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The increasing demand for improving fuel efficiency of marine transportation has presented opportunities for the development of the power management system (PMS). Different from the terrestrial power system, a shipboard power system contains a large proportion of propulsion loads, which has the characteristics of high dynamics, periodicity, uncertainty, and high dependence on the marine environment. The propulsion load fluctuation induced by sea waves, in-and-out-of water effects, and changeable consumers requirements, may lead to a low power quality and fuel efficiency and it brings challenges in the development of the marine PMS. In addition, the fluctuated load profiles could also be volatile and unpredictable, which makes long-term load forecasting unrealizable and real-time load forecasting essential. To address these issues, a real-time two-layer PMS is proposed for hybrid-powered ship in this article, that can maintain a high fuel efficiency and a healthy state-of-charge (SOC) level over the voyage even in extreme sea conditions. To adapt well to the fluctuated and changeable load condition, a novel multistep load forecasting system is integrated to make accurate load forecasting in a very very short-term time scale (centisecond). Multiple cases studies are conducted under different cases of voyage time, sailing speed, wave conditions, and submergence ratios. The results show that the proposed PMS can significantly reduce the power tracking delays, improve the fuel efficiency, and maintain a healthy SOC level.

When optimizing signals relevant traffic agencies adopt policies to either improve mobility performance measures (e.g., delay and stops), environmental aspects, or safety of signalized intersections. One of such policies, mainly implemented through so-called Performance Index (PI), advocates that reduction of excess fuel consumption (FC) should be achieved by minimizing the PI - a linear combination of delays and stops. The key factor of such a PI is the stop penalty “K”, which represents a weighting factor, or a stop equivalency measured in seconds of delay. In the contemporary signal optimization practice, this K is given a constant value (e.g., 10 seconds) and it is not recognized as a parameter that is dependent on various operational conditions. This study challenges that common view and presents a methodology to derive the K factor and investigate impacts of various operational conditions (e.g., cruising speed) on the K value. The study uses traffic simulation model coupled with a modal fuel consumption and emission model to investigate second-by-second FC during stopping events at a signalized intersection. The experiments are performed on a hypothetical, yet realistic, intersection under several operational scenarios. The findings show that the K varies significantly with all the investigated operational conditions. More importantly, results indicate that the K factor should be much larger than used by current signal timing practices. The implications of these findings may lead to significant changes in current polices for signal timing optimization. Future research shall validate these findings with second-by-second vehicle trajectory and FC data from the field.

The selfish routing of network flow often considers a single objective, namely travel time or travel distance, and optimisation models are often guided by the principle of user equilibrium (UE). A more challenging approach is to consider multiple objectives simultaneously, as for example distance, travel time and pollution. In this paper we present a bi-criteria problem whereby the road users selfish objective of minimising their travel time is at odds with the objective of minimising the overall fuel consumption of all road users. The approach taken is to manipulate “free” parameters, namely speed limits, in an attempt to coerce the road users into behaviour which helps the latter objective. Motivated by the work done on the Price of Anarchy (PoA) into classifying the suboptimality of equilibrium with respect to the minimum total travel time we look to classify the equilibrium solutions with respect to a weighted sum model of the minimum total travel time and overall fuel consumption. Our results show that small changes to these “free” parameters can result in solutions which Pareto dominate other solutions. Whilst our measure for the suboptimality of equilibrium solutions can highlight the inefficiency of a network configuration itself, it does not allow insight into the overall quality of the solution when compared with other network configurations.

Despite the tremendous growth of the transportation sector, the availability of systems that ensure safe, efficient, sustainable transportation reduces traffic congestion, maintenance costs, the off-road time of the vehicle, enhances driver's experiences, and ensures a more reliable journey are very limited. The fast evolution of our economy, lack of driver training, and the grown affordability of our society are reasons for this mismatch in developing economies. We think that the inconsistency will increase and unfavorably affect our traffic structure unless intelligent algorithm-based solutions are developed and deployed. This article presents a system for providing safe, accurate, comfortable, reliable, fuel-efficient, and economical driving behavior using Machine Learning techniques like the hidden Markov model (HMM). Our proposed system recommends subsequent trips using a multi-objective optimization (MOO) technique for the driver. It provides suggestions concerning speed limits and alerts based on the driver's behavior score and fuel efficiency. We used a publicly available UAH-DriveSet dataset captured by the driving monitoring app DriveSafe for all of our experiments. The results reveal that the proposed model predicts behavior with 95% accuracy and calculates fuel efficiency to improve driving quality and experience. This system recommends safer, more comfortable, more reliable, more efficient, and economical rides, beneficial for everyone in our society.

