Search Results (179)

To accurately investigate the loss characteristics of fuel cell vehicles (FCVs) under actual operating conditions and enhance their power performance and economic efficiency, this study establishes a numerical model of the proton exchange membrane fuel cell (PEMFC) stack based on real-world data from the second-generation Mirai. The stack model incorporates leakage current losses and imposes a limit on maximum current density. Besides, this study analyzes the effects of operating parameters (PEM water content, hydrogen partial pressure, current density, oxygen partial pressure, and operating temperature) on stack power output, efficiency, and eco-performance coefficient (ECOP). Furthermore, Non-Dominated Sequential Genetic Algorithm (NSGA-II) is employed to optimize the PEMFC stack performance, yielding the optimal operating parameter set for FCV operation. Further simulations are conducted on dynamic performance characteristics of the second-generation Mirai under two typical driving cycles, evaluating the power performance and economy of the FCV before and after optimization. Results demonstrate that the established PEMFC stack model accurately analyzes the output performance of an actual FCV when compared with real-world performance test data from the second-generation Mirai. Through optimization, output power increases by 7.4%, efficiency improves by 1.95%, and ECOP rises by 3.84%, providing guidance for enhancing vehicle power performance and improving overall vehicle economy. This study provides a practical framework for enhancing the power performance and overall energy sustainability of fuel cell vehicles, contributing to the advancement of sustainable transportation. Full article

The automotive industry’s shift to a Circular Economy for global sustainability is vital, but it faces challenges when establishing efficient Reverse Supply Chains. Reverse Supply Chain implementation is dependent on multiple barriers and enablers, including eco-nomic, managerial, technological, regulatory, and social domains, thus making single-factor solutions ineffective. The purpose of this review is to conduct a systematic literature review to understand how these interconnected barriers and enablers can collectively shape Reverse Supply Chain implementation and performance, specifically within the automotive sector, which remains little known. The PRISMA framework was utilised, which resulted in 129 peer-reviewed articles being selected for review. Findings showed that the literature focuses primarily on Electric Vehicle batteries within developing economies, particularly China. Reverse Supply Chain implementation is governed not only by isolated barriers but by complex systemic interdependencies between enablers as well. This complex inter-relationship between barriers and enablers can be categorised into five key dimensions: economic and financial; managerial and organisational; technological and infrastructural; policy and regulatory; and market and social. The study reveals two systemic patterns driving the transition: technology–policy interdependence and the conflicting relationship between large-scale production and value extraction. Our findings also presented a research agenda focusing on strategic value creation through material streams of automotive electronics, plastic, and composites with high potential value, and further insights are needed in regions such as the Middle East, Oceania, and the Americas. Organisations should consider Reverse Supply Chain as a strategic approach for securing critical material supplies, while policymakers could leverage the use of digital tools as the foundational infrastructure for subsidies allocation and prevent fraud. Full article

Nowadays, the automotive industry is primarily driven by the CO₂ policy that targets net zero carbon emissions by 2035 from passenger cars and commercial vehicles. The main path to achieve this goal is the implementation of electric powertrains with the energy stored in batteries, as the case for battery electric vehicles (BEV). However, this technology still faces some difficulties in terms of energy density, overall weight, charging time, and vehicle autonomy. From the other point of view, fuel cell electric vehicles (FCEV) offer the same advantages as BEV in terms of CO₂ reduction, providing better autonomy and lower refueling time. The energy demand by the electric powertrain strongly depends on the vehicle driving conditions as it directly affects energy consumption. In that context, the article aims to study the electrical energy demand of an ultra-energy efficient vehicle intended for a Shell eco-marathon competition in order to minimize hydrogen consumption. The study was carried out over a single lap on the racing track in Nogaro, France while applying the race rules from the competition in 2023. It includes a numerical evaluation of the vehicle resistance forces in different driving strategies and experimental validation on the propulsion test bench. Full article

