Search Results (22)

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

Urban bus fleets worldwide face urgent decarbonization requirements, with Germany targeting net-zero emissions by 2050. Current electrification research often addresses individual components—energy consumption, scheduling, or charging infrastructure—in isolation, lacking integrated frameworks that capture complex system interactions. This study presents “eflips-X”, a modular, open-source simulation framework that integrates energy consumption modeling, battery-aware block building, depot–block assignment, terminus charger placement, depot operations simulation, and smart charging optimization within a unified workflow. The framework employs empirical energy models, graph-based scheduling algorithms, and integer linear programming for depot assignment and smart charging. Applied to Berlin’s bus network—Germany’s largest—three scenarios were evaluated: maintaining existing blocks with electrification, exclusive depot charging, and small batteries with extensive terminus charging. Electric fleets need 2.1–7.1% additional vehicles compared to diesel operations, with hybrid depot-terminus charging strategies minimizing this increase. Smart charging reduces peak power demand by 49.8% on average, while different charging strategies yield distinct trade-offs between infrastructure requirements, fleet size, and operational efficiency. The framework enables systematic evaluation of electrification pathways, supporting evidence-based planning for zero-emission public transport transitions. Full article

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. Full article

The novel contribution of this research is decentralised IOTA-based concepts of digital trust for securing remote driving in an urban environment. The conceptual solutions are studied and described, and respective experimental solutions are developed relying on digital identities, public key cryptography with a decentralised approach using decentralised identifiers (DIDs) and verifiable credentials (VCs), and an IOTA-based distributed ledger. The provided digital trust solutions were validated by executing them according to the remote driving scenario but with a simulated vehicle and simulated remote driving system. The hybrid simulation mainly focused on the validation of functional, causal temporal correctness, feasibility, and capabilities of the provided solutions. The evaluations indicate that the concepts of digital trust fulfil the purpose and contribute towards making remote driving more trustable. A supervisory stakeholder was used as a verifier, requiring a set of example verifiable credentials from the vehicle and the remote driver, and accepting them to the security control channel. The separation of control and data planes from each other was found to be a good solution because the delays caused by required security control can be limited to the initiation of the remote driving session without causing additional delays in the actual real-time remote driving control data flow. The application of the IOTA Tangle as the verifiable data registry was found to be sufficient for security control purposes. During the evaluations, the need for further studies related to scalability, application of wallets, dynamic trust situations, time-sensitive behaviour, and autonomous operations, as well as smart contract(s) between multiple stakeholders, were detected. As the next step of this research, the provided digital trust solutions will be integrated with a vehicle, remote driving system and traffic infrastructure for evaluation of the performance, reliability, scalability, and flexibility in real-world experiments of remote driving of an electric bus in an urban environment. Full article

The electric bus is an extremely important part of urban public transportation and has a huge impact on the ecosystem. However, the battery capacity is still a tough problem, and electric buses often face a booming demand for charging during peak periods. This paper focuses on the problem of electric bus battery swapping station (BSS) location. Based on the traffic flow assignment theory, this paper proposes a hybrid traffic assignment method based on GA and Frank–Wolfe algorithm, which has proved to be closer to the global optimum than the traditional method. This paper proposes a BSS selection model considering service quality as an evaluating indicator and a simulation is made based on a virtual road network. Compared with the traditional method, the result from the hybrid method is more suitable for electric buses when considering the situation at peak hours. Full article

This work is focused on the simulation of a complete hybrid bus vehicle model performing a real-world driving cycle. The simulation framework consists of a coupled co-simulation environment, where all the vehicle sub-system models are linked to achieve a real time exchange of input and output signals. In the vehicle model also the electric devices of the powertrain and accumulation system are included. This co-simulation platform is applied to investigate the hybridization of a 12-m city bus, performing a typical urban driving mission. A comparison between the conventional powertrain is performed against the hybridized version, to highlight the advantages and challenges. In particular, the novelty of this modeling approach is that the IC engine simulation does not rely on pre-processed look-up tables, but exploits a high-fidelity one-dimensional thermo-fluid dynamic model. However, it was necessary to develop a fast simulation methodology to exploit this predictive tool, achieving a low computational cost. The 1D engine model is first validated against the experimental engine map data available, showing a good model predictivity. Then the 1D engine model and the other models of the powertrain are coupled to the vehicle model, in order to follow the prescribed velocity profile of the driving cycle. The complete model is applied under different conditions, to evaluate the impact on performance and emissions and assess the simulation predictivity. Full article

With the rapid development of machine learning, deep reinforcement learning (DRL) algorithms have recently been widely used for energy management in hybrid electric urban buses (HEUBs). However, the current DRL-based strategies suffer from insufficient constraint capability, slow learning speed, and unstable convergence. In this study, a state-of-the-art continuous control DRL algorithm, softmax deep double deterministic policy gradients (SD3), is used to develop the energy management system of a power-split HEUB. In particular, an action masking (AM) technique that does not alter the SD3′s underlying principles is proposed to prevent the SD3-based strategy from outputting invalid actions that violate the system’s physical constraints. Additionally, the transfer learning (TL) method of the SD3-based strategy is explored to avoid repetitive training of neural networks in different driving cycles. The results demonstrate that the learning performance and learning stability of SD3 are unaffected by AM and that SD3 with AM achieves control performance that is highly comparable to dynamic planning for both the CHTC-B and WVUCITY driving cycles. Aside from that, TL contributes to the rapid development of SD3. TL can speed up SD3’s convergence by at least 67.61% without significantly affecting fuel economy. Full article