The increasing demands in modern combustion engines for low emissions and low fuel consumption improved methods for the cycle of combustion. The Beare Heads is a recent 6-hour version of the Beare Heads, recognized also as Malcolm Beare model. Beare Heads uses pistons and harbours such as a 2-hour motor and replacements for the overlap valve system of 4-hour motors. The four-stroke engine, block as well as shaft remain intact. The six-time engine was a mix of two-hit and four-hit technologies. Six Stroke is a six-stroke engine that uses two energy strokes. The six-time engine including external combustion as well as internal dual stream is the same as the real interior combustion engine, according to its mechanical design. Air heating system as well as a combustion chambers, each separate of the cylinder, are two extra chambers throughout the thermodynamic cycles. The six-stroke is heat efficient, since the power pulse pressure changes are larger than for the injection process as well as compression strokes. The major benefits of six-stroke engines included lowering fuel consumption by 40 percent, reducing emissions, versatility for multi-fuel service by 2 power strokes over the six-stroke cycles.

Cooperative Adaptive Cruise Control (CACC) systems can significantly improve traffic safety and roadway capacity utilizing short following gaps of vehicles enabled by inter-vehicle communications. However, due to merging processes occurring at freeway merging areas, existing CACC operation approaches are generally not applicable and the operation will have to revert back to Adaptive Cruise Control (ACC) or human-driven mode, which in turn will result in a capacity drop. This paper proposes an optimal trajectory optimization strategy for Connected and Automated Vehicles (CAVs) to cooperatively carry out mainline platooning and on-ramp merging. Firstly, a control framework of the CACC is adopted for a longitudinal control of CAVs, which helps individual CAVs to join platoons and to maintain platoon operations. Secondly, to ensure smooth lane-changing executions while achieving stable platoons, an optimal controller that considers lane-changing motivation of merging vehicles and impact of merging on platoons, is proposed. Third, a Legendre pseudo-spectral algorithm is applied to transform the controller into a simpler nonlinear programming problem and to efficiently solve it. Simulation assessments of the proposed method are conducted at both individual vehicle level and traffic-flow level. At the individual vehicle level, the proposed method has the potential to improve the traffic safety without compromising fuel consumption and emissions compared with unoptimized feasible schemes. At a traffic-flow level, an online evaluation platform is implemented, and a typical freeway on-ramp area is studied. The simulation results have demonstrated that the proposed controller provides significant improvements in terms of efficiencies in traffic operations.

This paper presents a hierarchical Green-Light Approach Speed (h-GLAS) strategy for controlling heavy-duty trucks traveling through urban/suburban environments, where future intersection signal phase and timing (SPaT) information is non-deterministic. Through vehicle-to-infrastructure communication, past SPaT information is sent to the vehicle and is used to forecast future predictions of the signal timing using a Gaussian Process (GP) model. The h-GLAS strategy uses the predicted SPaT information to generate an efficient desired velocity profile that navigates through intersections where the probability of a green light is maximized. This velocity profile is tracked by a convex model predictive controller (MPC) that simultaneously minimizes mechanical energy expenditure and braking effort over its prediction horizon. Downstream from the MPC, we implement a command governor (CG) that adjusts the MPC output by the minimum amount necessary to maintain safe vehicle following and ensure that the vehicle stops at all red lights. Using a proprietary medium-fidelity Simulink model provided by Volvo Trucks, we characterize the h-GLAS strategy's performance over a real suburban route, consisting of 10 actuated signalized intersections, using actual SPaT information provided by the NC Department of Traffic (DOT). Simulation results demonstrate a 30-43% reduction in fuel consumption, as compared to a baseline control strategy, which is attributable primarily to avoiding the massive energy losses associated with braking. Furthermore, we evaluate the computational efficiency of our approach by assessing average simulation execution times.

