Fuel-cell hybrid trucks (FCHTs), as primary force in fuel-cell vehicles, currently lack comprehensive development of rule-based energy management strategies (EMSs). The current optimization rule of ...the dynamic programming (DP) algorithm exhibits poor adaptability to changes in operating conditions and battery state of charge (SOC). Therefore, the work proposed real-time rule EMS of DP-optimized FCHTs. First, DP was taken offline to determine the optimal relationship between fuel cell power and demand power under driving conditions and SOCs. Secondly, real-time online recognition was achieved through driving pattern recognition (DPR) controller. A backpropagation neural network, optimized by the northern goshawk algorithm, was used for constructing DPR. Finally, the MATLAB/Simulink simulation showed that the proposed strategy exhibited superior DPR accuracy. Compared to rule-based strategies, significantly lower hydrogen consumption reduced vehicle-operating costs.
•A DP-optimized real-time energy management strategy was designed.•The multivariate DP offline of the optimal rule control sequence library was constructed.•Driving conditions and the initial SOC were defined as multivariates.•A driving pattern recognizer was constructed using the northern goshawk optimization and back-propagation neural network.
Artificial intelligence (AI) can be used to support intelligent and sustainable mobility solutions. For AI to be functional, it must be supplied with reliable data. With the continuous expansion of ...the data base for AI, it can fill data gaps based on learning effects and thus increase data quality. The electric Highway (eHighway) system as a sustainable mobility solution for long-distance road freight transport is a megaproject where the use of AI can be helpful. As a case study for this paper, the research project ELISA ('ELektrifizierter, Innovativer Schwerverkehr auf Autobahnen' = 'electrified, innovative road freight transport on motorways') was chosen in which the eHighway has been tested on a 10 km test track in Hesse (Germany) for about 2.5 years. The data on overhead line hybrid trucks and overhead line infrastructure obtained from the project was analysed in terms of their availability and combined to the overall availability of the eHighway system. These results provided the basis for a subsequent SWOT analysis to evaluate the integrability of AI in the eHighway system. The findings from the SWOT analysis show that with the continuous improvement of data availability and quality, the use of AI in the eHighway system is feasible. Energy, cost, and operational improvements in the eHighway system are expected through the use of AI.
Purpose The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight ...range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology. Design/methodology/approach Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques. Findings Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate. Originality/value Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.
In this paper, we introduce and formulate the capacitated hybrid truck platooning network design problem. The scope of this research is to benchmark the various parameters that affect the ...implementation of the hybrid truck platoon concept and the quantification of (any) monetary savings under the assumption that technological advancements and new or updated infrastructure (e.g., dedicated truck corridors) will allow implementation. Cost savings considered in this research are easily verifiable as they are solely derived from driver compensation savings (i.e., fuel savings, emissions reduction, and insurance savings are not considered). The proposed model further considers monetary penalties from truck late arrivals at the destinations. Multiple network instances are developed and used to evaluate the proposed model. Results indicate that significant cost savings can be achieved from the platooning network when compared to the shortest path origin–destination counterpart and suggest that the optimal hybrid truck platoon capacity is between four and six.
Owing to the continued development of e-commerce, logisticians now have an outstanding obligation to tackle last-mile delivery challenges. A number of logistics providers have suggested the ...incorporation of drones with trucks to provide a more flexible delivery system. This paper analyzes the content of 95 publications related to hybrid truck-drone delivery systems (HTDDS) in the context of last-mile delivery. First, a brief overview of the potential implementation of drone delivery systems is presented, including their integration with other vehicles. The overview aims to demonstrate the operational characteristics of such systems and their implications. Then, the surveyed literature is classified based on vehicles roles, system configuration, problem formulation, and solution methods. In relation to this research, several key findings and potential research directions are discussed. Despite the high level of interest in HTDDS research, it is still in its early phases and requires improvements in various areas. The payload capacity, speed, range, and energy consumption are all factors that must be considered in the modeling of drone characteristics. Almost all studies identify customer requests before the delivery operation begins. However, customer demands for immediate delivery present an opportunity for real-time optimization to provide solutions for e-commerce activities. Environmental issues are developing, as the last-mile delivery problem is regarded as the most polluting portion of the supply chain. Thus, more consideration should be given to the environmental impact of HTDDS. Finally, research on drone routing related to air traffic management has received relatively little attention.
