HDCMG and MPP tracking are described. The bidirectional 3-Φ inverter connects the 760 V bus voltage to the AC grid for regulating this DC bus by absorbing the excess power or supplying the required power during the shortage in the generation and the low SOC of the battery storage. Buck converters with controlled duty cycles rather than constant duty cycles are used to obtain 380 V and 48 V from 760 V to achieve better dynamic responses. The overall HDCMG is evaluated using the MATLAB/Simulink package under different working cases to verify the capability of the control system and the PEMS. The obtained results are discussed and show the good performance and the capability of the overall system under the different scenarios, including (i) a comparison between variable duty and constant duty; (ii) high/low generated power and the SOC of the battery in the acceptable region; (iii) high/low generated power and the SOC of the battery in the critical region; and (iv) high/low generated power and the SOC of the battery in the overcharging region. [ABSTRACT FROM AUTHOR]

This study presents a novel bus charging station planning problem considering integrated photovoltaic (PV) and energy storage systems (PESS) to smooth the carbon‐neutral transition of transportation. This paper illustrates a two‐stage stochastic programming model capturing the uncertainty of PV power outputs and designs a step‐wise solution approach in which a conventional charging station location problem is solved in the first step and an improved L‐shaped algorithm is developed in the second step to determine charging station locations with PESS. The numerical experiments indicate that the step‐wise solution approach can provide high‐quality solutions within acceptable computational time. A case study is performed using a real‐world transit network in Beijing, China, with 34 bus routes and 15 candidate bus charging stations. Compared with the benchmark model, both recharging cost and carbon emission are reduced considerably. This paper provides novel insights into the development of integrated transportation and renewable energy systems. [ABSTRACT FROM AUTHOR]

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

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

Electric buses running on city routes have different thermal conditions than long-distance intercity buses. The main difference is the frequent stops every 0.5-1 minutes and the embarkation and disembarkation of passengers, which causes heat loss. On the other hand, such routes offer the possibility to recharge heating batteries with contactless or non-contact wireless charging systems during stops. In order not to reduce the driving distance of the bus by consuming energy from electric batteries for heating, one option is to use heating batteries. As the total duration of routes from one final destination to another varies from half an hour to an hour depending on the city, but the duration of parking at the end destination is 10-15 minutes, heating batteries must provide cabin heating for one full journey using fast charging while standing at the final stops. This is the main condition for choosing the energy capacity of heating batteries. In the research there was developed and in the article there is described a simulation model in MATLAB/Simulink, which allows modelling different heating power and temperature regimes by varying the bus cabin microclimate influencing factors, such as outside temperature, door opening duration, the heat emitted by passengers, etc. Recommendations are given for levelling the temperature in the cabin, which, according to the measurement data, is markedly uneven. [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]