The rapid development of connected vehicles (CVs) has offered novel opportunities for eco-driving control. Considering inherent spatial and temporal constraints from the preceding vehicle, multiple signalized intersections, and queues, this paper proposes a hierarchical energy-efficient control strategy (HCS) in different domains to reduce fuel consumption and travel time. Considering both traffic lights and queue information, the concept of virtual traffic lights is proposed based on queue estimation. In the higher-level controller, a distance-based energy-economy velocity optimization problem is formulated to treat spatial constraints from virtual traffic lights and queues. The optimal velocity profile is solved by the direct multiple shooting algorithm in a model predictive control (MPC) framework, which is used as a reference by the lower-level controller. To treat temporal constraints of safe inter-vehicular time, a predictive cruise control (PCC) in the time domain is introduced in the lower-level controller to ensure safe inter-vehicle distances and improve fuel efficiency while tracking the reference speed. Comparative simulation results show that the proposed strategy can significantly reduce fuel consumption and travel time.

In the scope of the rapid technological advancements, nanoparticles (NPs) have gained prominence due to their excellent and tunable biological, and physicochemical properties. Nowadays, different methods are used for their synthesis. In particular, the green synthesis of metal precursors for the synthesis of NPs, represents a cost-effective, environmentally friendly, and hazardous chemical-free method for developing a large variety of NPs. By exploiting plant extracts, the production rate of NPs is relatively faster. Due to fossil reserves and high fuel consumption, renewable and clean energy materials are urgently needed to improve environmental sustainability. With outstanding electrochemical and physicochemical characteristics, molybdenum-based NPs (Mo-NPs) are gaining increasing attention in the fields of energy conversion and storage. Considering the significance of Mo-NPs synthesized from greener routes and their energy applications, it is necessary to review recent trends and developments in this field. This review summarizes important research studies and future research guidelines for the preparation of Mo-NPs through green routes and their applications to meet global energy and environmental demands. Moreover, future research directions are also highlighted to achieve sustainable greener precursors and Mo-NPs based energy storage devices.

Eco-driving incorporating multiple signalized intersections simultaneously has been proven to substantially benefit Connected Vehicles (CVs) in energy performance. However, ignoring the dynamic variation of waiting queues before downstream intersections may prevent CVs from following the obtained speed profile on security grounds. In this paper, the dynamic variation of waiting queue is modelled and predicted based on shockwave theory and data-driven based traffic flow prediction. To formulate the waiting queues as additional time-varying constraints for optimization problem, an extended traffic signal model is constructed based on the prediction. Furthermore, a hierarchical optimization framework is proposed, under which the hybrid optimization problem is decomposed into a discrete problem and a continuous one. Monte Carlo simulation demonstrates that if the proposed eco-driving approach is implemented, failure to follow the reference speed profile decreases by 79.4%. Also, the fuel consumption can be saved by over 4% compared with approaches ignoring the waiting queue.

In the recent past, platooning evolved into an attractive cooperative driving technology, broadly discussed in research and practice. Vehicles in platoons use cooperative adaptive cruise control to drive at close distances to each other. Platooning (i) increases the capacity of the street by a factor of 2; (ii) reduces the fuel consumption and emissions by up to 20%; and (iii) has social implications as it increases driver comfort and safety. As platooning research progresses, platooning coordination becomes a major research focus. The coordination of platoons, including the assignment of vehicles to platoons, the management of inter- and intra-platoon interactions, and the coordination of interactions with other vehicles is an important step towards an effective usage of platooning in practice. Based on a literature review of 1,600 papers, this survey provides an overview of state of the art in platooning coordination research for both cars and trucks. In this paper, we present a novel taxonomy for platooning coordination and classify existing approaches. We use the results of the literature review to discuss challenges and outline avenues for future work such as multi-objectiveness and individualisation.

This paper aims to design speed advisory profile for connected vehicles (CVs) at an isolated signalized intersection in a mixed traffic flow of CVs and human driven vehicles (HDVs). To consider the uncertainty of HDVs, we propose the concept of α -percentile trajectory of HDVs. We calculate the advisory speed of a CV that follows a HDV, assuming that the HDV moves along its α -percentile trajectory. We use dynamic programming to solve the optimization problem. Simulation experiments show that, roughly speaking, the benefits of fuel consumption and travel time would increase with the increase of inflow rate and market penetration rate (MPR) of CVs. The maximum benefits would be achieved at the smallest value of α when the traffic flow is undersaturated. However, when the traffic flow is oversaturated, the maximum benefits would be achieved at intermediate value of α provided the MPR of CVs is not large. With the further increase of the MPR of CVs, the benefits would become not so sensitive to α in the range α le 40%, but significantly decrease with the increase of α when α >40%. Finally, the impact of driver heterogeneity, inflow heterogeneity, tracking error and delay has been investigated.