Due to the growing air quality concern in urban areas and rising fuel prices, urban bus fleets are progressively turning to hybrid electric vehicles (HEVs) which show higher efficiency and lower emissions in comparison with conventional vehicles. HEVs can reduce fuel consumption and emissions by combining different energy sources (i.e., fuel and batteries). In this sense, the performance of HEVs is strongly dependent on the energy management strategy (EMS) which coordinates the energy sources available to exploit their potential. While most EMSs are calibrated for general driving conditions, this paper proposes to adapt the EMS to the specific driving conditions on a particular bus route. The proposed algorithm relies on the fact that partial information on the driving cycle can be assumed since, in the case of a urban bus, the considered route is periodically covered. According to this hypothesis, the strategy presented in this paper is based on estimating the driving cycle from a previous trip of the bus in the considered route. This initial driving cycle is used to compute the theoretical optimal solution by dynamic programming. The obtained control policy (particularly the cost-to-go matrix) is stored and used in the subsequent driving cycles by applying one-step look-ahead roll out, then, adapting the EMS to the actual driving conditions but exploiting the similarities with previous cycles in the same route. To justify the proposed strategy, the paper discusses the common patterns in different driving cycles of the same bus route, pointing out several metrics that show how a single cycle captures most of the key parameters for EMS optimization. Then, the proposed algorithm (off-line dynamic programming optimization and one-step look-ahead rollout) is described. Results obtained by simulation show that the proposed method is able to keep the battery charge within the required range and achieve near-optimal performance, with only a 1.9% increase in fuel consumption with regards to the theoretical optimum. As a reference for comparison, the equivalent consumption minimization strategy (ECMS), which is the most widespread algorithm for HEV energy management, produces an increase in fuel consumption with respect to the optimal solution of 11%. Full article

In this paper, an adaptive nonlinear control strategy for the energy management of a polymer electrolyte membrane fuel cell and supercapacitor-based hybrid electric vehicle is proposed. The purpose of this work was to satisfy: (i) tight DC bus voltage regulation, (ii) good fuel cell reference current tracking, (iii) better supercapacitor reference current tracking (iv) global asymptotic stability of the closed-loop control system, and (v) better vehicle performance by catering to slowly-varying parameters. We have selected the power stage schematic of a hybrid electric vehicle and utilized adaptive backstepping and adaptive Lyapunov redesign-based nonlinear control methods to formally derive adaptive parametric update laws for all slowly-varying parameters. The performance of the proposed system has been tested under varying load conditions using experimental data from the “Extra Urban Driving Cycle.” Mathematical analysis and Matlab/Simulink results show that proposed controllers are globally asymptotically stable and satisfy all the design requirements. The physical effectiveness of proposed system has been verified by comparing simulation results with the real-time controller hardware in the loop experimental results. Results show that proposed system shows satisfactory performance and caters for the time-varying parametric variations and the load requirements. Full article

A driving cycle is important to accomplish an accurate depiction of a vehicle’s driving characteristics as the traction motor’s flexible response to stop and start commands. In this paper, the driving cycle construction of an urban hybrid electric bus (HEB) in Zhengzhou, China is developed in which a measurement system integrating global positioning and inertial navigation function is used to acquire driving data. The collected data are then divided into acceleration, deceleration, uniform, and stop fragments. Meanwhile, the velocity fragments are classified into seven state clusters according to their average velocities. A transfer matrix applied to reveal the transfer relationship of velocity clusters can be obtained with statistical analysis. In the third stage, a three-part construction method of driving cycle is designed. Firstly, according to the theory of Markov chain, all the alternative parts that satisfy the construction’s precondition are selected based on the transfer matrix and Monte Carlo method. The Zhengzhou urban driving cycle (ZZUDC) could be determined by comparing the performance measure (PM) values subsequently. Eventually, the method and the cycle are validated by the high correlation coefficient (0.9972) with original data of ZZUDC than that of the other driving cycle (0.9746) constructed with traditional micro-trip and as well by comparing several statistical characteristics of ZZUDC and seven international cycles. Particularly, with around 20.5 L/100 km fuel and approximately 12.8 kwh/100 km electricity consumption, there is a narrow gap between the energy consumption of ZZUDC and WVUCITY, and their characteristics are similar. Full article