This paper aims at investigating powertrain behaviour, especially in transient dynamic responses, using a nonlinear truck vehicle dynamic model with a parallel hybrid configuration. A power split ...control was designed to achieve the desired drivability performance, with a focus on NOx emissions. The controller was characterized by high-level model-based logic used to elaborate the total powertrain torque required, and a low-level allocation strategy for splitting power between the engine and the electric motor. The final task was to enhance vehicle drivability based on driver requests, with the goal of reducing—in a hybrid configuration—transient diesel engine emissions when compared to a conventional pure thermal engine powertrain. Different parameters were investigated for the assessment of powertrain performance, in terms of external input disturbance rejection and NOx emissions reduction. The investigation of torque allocation performance was limited to the simulation of a Tip-in manoeuvre, which showed a satisfying trade-off between vehicle drivability and transient emissions.
The paper describes a fully automated process to generate a shell-based finite element model of a large hybrid truck chassis to perform mass optimization considering multiple load cases and multiple ...constraints. A truck chassis consists of different parts that could be optimized using shape and size optimization. The cross members are represented by beams, and other components of the truck (batteries, engine, fuel tanks, etc.) are represented by appropriate point masses and are attached to the rail using multiple point constraints to create a mathematical model. Medium-fidelity finite element models are developed for front and rear suspensions and they are attached to the chassis using multiple point constraints, hence creating the finite element model of the complete truck. In the optimization problem, a set of five load conditions, each of which corresponds to a road event, is considered, and constraints are imposed on maximum allowable von Mises stress and the first vertical bending frequency. The structure is optimized by implementing the particle swarm optimization algorithm using parallel processing. A mass reduction of about 13.25% with respect to the baseline model is achieved.
•The impacts of higher weights and hybridization on fuel economy of heavy vehicle combinations were evaluated.•The specific fuel consumption can be significantly reduced with higher payload ...capacities.•The fuel economy can be improved up to 6% by hybridization.•The amount of hill climbing has a major impact on fuel economy.
This research evaluates the fuel economy of conventional and hybrid heavy vehicle combinations. The evaluation takes into account four heavy vehicle combinations with different total weights and three parallel hybrid configurations, which were developed for the tractor powertrain. The simulation models of conventional diesel powered and parallel hybrid vehicle combinations were developed in the Autonomie vehicle simulation software. Simulations were carried out in real-world operating routes that had been measured from popular truck routes in southern Finland. According to the simulations results, for one ton of additional weight to the total weight of the vehicle combination, the fuel consumption increases by 0.65–0.95l/100km depending on the operating route. The payload specific fuel consumption (the amount of fuel consumed per payload ton-kilometer) decreases on average 17% when total combination weight increases from 40t to 60t. The decrease is 23% when going from 40t to 76t and 28% when going from 40t to 90t. According to the simulation results, the fuel economy of a heavy vehicle combination can be improved by up to 6% by hybridization. The simulation results also indicate that the hybridization is more beneficial in operating routes which have more hill climbing.
The key point of the research is to improve the eco-driving performance in a hybrid truck among various types of drivers. An investigation relationship between the driver control parameters, driving ...performance and fuel economy has shown a potential to improve fuel economy and reduce the dispersion of fuel economy among various types of drivers by improving the vehicle longitudinal dynamics. Then, a hybrid vehicle controller consisting of an automatic gear controller and a hybrid torque distribution controller with a driving torque feedback compensator is designed. The automatic transmission controller calculates the optimal transmission gear ratio and the desired torque considering fuel economy. The hybrid torque distribution controller with a driving torque feedback compensator considering the torque response of hybrid system is designed. Finally, the effectiveness of the system is verified by simulations. The result indicates that the driving performance and fuel economy of the HEV was improved by using proposed hybrid control system.
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