An intelligent energy management strategy (EMS) based on an improved Reinforcement Learning (RL) algorithm is developed to enhance the adaptability of the EMS and to further improve the fuel efficiency of a Plug-in Parallel Hybrid Electric Vehicle (PHEV). Both the numerical model and the energy management strategy of a plug-in PHEV are described. The improved RL with Q-learning algorithm is implemented to acquire the optimal control strategies for improving fuel economy. The Markov Chain is employed to calculate the Transition Probability Matrix of the required power. A Kullback-Leibler (KL) divergence rate is designed to activate the update of EMS, when a new corresponding driving cycle is expected. An Exploration Factor (EF) is proposed to overcome the disadvantages of the normal RL algorithm in convergence rate and reward cost evaluation. The diverse KL divergence rates are examined to seek optimal solutions. The normal-RL strategy, rule-based strategy, and dynamic programming strategy are implemented as benchmark strategies to verify the effectiveness of the proposed strategy. The validation results indicate that the improved RL algorithm with EF makes it possible to promote the EMS capable of significantly improving the energy efficiency of a plug-in PHEV.

Inherent uncertainties of the air transportation system (ATS) can induce unexpected anomalies in its operations such as deviations in flight schedules, sudden imbalances of demands and capacities, etc.. Current air traffic flow management (ATFM) models rarely consider both demand and capacity uncertainties in their algorithms, and generally focus on minimizing the flight delays under deterministic constraints. Thus, to bridge this gap, we propose a framework for en-route ATFM while scrutinizing uncertainties in en-route capacity and demand and their imbalance, via a chance constraint based probabilistic approach. The proposed framework plays a key role in ensuring the safety of the overall ATS in terms of maintaining the safety separation between flights and constraining the capacity of the sectors as well. Moreover, flight level assignments scheme is proposed based on the Base of Aircraft Data (BADA) of the European Organization for the Safety of Air Navigation (EUROCONTROL) with the objective of minimizing the fuel consumption. The model further minimizes the overall expected delay of the system using the control actions of ground holding, speed control, rerouting, and flight cancellations. At the implementation stage, two phases of ATFM as pre-tactical and tactical are considered, in which the former focuses on generating optimal trajectories and the latter focuses on real-time updates of flight plans. The computational complexity is reduced by shrinking the feasibility region and decomposing the problem into maximum weighted independent sets. The experimental results of realistic large-scale problems demonstrate the effectiveness and computational feasibility of our ATFM framework.

This paper presents a distributed synchronization strategy for connected and automated vehicles in traffic networks. The strategy considers vehicles traveling from one intersection to the next as waves. The phase angle and frequency of each wave map to its position and velocity, respectively. The goal is to synchronize traffic such that intersecting traffic waves are out of phase at every intersection. This ensures the safe collective navigation of intersections. Vehicles share their phase angles through the V2X infrastructure, and synchronize these angles using the Kuramoto equation. This is a classical model for the self-synchronization of coupled oscillators. The mapping between phase and location for vehicles on different roads is designed such that Kuramoto synchronization ensures safe intersection navigation. Each vehicle uses a constrained optimal control policy to achieve its desired target Kuramoto phase at the upcoming intersection. The overall outcome is a distributed traffic synchronization algorithm that simultaneously tackles two challenges traditionally addressed independently, namely: coordinating crossing at an individual intersection, and harmonizing traffic flow between adjacent intersections. Simulation studies highlight the positive impact of this strategy on fuel consumption and traffic delay time, compared to a network with traditional traffic light timing., 12 pages, 5 figures, submitted to IEEE Transactions on Intelligent Transportation Systems

This study introduces a methodology for cooperative signal timing and trajectory optimization at intersections with a mix of connected automated vehicles (CAVs) and human-driven vehicles (HVs). We represent joint signal timing and trajectory control as a mixed-integer non-linear program, which is computationally complex. The developed methodology provides a balance between computational efficiency and solution quality by (a) linearizing the nonlinear constraints and reformulating the problem with a tight convex hull of the mixed-integer solutions and (b) decomposing the intersection-level program into several lane-level programs. Hence, a unique controller jointly optimizes the trajectories of CAVs on a lane and the signal timing parameters associated with that lane. This setting will allow finding near-optimal solutions with small duality gaps for complex intersections with different demand levels. Case study results show that the proposed methodology finds solutions efficiently with at most 0.1% duality gap. We compared the developed methodology with an existing signal timing and trajectory control approach and found 13% to 41% reduction in average travel time and 1% to 31% reduction in fuel consumption under different scenarios.