The development of more sustainable and zero-emissions collective transport solutions could play a very important measure in the near future within smart city policies. This paper tries to give a contribution to this aim, proposing a novel approach to fuel cell vehicle design and operation. Traditional difficulties experienced in fuel cell transient operation are, in fact, normally solved in conventional vehicle prototypes, through the hybridization of the propulsion system and with the complete fulfillment of transients in road energy demand through a high-capacity onboard energy storage device. This makes it normally necessary to use Li-ion battery solutions, accepting their restrictions in terms of weight, costs, energy losses, limited lifetime, and environmental constraints. The proposed solution, instead, introduces a partitioning of the hydrogen fuel cell (FC) and novel optimal power control strategy, with the aim of limiting the capacity of the energy storage, still avoiding FC transient operation. The limited capacity of the resulting energy storage systems which, instead, has to answer higher power requests, makes it possible to consider the utilization of a high-speed flywheel energy storage system (FESS) in place of high energy density Li-ion batteries. The proposed control strategy was validated by vehicle simulations based on a modular and parametric model; input data were acquired experimentally on an operating electric bus in real traffic conditions over an urban bus line. Simulation results highlight that the proposed control strategy makes it possible to obtain an overall power output for the FC stacks which better follows road power demands, and a relevant downsizing of the FESS device. Full article

Interests in evaluating lifecycle energy use in urban transport have been growing as a research topic. Various studies have evaluated the relationship between the intracity transport energy use and population density and commonly identified its negative correlation. However, a diachronic transition in an individual city has yet to be fully analyzed. As such, this study employed transport energy intensity widely used for evaluating transport energy efficiency and obtained the transport energy intensity for each transportation means including walk, bicycle, automobile (conventional vehicles, electric vehicles, hybrid vehicles, and fuel cell vehicles), bus and electric train by considering the lifecycle energy consumption. Then, the intracity lifecycle transport energy intensity of 38 cities in Japan in 1987–2015 was computed, assuming that the cause of diachronic transition of intracity transport energy efficiency is the modal shifting and electricity mix change. As a result, the greater level of population density was associated with the lower intracity transport energy intensity in Japanese cities. The negative slope of its regression line increased over time since the intracity lifecycle transport energy intensity in cities with low population density continuously increased without any significant change of population density. Finally, this study discussed the strategic implications particularly in regional areas to improve the intracity lifecycle transport energy efficiency. Full article

The acceleration of the motorization process creates severe environmental problems by affecting the energy consumption of urban traffic. As a major source of traffic pollution, vehicle exhaust deserves more attention when making traffic policy. Actually, the acceleration, deceleration, and idling conditions of vehicles cause more pollution than usual, which mainly happens at intersections of the road network. Besides, in the context of giving priority on public transport development, bus signal priority (BSP) at intersections becomes a quite prevalent measure to reduce average capita delay for travelers, while long-term practice also indicates that the unreasonable setting of bus lane further worsens the running conditions for other vehicles by occupying excessive traffic capacity, which highlights the indirect environmental effects of BSP. This paper provides a simulation-based method for evaluating the adaptability of BSP to find an optimum balance between efficient and environmental care. Specifically, the traffic volume, bus mixed rate of the intersection and energy types of vehicles consist of hybrid energy consumption conditions collectively. A VSP (vehicle specific power)-based exhaust emission models for both buses and other vehicles are employed to estimate the environmental cost of the entire intersection. Moreover, the overall efficiency of gasoline and electric vehicles is further evaluated to offer more implications for traffic control practice. Full article

Hybrid electric vehicles (HEV) might cause new noise vibration and harshness (NVH) problems, due to their complex powertrain systems. Therefore, in this paper, a new longitudinal dynamic simulation model of a series-parallel hybrid electric bus with an active torsional vibration control module is proposed. First, the schematic diagrams of the simulation model architecture and the active control strategy are given, and the dynamic models of the main components are introduced. Second, taking advantage of the characteristics of hybrid systems, a method of determining the key dynamic parameters by a bench test is proposed. Finally, in a typical bus-driving cycle for Chinese urban conditions, time domain and frequency domain processing methods are used to analyze vehicle body jerk, fluctuation of rotational speed, and torsional angle of the key components. The results show that the active control method can greatly improve the system’s torsional vibration performance when switching modes and at resonance. Full article

This contribution outlines the design of electric vehicle direct-current (DC) bus control system supplied by a battery/ultracapacitor hybrid energy storage system, and its coordination with the fully electrified vehicle driveline control system. The control strategy features an upper-level DC bus voltage feedback controller and a direct load compensator for stiff tracking of variable (speed-dependent) voltage target. The inner control level, comprising dedicated battery and ultracapacitor current controllers, is commanded by an intermediate-level control scheme which dynamically distributes the upper-level current command between the ultracapacitor and the battery energy storage systems. The feedback control system is designed and analytical expressions for feedback controller parameters are obtained by using the damping optimum criterion. The proposed methodology is verified by means of simulations and experimentally for different realistic operating regimes, including electric vehicle DC bus load step change, hybrid energy storage system charging/discharging, and electric vehicle driveline subject to New European Driving Cycle (NEDC), Urban Driving Dynamometer Schedule (UDDS), New York Certification Cycle (NYCC) and California Unified Cycle (LA92), as well as for abrupt acceleration/deceleration regimes. Full article