Search Results (144)

The adoption of predictive maintenance in public transportation has gained increasing attention in the context of Industry 4.0. However, many urban bus fleets remain in early digital transformation stages, with limited historical data and fragmented infrastructures that hinder the implementation of data-driven strategies. This study proposes a reproducible Machine Learning pipeline tailored to such data-scarce conditions, integrating domain-informed feature engineering, lightweight and interpretable models (Linear Regression, Ridge Regression, Decision Trees, KNN), SMOGN for imbalance handling, and Leave-One-Out Cross-Validation for robust evaluation. A scheduled batch retraining strategy is incorporated to adapt the model as new data becomes available. The pipeline is validated using real-world data from hybrid diesel buses, focusing on the prediction of time spent in critical soot accumulation zones of the Diesel Particulate Filter (DPF). In Zone 4, the model continued to outperform the baseline during the production test, indicating its validity for an additional operational period. In contrast, model performance in Zone 3 deteriorated over time, triggering retraining. These results confirm the pipeline’s ability to detect performance drift and support predictive maintenance decisions under evolving operational constraints. The proposed framework offers a scalable solution for digitally emerging fleets. Full article

Air pollution caused by CO₂ emissions has become a global issue of vital importance, posing irreversible risks to health and life when concentration of CO₂ becomes too high. This study aims to estimate the CO₂ emissions and carbon footprint of the public transport bus fleet in Bucharest, with a comparative analysis of greenhouse gas (GHG) emissions generated by diesel and electric buses of the Bucharest Public Transport Company (STB S.A.) in the period 2021–2024, after the modernization of the fleet through the introduction of 130 hybrid buses and 58 electric buses. In 2024, the introduction of electric buses and the reduction in diesel bus mileage reduced GHG emissions by almost 13% compared to 2023, saving over 11 kilotons of CO_2e. There was also a 2.68% reduction in the specific carbon footprint compared to the previous year, which is clear evidence of the potential of electric vehicles in achieving decarbonization targets. We have also developed two strategies, one for 2025 and one for the period 2025–2030, replacing the aging fleet with electric vehicles. This demonstrates the relevance of electric transport integrated into the sustainable development strategy for urban mobility systems and alignment with European standards, including improving air quality and living standards. Full article

The DC-DC boost converter plays a crucial role in interfacing low-voltage sources with high-voltage DC buses in DC microgrid systems. To enhance the dynamic response and robustness of the system under large-signal disturbances and time-varying system parameters, this paper proposes a hybrid control strategy that integrates proportional–integral (PI) control with a deep Q-network (DQN). The proposed framework leverages the advantages of PI control in terms of steady-state regulation and a fast transient response, while also exploiting the capabilities of the DQN agent to learn optimal control policies in dynamic and uncertain environments. To validate the effectiveness and robustness of the proposed hybrid control framework, a detailed boost converter model was developed in the MATLAB 2024/Simulink environment. The simulation results demonstrate that the proposed framework exhibits a significantly faster transient response and enhanced robustness against nonlinear disturbances compared to the conventional PI and fuzzy controllers. Moreover, by incorporating PI-based fine-tuning in the steady-state phase, the framework effectively compensates for the control precision limitations caused by the discrete action space of the DQN algorithm, thereby achieving high-accuracy voltage regulation without relying on an explicit system model. Full article

The power system, which is also one of the most crucial parts of fuel cell cars, marks the biggest distinction between them and conventional automobiles. Fuel cell hybrid power systems are reviewed in this paper along with their current state of research. Three different kinds of fuel cell hybrid power systems—fuel cell–battery, fuel cell–supercapacitor, and fuel cell–battery–supercapacitor—are thoroughly compared and analyzed, and they are systematically explained in the three areas of passenger cars, buses, and heavy duty trucks. Existing fuel cell hybrid systems and energy strategies are systematically reviewed and summarized, including predictive control strategies based on game theory, power allocation strategies, fuzzy control strategies, and adaptive super twisted sliding mode control (ASTSMC) energy management techniques. This study offers recommendations and direction for the future direction of fuel cell hybrid power system research and development. Full article