For a group of vehicles, collaborative platooning can be valuable in certain situations due to aerodynamic drag reduction, while being detrimental or altogether impractical in others. This paper details a platoon engagement/disengagement controller, which alternates between velocity trajectory optimization (VTO) in isolation and a fused platooning and VTO approach, capable of disengaging a platoon during segments detrimental to fuel savings and rejoining the platoon afterwards without significant energy expenditure. The proposed approach leverages parallel model predictive control (MPC) computations that (i) can identify when a platoon should be engaged/disengaged and (ii) performs the engagement/disengagement in a fuel-optimal manner. Using a medium-fidelity Simulink model furnished by Volvo, two real-world trucking routes, and two different traffic scenarios, the effectiveness of the approach was compared against non-platooning VTO, as well as a baseline controller that uses a PI-based cruise controller that incorporates Gipps car-following model. Results for a two-vehicle platoon using a 1 vehicle following distance reveal a 9.6% to 11.9% decrease in aggregate fuel consumption for both vehicles within the platoon, as compared to the baseline, highlighting the ability to disengage and rejoin a platoon without expending unnecessary fuel consumption. Additionally, the approaches for disengaging a platoon result in a 4-7% decrease in aggregate fuel consumption, as compared to a VTO-only approach.

The existing conventional traffic flow models aims to simulate human-driven following vehicles in real world. In this era of emerging transport solutions, controlling or intervening traffic flow to achieve high fuel efficiency along with good driving safety and travel efficiency becomes a reality. As such, it is worth exploring the possibility of developing eco-driving models to optimise vehicle movements for fuel consumption minimisation, while maintaining safety and efficiency. In this study, we propose a modified genetic algorithm (GA) based calibration method that enables the calibrated parametric traffic flow (car following) models to simulate or control vehicles in an eco-driving manner. By developing a novel objective function for the GA method based on the widely-used VT-Micro fuel consumption model, the proposed method can calibrate model parameters towards improving fuel efficiency. Besides, by subtly using heavy fuel consumptions as a surrogate index to represent low travel efficiency or dangerous driving strategies, the modified GA method with the novel objective function can guide the calibrated model towards achieving complete eco-driving requirements. Experimental simulation results further indicate that traffic flow models calibrated by the modified GA-based method can also alleviate traffic disturbances and oscillations in a more effective manner.

An accurate prediction of ship fuel consumption is critical for speed, trim, and voyage optimisation etc. While previous studies have focused on predicting ship fuel consumption with respect to a variety of factors, research on the impact of environmental factors on fuel consumption has been lacking. In addition, although recent research efforts have widely focused on machine learning methods to predict fuel consumption, studies on hyperparameter values that are suitable for these prediction models are limited. To compensate for this deficiency in existing literature, an adaptive hyperparameter tuning method is proposed, and the effects of maritime environmental factors on fuel consumption are taken into account. Through experimentation, the proposed adaptive hyperparameter tuning method was validated via artificial neural network (ANN), support vector regression (SVR), random forest (RF), and least absolute shrinkage and selection operator (Lasso). The hyperparameter tuning proportionally increased the amplitudes of the coefficients of determination (R2) of these algorithms. The increase of the amplitude demonstrated the following trend, in the order of the largest increase to the lowest increase: ANN, Lasso, SVM, and RF. The rates of increase were between 0.0773% and 2.1653%. Furthermore, after the environmental factors were considered, the prediction accuracies of the ANN and Lasso increased; however, the opposite was observed for the SVR and RF. As such, we confirmed that the use of Bayesian optimisation for hyperparameter tuning can effectively improve the fuel consumption prediction accuracy, and our proposed model can therefore serve as a significant reference for calculating fuel consumption.

In this article, we propose a multihorizon model predictive control (MH-MPC) approach with applications to integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs). The proposed MH-MPC leverages preview and optimization over a short receding and a long shrinking horizon, where the accuracy of preview, model, and integration can be different over different horizons. Compared with a conventional MPC-based approach with a short prediction horizon and terminal cost, the MH-MPC improves fuel consumption to a level comparable to dynamic programming (DP) while still being computationally affordable. A statistical sensitivity analysis over real-world city driving cycles is conducted to demonstrate the robustness of MH-MPC to moderate levels of uncertainty in the long-term preview.