Nitrogen oxides (NO_x) emitted mainly from vehicle exhaust significantly contribute to urban air pollution, leading to photochemical smog and secondary particulate matter. Photocatalytic technology has emerged as a promising solution for continuous NO_x decomposition under ultraviolet (UV) irradiation. This study developed an eco-friendly permeable concrete incorporating activated loess and zeolite to improve roadside air quality. The high porosity and adsorption capability of the concrete provided a suitable substrate for a TiO₂-based photocatalytic coating. A single-component coating system was optimized by introducing colloidal silica to enhance TiO₂ particle dispersibility and adding a binder to secure durable adhesion on the concrete surface. The produced permeable concrete met sidewalk quality standards specified in SPS-F-KSPIC-001-2006. Photocatalytic NO_x removal performance evaluated by ISO 22197-1 showed a maximum removal efficiency of 77.5%. Even after 300 h of accelerated weathering, the activity loss remained within 13.8%, retaining approximately 80% of the initial performance. Additionally, outdoor mock-up testing under natural light confirmed NO_x concentration removal and formation of nitrate by-products, demonstrating practical applicability in real environments. Overall, the integration of permeable concrete and a durable, single-component TiO₂ photocatalytic coating provides a promising approach to simultaneously enhance pavement sustainability and reduce urban NO_x pollution. Full article

Trajectory planning for intelligent connected vehicles (ICVs) must simultaneously address safety, efficiency, and environmental impact to align with sustainable development goals. This paper proposes a novel hierarchical trajectory planning framework, designed for intelligent connected vehicles (ICVs) that integrates a semantic corridor with a spatiotemporal potential field. First, a spatiotemporal safety corridor, enhanced with semantic labels (e.g., low-carbon zones and recommended speeds), delineates the feasible driving region. Subsequently, a multi-objective sampling optimization method generates candidate trajectories that balance safety, comfort and energy consumption. The optimal candidate is refined using a spatiotemporal potential field, which dynamically integrates obstacle predictions and sustainability incentives to achieve smooth and eco-friendly navigation. Comprehensive simulations in typical urban scenarios demonstrate that the proposed method reduces energy consumption by up to 8.43% while maintaining safety and a high level of comfort, compared with benchmark methods. Furthermore, the method’s practical efficacy is validated using real-world vehicle data, showing that the planned trajectories closely align with naturalistic driving behavior and demonstrate safe, smooth, and intelligent behaviors in complex lane-changing scenarios. The validation using 113 real-world truck lane-changing cases demonstrates high consistency with naturalistic driving behavior. These results highlight the framework’s potential to advance sustainable intelligent transportation systems by harmonizing safety, comfort, efficiency, and environmental objectives. Full article

Autonomous-rail Rapid Transit (ART) systems operate on standard roadways while maintaining dedicated right-of-way privileges. Owing to their sustainability, punctual operation, and cost efficiency, ART systems have emerged as a promising solution for medium-capacity urban transit. However, the exclusive lane usage for ART systems frequently leads to inefficient lane utilization, thereby intensifying congestion for non-ART vehicles. This study proposes a moving-block-based lane-sharing strategy for ART with a leading eco-driving approach. First, dynamic lane-access rules are introduced, allowing non-ART vehicles to temporarily use the ART lane without forced clearance or signal coordination. Second, a modified eco-driving trajectory optimization algorithm is constructed on a discrete time–space–state network, allowing the ART trajectory to be obtained through an efficient graph-search procedure while simultaneously guiding following vehicles toward energy-efficient driving patterns. Finally, simulation experiments are conducted to evaluate the impacts of traffic demand, arrival interval, and non-ART vehicles’ compliance rate on system performance. The results demonstrate that the proposed strategy significantly reduces delay and energy consumption for non-ART vehicles by 72.6% and 24.6%, respectively, without compromising ART operations efficiency. This work provides both technical insights and theoretical support for the efficient management of ART systems and the sustainable development of urban transportation. Full article

In the analysis of electric vehicle (EV) energy consumption, three main approaches are commonly used: physics-based models, artificial intelligence (AI) models, and hybrid frameworks that combine both. This combination enables more accurate estimations of EV energy consumption under diverse operating conditions, while also supporting applications in eco-driving, route planning, and urban energy management. Accurate analysis and prediction of EV energy consumption are critical for vehicle design, route planning, grid integration, and range anxiety. Recent advances in AI, notably machine learning (ML) and deep learning (DL), enable data-driven models that capture complex interactions among driving behavior, vehicle characteristics, road topology, traffic, and environmental conditions. This paper reviews the state of the art and presents a structured methodology for building, validating, and deploying AI models for EV energy consumption and efficiency analysis. Features, model architectures, performance metrics, explainability techniques, and system-level applications are discussed. Full article