The long-standing need for a modern public transportation system in Lebanon, a developing country of the Middle East with an almost exclusive dependence on costly and polluting passenger cars, has become more pressing in recent years due to the worsening economic crisis and the onset of hyperinflation. This study investigates the potential reductions in energy use, emissions, and costs from the possible introduction of natural gas, hybrid, and battery-electric buses compared to traditional diesel buses in local real driving conditions. Four operating conditions were considered including severe congestion, peak, off-peak, and bus rapid transit (BRT) operation. Battery-electric buses are found to be the best performers in any traffic operation, conditional on having clean energy supply at the power plant and significant subsidy of bus purchase cost. Natural gas buses do not provide significant greenhouse gas emission savings compared to diesel buses but offer substantial reductions in the emission of all major pollutants harmful to human health. Results also show that accounting for additional energy consumption from the use of climate-control auxiliaries in hot and cold weather can significantly impact the performance of all bus technologies by up to 44.7% for electric buses on average. Performance of all considered bus technologies improves considerably in free-flowing traffic conditions, making BRT operation the most beneficial. A vehicle mix of diesel, natural gas, and hybrid bus technologies is found most feasible for the case of Lebanon and similar developing countries lacking necessary infrastructure for a near-term transition to battery-electric technology. Full article

Hybrid energy storage systems (HESSs), with superior transient response characteristics compared to conventional battery (BAT) systems, have emerged as an effective solution for power balance. However, the high penetration of constant power loads (CPLs) introduces destabilization risks to the system. To address this challenge, this paper proposes a novel hierarchical control strategy to achieve voltage stabilization and accurate current sharing. First, this paper proposes an improved P–V² controller as the primary controller. It utilizes virtual conductance to replace the fixed coefficients of traditional droop controllers to achieve automatic power allocation between supercapacitors (SCs) and BATs, while eliminating the effects of CPLs on the voltage–current relationship. Second, based on traditional distributed control, the secondary control layer integrates a dynamic event-triggered communication mechanism, which reduces communication bandwidth requirements while maintaining precise current sharing across distributed buses. Finally, simulation and experimental results validate the effectiveness and robustness of the proposed control strategy. Full article

Public transportation in cities is negatively affected by reliance on petroleum-based fuels, leading to emissions and poor air quality. Although the environmental evaluation of alternative buses in terms of sustainability has been extensively studied, the social dimensions have not received as much attention. In this regard, this research examines the social implications of alternative urban buses through life cycle impact assessment (LCIA) methods, including Eco-Indicator 99, Impact 2002+, and ReCiPe Endpoint. The results indicate that diesel buses significantly impact health, while hybrid, fuel cell, and electric buses can decrease emissions by 50%. These results underscore the necessity of zero-emission technologies to enhance urban air quality and promote better public health. Full article

This study evaluates four hybrid infrastructure scenarios for supporting battery electric buses (BEBs) and fuel cell electric buses (FCEBs), analyzing different combinations of grid power, solar energy, battery storage, and fuel cell systems. A multi-stage framework—comprising energy demand forecasting, infrastructure capacity planning, and multi-criteria decision-making (MCDM) evaluation incorporating total cost of ownership (TCO), carbon emissions, and energy resilience—was developed and applied to a real-world transit depot. The results highlight critical trade-offs between financial, environmental, and operational objectives. The limited rooftop solar configuration, integrating solar energy through a Solar Power Purchase Agreement (SPPA), emerges as the most cost-effective near-term solution. Offsite solar with onsite large-scale battery storage and offsite solar with fuel cell integration achieve greater sustainability and resilience, but they face substantial cost barriers. The analysis underscores the importance of balancing investment, emissions reduction, and resilience in planning zero-emission bus fleets. Full article

Demand-responsive connector services (DRC) are an important means to improve the current mobility connection problem. In this study, we develop a hybrid model for the minimization of total system cost for demand response shuttle buses, which includes operating cost and user cost, with fleet size per hour as the optimization variable of the model. The relevant variables are analyzed and numerically modeled by Matlab, and the relationship between fleet size, vehicle capacity and demand density and waiting time, onboard time, vehicle travel distance, and total system cost is analyzed. The results indicate that introducing financial subsidies markedly lowers the critical demand density necessary to ensure system viability. Moreover, subsidy intensity is positively associated with the service’s operational robustness. Through parametric examination, we observe a strictly monotonic relationship between subsidy magnitude and demand thresholds: as subsidy levels increase, the minimum demand requirements for sustainable operation decrease in a consistent, progressive manner; meanwhile, the optimal fleet size exhibits an approximately linear relationship with travel demand per unit area across varying vehicle capacities. Notably, an increase in vehicle capacity corresponds to a decrease in the growth rate of the required fleet size. This model demonstrates robust adaptability across diverse operational scenarios and serves as an effective tool for evaluating the efficiency of resource allocation in demand-responsive transit (DRT) services. Furthermore, it provides valuable theoretical support for the scheduling and planning of public transportation systems, particularly in low-density urban environments. Full article