We examine how behavioral adjustments by regulated vessels affect welfare outcomes of a local fuel sulfur standard targeting particulate matter pollution from maritime transport. Our analysis combines one-minute scale data of vessel locations with location-specific marginal damages to obtain voyage-level measures of welfare outcomes. Exploiting the introduction of California's Ocean-Going Vessel Fuel Rule, we find sharp reductions in fuel consumption in the regulated area and a considerable emission spillover in unregulated waters. Despite these adjustments, the rule generates net benefits of close to $1 billion over 29 months because the emission spillovers occur in low marginal damage areas.

The operation of connected platooning vehicles (CPVs) with V2V and V2I techniques has the potential to decrease the overall aerodynamic drag and reduce fuel consumption. One of the key technologies for CPVs is the spacing control which is heavily dependent on the drag force. However, the existing studies on drag coefficient of CPVs seldom pay attention to heterogeneous platoon, and most analysis only consider the effect of vehicle spacing and ignore key information such as cross-sectional area of adjacent vehicles. In addition, most research only focuses on qualitative research, without concluding a quick formula for calculating vehicle drag coefficient in a platoon. In this work, we investigate the modeling and estimation of drag coefficient in terms of the inter-vehicle distances, the platoon configurations, and the cross-sectional area of the adjacent vehicles. We calculate the drag coefficients for ten categories of vehicles with different inter-vehicle distances via numerical analysis in the software FLUENT. According to hundreds of the analysis results, we propose a model to estimate the drag correction factor online. A hybrid algorithm combining the BP neural network (BPNN) and particle swarm optimization (PSO) is employed to optimize the parameters in the model. The model is validated via three kinds of regression evaluation indexes and additional simulation tests with different platooning configurations and different types of vehicles. In terms of the comparison results, the developed model is effective to estimate the drag coefficients of CPVs.

In order to improve eco-driving performance, a speed profile for a vehicle has been investigated. For its great effect on fuel economy, an economic speed planning method based on iterative dynamic programming (IDP) with a high calculation efficiency is proposed for slope driving. Taking fuel economy as an optimized goal, a transient fuel consumption model is developed, which plays an important role in the economic evaluation of fuel consumption. Moreover, a discrete form of the speed planning model is established based on the vehicle dynamics equations. Based on the dynamic programming (DP) algorithm, the calculation complexity is analyzed in detail, and the fuel-saving characteristics of the DP algorithm are verified in a comparative experiment with the fixed-speed cruise control (FCC) method and the velocity set-point (VS) method. To overcome the low calculation efficiency of the DP algorithm, a new algorithm based on iterative dynamic programming is proposed to reduce the size of search domain. Furthermore, an improved IDP (i-IDP) algorithm with variable step size is proposed, which further optimizes the performance of IDP in terms of distance step size. Additionally, the calculation accuracy is also guaranteed while reducing the calculation scale and improving the efficiency of speed planning. What the calculation results have shown that under the same level of accuracy, the new algorithm can save the calculation time by more than 90%, with possibilities for real-time speed profile optimization for vehicles.

This study presents a fuel-economical driving strategy for connected and automated vehicles (CAVs) and investigates its impacts on human-driven platoon's fuel efficiency, considering driver behavioral diversity. In mixed traffic where a limited number of CAVs and many human-driven vehicles share the road, the Eco-Driving strategy of CAVs, accomplished through vehicle connectivity and longitudinal dynamics control, can significantly reduce fuel consumption of CAVs, particularly during transient traffic conditions, by avoiding unnecessary braking and acceleration maneuvers which lead to excessive fuel consumption. This is implemented and validated by a vehicle longitudinal dynamic control design with an instantaneous power-based fuel rate estimation model. Moreover, such an Eco-Driving strategy will also help most of the following human-driven vehicles to improve fuel performance despite a highly diverse and uncertain driving characteristics spectrum. The car-following behaviors of a human-driven platoon are described by a microscopic traffic model with dedicated representations of the individual drivers' preferences. A comprehensive statistical investigation shows the fuel-saving benefits for a human-driven platoon by adopting the proposed Eco-Driving strategy for CAVs. Some special cases are discussed, as well. The results collectively demonstrate the positive impacts of CAVs, even with a low penetration rate, could potentially have on the energy efficiency of the transportation sector within the foreseeable future.