Travel planning is a process that allows users to obtain maximum benefit from their time, cost and energy. When planning a route from one place to another, it is an important option to present alternative travel areas on the route. This study proposes a travel route planning (TRP) architecture using a Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) model to improve both travel efficiency and environmental sustainability in route selection. This model incorporates carbon emissions directly into the route planning process by unifying user preferences, location recommendations, route optimization, and multimodal vehicle selection within a comprehensive framework. By merging environmental sustainability with user-focused travel planning, it generates personalized, practical, and low-carbon travel routes. The carbon emissions observed with TRP’s artificial intelligence (AI) recommendation route are presented comparatively with those of the user-determined route. XGBoost, Random Forest (RF), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), (Extra Trees Regressor) ETR, and Multi-Layer Perception (MLP) models are applied to the TRP model. LSTM is compared with Recurrent Neural Networks (RNNs) and Gated Recurrent Unit (GRU) models. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Normalized Root Mean Square Error (NRMSE) error measurements of these models are carried out, and the best result is obtained using XGBoost and LSTM. TRP enhances environmental responsibility awareness within travel planning by integrating sustainability-oriented parameters into the decision-making process. Unlike conventional reservation systems, this model encourages individuals and organizations to prioritize eco-friendly options by considering not only financial factors but also environmental and socio-cultural impacts. By promoting responsible travel behaviors and supporting the adoption of sustainable tourism practices, the proposed approach contributes significantly to the broader dissemination of environmentally conscious travel choices. Full article

To achieve the optimal energy allocation between the auxiliary power unit (APU) and battery of connected automated range-extended electric vehicle (CAR-EEV), the hierarchical eco-driving control with dynamic game energy management were investigated and the optimization design of APU working mode was carried out from a multi-objective perspective. Initially, the acceleration and speed of the host vehicle were adjusted in real time, based on the driving status of the preceding vehicle, and the ecological driving speed was obtained in the adaptive car-following eco-driving mode. The dynamic game energy management strategy was proposed, leveraging the real-time interactive information between the vehicle and the traffic environment, and intelligently allocating and scheduling the energy flow within the powertrain. Dynamic game optimization was adopted to achieve dynamic decision-making and control optimization on whether to switch the APU operating speed or not. The multi-objective optimization analyses are carried out based on the weight coefficient matrix. The hierarchical dynamic game energy management strategy based on eco-driving speed (HDGEMS) is implemented through dynamic games and exhibits excellent performance. This strategy enables dynamic adjustment of power distribution between the APU and the battery, thereby allowing the APU to operate efficiently under optimal operating conditions. Meanwhile, it effectively reduces secondary charging losses and the dynamic switching time of the APU, and ultimately achieves energy optimization. Eventually, the results of simulation and experimental thoroughly indicated that economy improvement, emission reduction, and battery life enhancement of CAR-EEV were effectively kept in balance under the control of the proposed HDGEMS with intelligent optimization mode. New research ideas and technical directions are provided for the field of EMS, which is expected to promote technological progress in the industry. Full article

In urban arterials, buses face dual constraints from signal-controlled intersections and bus stop dwell demands, and frequent start–stop cycles result in reduced operational efficiency and elevated energy consumption. To address this critical challenge, a sustainable eco-driving strategy integrating offline and online Reinforcement Learning (RL) is proposed in this study. Leveraging real-world trajectory data from a 15.47 km route with 31 stops, the energy consumption characteristics of electric buses under the combined effects of stops and intersections are systematically analyzed, and high energy consumption scenarios are precisely identified. An initial energy saving strategy is first trained using offline RL, and subsequently subjected to online optimization in a vehicle–infrastructure cooperative simulation environment that incorporates three typical stop configurations. The soft actor-critic algorithm is employed to reconcile the dual goals of energy efficiency and ride comfort. Simulation results reveal a significant improvement with the proposed strategy, achieving an 11.2% reduction in energy consumption and a 37.7% decrease in travel time compared to the Krauss benchmark model. This study confirms the effectiveness of RL in boosting the operational sustainability of public transport systems, offering a scalable technical framework to promote the development of green urban mobility. The research findings provide theoretical support and practical references for the large-scale promotion and engineering application of energy saving autonomous driving technology for electric buses. Full article

The article presents the results of a study on heat transfer within the engine block of the Eco Arrow 3 prototype vehicle, developed for participation in Shell Eco-marathon competitions. The main objective of these events is to minimize fuel consumption during track races, which leads to a specific driving strategy characterized by frequent engine shut-downs and restarts. Such a driving style introduces challenges not typically encountered in conventional vehicles, including the need to maintain the engine within an optimal temperature range. In this work, several geometric variants of cylinder finning were investigated with respect to their influence on cooling, heating, and heat accumulation. Four configurations of finning were analysed: the original fins with a height of h = 15 mm, a cylinder with fins completely removed (h = 0 mm), and two intermediate variants with fin heights of 5 mm and 10 mm. The original and finless cylinders were studied both experimentally and numerically, while the intermediate variants were analysed solely using numerical methods. A comparison between experimental and numerical results showed satisfactory agreement in terms of maximum temperatures, with differences of approximately 10–15 °C. Considering the specific operating conditions of such an engine, characterized by irregular on–off cycles, the numerical analysis indicated that fins with a height of h = 10 mm provide the most favourable balance, ensuring that the engine remains within the optimal temperature range required to achieve minimal fuel consumption. Full article