To enhance the management of NOx emissions from hybrid electric buses, this paper develops an instantaneous NOx emission prediction model for hybrid electric buses based on a two-layer stacking ensemble learning method. Seventeen parameters, including operational characteristic parameters of hybrid electric buses, engine operating parameters, and emission after-treatment device operating parameters are selected as input features for the model. The correlation analysis results indicate that the Pearson correlation coefficients of engine coolant temperature and selective catalytic reduction (SCR) after-treatment device temperature show a significant linear negative correlation with instantaneous NOx emission mass. The Mutual Information (MI) analysis reveals that engine intake air volume, SCR after-treatment device temperature and engine fuel consumption have strong nonlinear relationships with instantaneous NOx emission mass. The two-layer stacking ensemble learning model selects eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and an optimized BP neural network as base learners, with a linear regression model as the meta-learner, effectively predicting the instantaneous NOx emission mass of hybrid electric buses. The evaluation metrics of the proposed model—mean absolute error, root mean square error, and coefficient of determination—are 0.0068, 0.0283, and 0.9559, respectively, demonstrating a significant advantage compared to other benchmark models. Full article

The global adoption of battery electric vehicles (EVs) and hybrid electric vehicles (HEVs) as a substitute for internal combustion engine cars (ICEs) in various nations offers a substantial opportunity to reduce carbon dioxide (CO₂) emissions from land transportation. EVs are fitted with an energy conversion system that efficiently converts stored energy into propulsion, referred to as “tank-to-wheel (TTW) conversion”. Battery-electric vehicles have a significant advantage in that their exhaust system does not produce any pollutants. This hypothesis is equally relevant to public transport. Despite their higher upfront cost, electric buses contribute significantly to environmental sustainability during their operation. This study aimed to evaluate the environmental sustainability of electric buses during their operational phase by utilizing the life cycle assessment (LCA) technique. This paper used the MATLAB R2021b code to ascertain the mean load of the buses during their operation. The energy consumption of battery electric and hybrid electric buses was evaluated using the WLTP Class 2 standard, which refers to vehicles with a power-to-mass ratio between 22 and 34 W/kg, overing four speed phases (low, medium, high, extra high) with speeds up to 131.3 km/h. The code was used to calculate the energy consumption levels for the complete test cycle. The code adopts an idealized rectangular blind box model, disregarding the intricate design of contemporary buses to streamline the computational procedure. Simulating realistic test periods of 1800 s resulted in an average consumption of 1.451 kWh per km for electric buses and an average of 25.3 L per 100 km for hybrid buses. Finally, through an examination of the structure of the Hungarian power system utilization, it was demonstrated that electrification is a more appropriate method for achieving the emission reduction goals during the utilization phase. Full article

The vehicle mass is a crucial state variable for achieving safe and energy-efficient driving, as it directly impacts the vehicle’s power performance, braking efficiency, and handling stability. However, current methods frequently rely on particular operating conditions or supplementary sensors, which limits their ability to provide accurate, stable, and convenient vehicle mass estimation. Moreover, as a form of public transportation, buses are subject to stringent safety standards. The frequent variations in passenger numbers result in substantial fluctuations in vehicle mass, thereby complicating the accuracy of mass estimation. To address these challenges, this paper proposes a hybrid vehicle mass estimation algorithm that integrates Robust Forgetting Factor Recursive Least Squares (Robust FFRLS) and Extended Kalman Filter (EKF). By sequentially employing these two methods, the algorithm conducts dual-stage mass estimation and incorporates a proportional coordination factor to balance the outputs from FFRLS and EKF, thereby improving the accuracy of the estimated mass. Importantly, the proposed method does not necessitate the installation of new sensors, relying instead on data from existing CAN-bus and IMU sensors, thus addressing cost control concerns for mass-produced vehicles. The algorithm was validated through MATLAB(2022b)-TruckSim(2019.0) simulations under three loading conditions: empty, half-load, and full-load. The results demonstrate that the proposed algorithm maintains an error rate below 10% across all conditions, outperforming single-method approaches and meeting the stringent requirements for vehicle mass estimation in safety and stability functions. Future work will focus on conducting real-world tests under various driving conditions to further validate the robustness and applicability of the proposed method. Full article