Improvement of PHEV equivalent fuel consumption and battery life are significant issues in the design of PHEVs. This research aims to develop an adaptive traffic-based controller to improve the fuel economy and battery life. The optimal SOC trajectories in real-world and standard cycles are extracted using modified dynamic programming (MDP) approach. The optimal data is clustered based on the driving features, under the light of fuzzy clustering. Then, a control map is formed based on the clustered data to estimate SOC trajectories as reference in an adaptive control strategy. A battery life model is employed to investigate the effect of proposed method on the battery aging. This study reveals that the traffic-based SOC management simultaneously reduces the operating and maintenance costs since it improves the equivalent fuel economy and battery life in various driving cycles. Moreover, the suggested energy management strategy is implemented on an embedded system by applying model-based design (MBD) method. The comparison of Hardware-In-the-Loop (HIL) and Model-In-the-Loop (MIL) simulation shows that the suggested strategy is implementable and effective for real applications.

The logistics of maintaining a fuel supply is a significant consideration for the military. Members of the North American Treaty Organization (NATO) prefer to use a single fuel to avoid problems with fuel transport and storage. Thus, we chose the kerosene-based F-34 jet fuel as the “single fuel” for testing on a land-based vehicle. We examined the feasibility of using F-34 jet fuel in a diesel engine and compared the performance of the jet fuel with a conventional F-54 diesel fuel. We compared the power and torque characteristics of an M52 diesel engine, as well as fuel consumption as a function of engine speed and torque using F-34 jet fuel and F-54 diesel fuel. Moreover, wear of the fuel pumps in Magirus Unimog and MAN trucks was measured during long-term tests operating with F-34 and F-54 fuels, respectively. We found that there was no obvious difference in engine power and torque using F-34 or F-54 fuel.

This paper proposes a battery health-aware and deep reinforcement learning (DRL)-based energy management framework for power-split hybrid electric vehicles in a naturalistic driving scenario. First, based on the data collected from the actual traffic flow, a data-driven method is used to establish driving scenarios that reflect different driving patterns and behaviors. Second, the expert knowledge is embedded into the deep deterministic policy gradient (DDPG) to achieve faster convergence with the guaranteed vehicle performance. Third, the superiority of the control strategy is achieved by optimizing the trade-off among fuel consumption, battery aging cost and SOC sustainability penalty under different weight coefficients, and verified by comparison with the existing state-of-the-art strategies including the deep Q-network (DQN) and dynamic programming (DP). The results show that the proposed strategy can slow down battery aging by lowering the operating severity factor with minimal fuel economy penalty while remaining accelerated iterative convergence compared with DQN. The benefits of proposed strategy become very evident when the vehicle is driving under the high power demand and it has good stability to cope with the change of operating conditions.

The increasing demands for reducing greenhouse emissions and improving fuel efficiency of marine transportation have presented opportunities for electric ships. Due to the complexity of multiple power resources coordination, varied propulsion loads, changeable economical, and environmental requirements, power/energy management system (PMS/EMS) becomes essential in both designing and operational processes. The existing literature on PMS/EMS can be categorized into rule-based and optimization-based approaches. Compared to the rule-based PMS/EMS, which relies heavily on human expertise, as well as predefined strategies and priorities, the optimization-based approaches can offer more efficient solutions and are more widely used nowadays. This article provides a comprehensive review of the marine optimization-based power/energy management system and discusses the future trends of PMS/EMS in ship power systems.

This paper proposes a conceptual methodology for designing vertical takeoff and landing aircraft with a hybrid-electric propulsion system as an alternative to facilitate intercity missions for urba...