The study analyzes pro-environmental attitudes and behaviors of residents in the mountainous regions of Małopolska regarding energy saving and transportation. The main objective was to determine the extent to which environmental awareness, vehicle technical condition, and driving style translate into actual energy-efficient behaviors. The research was conducted using a quantitative method among 423 respondents from six mountain districts of Małopolska, based on a proprietary questionnaire and statistical analysis employing non-parametric tests, correlation coefficients, and principal component analysis. The results indicate that respondents most frequently declare simple pro-environmental actions such as waste segregation and energy saving, while less often engaging in activities requiring higher effort or investment, such as eco-driving or limiting car use. Women exhibit higher environmental sensitivity and greater support for ecological regulations, whereas men tend to focus on the technical aspects of vehicle maintenance. The most pro-environmental attitudes and motivations to switch to low-emission vehicles are observed among individuals aged 25–44. The findings confirm that demographic factors significantly differentiate eco–energy-saving orientations, and that environmentally friendly transport behaviors are closely linked to everyday energy-saving practices. Full article

This study presents models for estimating the charging time and travel time in autonomous electric taxi systems, based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning methods. In addition to these models, two classical time-series forecasting techniques ARIMA and Prophet were also applied to provide a broader comparative baseline. Unlike traditional time-series prediction methods, the proposed system combines artificial intelligence with Internet of Things (IoT) technologies to perform secure charging operations based on multi-layer cybersecurity mechanisms, including IP authentication, encrypted communication, and charger server validation steps. The models were trained and validated using a comprehensive dataset obtained from 100 electric vehicles with different battery capacities at 50 charging stations located in Kocaeli Province. In the predictions considering parameters such as the vehicle type, battery capacity, and charge level, both models showed high accuracy rates, with the GRU model performing better than the LSTM model in terms of the error rate and temporal consistency. ARIMA and Prophet, on the other hand, produced significantly lower performance compared to deep learning models, confirming that GRU is the most suitable approach for real-time duration estimation. Customers can obtain the estimated time, cost, and charging requirements before their trip, and continuous multi-stage IP-based security controls are performed throughout the charging process as part of the cybersecurity framework. If a foreign or unauthorized connection is detected, the charging operation is automatically stopped. The proposed approach not only increases the efficiency in electric vehicle energy management but also presents an innovative framework that contributes to sustainable and smart transportation. By combining deep learning models, classical statistical forecasting methods, IoT integration, and enhanced cybersecurity controls, this work represents a pioneering step toward autonomous, secure, and eco-friendly urban transportation systems. Full article

This paper introduces a control-oriented Linear Parameter Varying (LPV) model of an energy-efficient electric vehicle, enhanced to account for wind-induced disturbances. The proposed model structure is designed to support model-based control strategies focused on minimizing energy consumption. In addition to core control inputs—such as torque reference and cornering radius—the model integrates a simulated representation of wind effects on the vehicle’s longitudinal dynamics. To manage the underlying nonlinearities of the vehicle dynamics, a trajectory-based linearization approach was employed to construct the baseline LPV model without wind effects. The accuracy of the extended model was validated using real-world speed profile data. Owing to its modular and control-compatible design, the model provides a solid foundation for testing and developing energy-saving control strategies, making it especially applicable to the design and operation of energy-efficient electric vehicles. The proposed model holds significant potential for further reducing energy consumption, particularly in urban transportation scenarios. Full article

In this paper, we analyze the possibilities of optimizing the driving strategy for energy-efficient electric vehicles competing in the Shell Eco-marathon race. The base method we already developed and successfully applied for several years—winning the Urban Concept Battery Electric competition of the 2022, 2023, and 2024 Shell Eco-marathon races—was further tested, with small modifications to our optimization method. We only used an optimizer tool based on a genetic algorithm. We were interested in determining how a modification to the minimalization problem could help our optimizer find the best driving cycle to reach the minimum energy consumption. We successfully applied the modification to our method at the 2025 competition, where we beat our own record and proved its practical applicability. Full article