Urban transportation is a key contributor to air pollution in cities. While urban buses impact air quality, they also promote sustainable mobility. In the Paris region, buses account for approximately 4% of traffic emissions. This study addresses the gap in real-world emissions data for Euro VI diesel, diesel-hybrid, and compressed natural gas (CNG) urban buses by developing speed-dependent emission factors for CO, NO_X, SPN₂₃, and energy consumption. An optimized methodology was applied to portable emission measurement system data collected from 28 urban buses across various routes in the Paris metropolitan area, capturing emissions across different speeds and traffic conditions. Results showed that diesel buses emit around 2 g/km of NOx at low speeds, compared to 1.4 g/km for diesel hybrids and 0.6 g/km for CNG. CO emissions reached approximately 1 g/km for CNG and 0.5 g/km for diesel, while SPN₂₃ emissions for all powertrains were in the order of 10¹² particles/km. The resulting speed-dependent emission factors were incorporated into COPERT version 5.8, the European Union’s standard emission inventory software, improving the inventory accuracy. The findings underscore the need for additional reductions in air pollutant emissions to meet Euro 7 standards and provide a robust framework for improving air quality management. Full article

The large-scale integration of renewable energy sources and new loads, such as distributed photovoltaics and electric vehicles, has resulted in frequent power quality issues within distribution networks. Traditional AC distribution networks lack the necessary flexibility and have limited capacity to accommodate these new energy sources and loads. Transforming the conventional distribution network into an AC-DC hybrid network using flexible interconnection devices like Voltage Source Converters can enhance the network’s flexibility, mitigating the power quality challenges arising from the integration of renewable energy and new loads. Electric buses, with their substantial capacity, mobility, and centralized management, offer potential as mobile energy storage. They can participate in the dispatching of the distribution network, thereby improving the network’s flexibility in power regulation. This paper proposes a coordinated optimization approach that integrates electric buses and VSCs for distribution network dispatch. This method enables electric buses to assist in power dispatch without interfering with their primary public transport duties, thus enhancing the network’s capacity to absorb new energy sources and loads. Firstly, considering the mobility characteristics of electric buses, a multi-layer stochastic Time–Space Network model is developed for bus dispatching. Secondly, an optimization model is constructed that accounts for the coordination of charging and discharging power between VSCs and electric buses, with the objective of minimizing the network losses in the distribution system. Finally, the proposed model is transformed into a second-order cone programming formulation, facilitating its solution through convex optimization techniques. The effectiveness of the proposed approach is demonstrated through a case study. Full article

Microgrids are considered a practical solution to revolutionize power systems due to their ability to island and sustain the penetration of renewables. Most existing studies have focused on the optimal management of microgrids with a fixed configuration. This restricts the application of developed algorithms to other configurations without major modifications. The objective of this study is to design a rule-based modular energy management system (EMS) for microgrids that can dynamically adapt to the microgrid configuration. To realize this framework, first, each component is modeled as a separate entity with its constraints and bounds for variables. A wide range of components such as battery energy storage systems (BESSs), electric vehicles (EVs), solar photovoltaic (PV), microturbines (MTs), and different priority loads are modeled as modules. Then, a rule-based system is designed to analyze the impact of the presence/absence of one module on the others and update constraints. For example, load shedding and PV curtailment can be permitted if the grid module is not included. The constraints of microgrid components present in any given configuration are communicated to the EMS, and it optimizes the operation of the available components. The configuration of microgrids could be as simple as flexible loads operating in grid-connected mode or as complex as a hybrid microgrid with AC and DC buses with a diverse range of equipment on each side. To facilitate the realization of diverse configurations, a hybrid AC/DC microgrid is considered where the utility grid and interlinking converter (ILC) are also modeled as separate modules. The proposed method is used to test performance in both grid-connected and islanded modes by simulating four typical configurations in each case. Simulation results have shown that the proposed rule-based modular method can optimize the operation of a wide range of microgrid configurations. Full article