To optimize the fuel economy of parallel hybrid electric vehicles (HEVs), improve the working conditions of the engine, and promote the application of deep reinforcement learning (DRL) in the field of energy management strategies (EMSs), this paper firstly proposed a Deep Reinforcement Learning-based EMS combined with an engine start-stop strategy. In addition, considering both the engine and the transmission are controlled components, this paper developed a novel Double Deep Reinforcement Learning (DDRL)-based EMS, which uses Deep Q-Network (DQN) to learning the gear-shifting strategy and uses Deep Deterministic Policy Gradient (DDPG) to control the engine throttle, and the DDRL-based EMS realizes the multi-objective synchronization control by intelligent algorithms. After offline training, the simulation result of the online test shows that the fuel consumption gaps of the proposed DRL-based and DDRL-based EMS are -0.55% and 2.33% compared to that of the deterministic dynamic programming (DDP)-based EMS by overcoming some inherent flaws of DDP, respectively. Moreover, the computational efficiency is improved significantly, and the average output time per action is 0.91ms. Therefore, the control strategy that combines learning-based and rule-based control and the further multi-objective control strategies can achieve good optimization effects and application efficiency.

The use of nanoparticles in fuels provides new opportunities for modification of fuel properties, which may affect the operational parameters of engines, in particular the efficiency and fuel consumption. The paper presents comparison of compression ignition engine performance fuelled with neat diesel and nano-diesel. Alumina (Al2O3) was used as nanoparticles. Surface-active substances, including Span 80 surfactant, as well as water admixture were used to improve the stability of the produced fuel. Measurements of the thermal conductivity and dynamic viscosity of the produced mixtures were conducted. In this study was used naturally aspirated, water cooled, four-stroke diesel engine. Addition of Al2O3 nanoparticles result in 4% reduced fuel consumption, addition of TiO2 nanoparticles result in 10% reduced fuel consumption with respect to neat diesel fuel.

Aircraft taxiing in large airports is often delayed by traffic conflicts. The increased fuel consumption and pollutant emission lower the efficiency of airport surface operation and bring potential...

Cooperative automotive platooning can improve safety and efficiency on the road. Look-ahead control of an entire platoon allows to reduce fuel consumption and travel time in open road scenarios, but dense traffic requires continuous adaptation of far-sighted plans. To achieve efficient individual vehicle control, these control systems need to be informed appropriately. For this purpose a novel concept for distributed model predictive control of the platoon vehicles is proposed which safely allows dense spacing and keeps communication requirements small while being robust against communication loss. A safety-extension separates safety constraints from the design of the tracking control goals and enables agreed-upon behavior in terms of temporarily limited decelerations. Driving corridors based on position errors are utilized to select suitable control modes or trigger prediction updates to following vehicles. Realistic vehicle dynamics co-simulations demonstrate the platoon safety and performance in selected scenarios, including emergency braking and maneuver tracking subject to traffic disturbances. The proposed measures are effective with realistic model errors, provide implicit collision safety and show string stability with low communication requirements.

This paper examines the effect of an external preheating system for an internal combustion engine on fuel consumption, CO2 emissions, and cabin temperature of a Euro4 vehicle. A 1 kW electric system powered by 220 V was installed in series in the cooling system of a vehicle with a compression-ignition engine of 2.5 dm3 capacity. The tests were carried out in simulated urban driving conditions (distance of 4.2 km), extra-urban driving conditions (distance of 17 km), and during idling at cold-start temperatures ranging from -10 oC to 2 oC. Preheating the engine under simulated city conditions reduces fuel consumption by 2.64 dm3/100 km and increases the supply air temperature immediately after engine start-up. Due to the preheater being powered from an external power grid, the cost per trip and total CO2 emissions are increased. Assuming renewable energy sources, CO2 emissions would be reduced the most for the stationary tests after engine preheating. In contrast, emissions would be reduced the least for extra-urban driving.

This study focuses on the development and analysis of a real-time updated operations strategy of a distributed energy system (DES). Owing to the relevant Chinese policy of electrical transmission and distribution, combined cooling, heating, and power system (CCHP) and photovoltaic (PV) systems are not currently allowed. However, with the Chinese supply-side power grid reform, the permissions for connections between DESs and utilities are gradually evolving. By performing building simulation and using mixed integer linear programming (MILP), a real-time updated operation strategy of a DES is established. Then, considering the DES from Tianjin Eco-city as a case study, a comparative analysis between this updated strategy and the current operation strategy is performed by evaluating three factors: economic efficiency, energy consumption, and CO2 emission. The results show that the updated strategy can reduce 29.12% of electricity time-of-use cost, 10.11% of total fuel consumption, and 18.40% of CO2 emission during the cooling season. Besides, a method of “rolling load forecasting” for DES by using Support vector regression machine (SVR) is proposed and discussed. The testing shows that the Mean Absolute Percentage Error (MAPE) is below 7.5%. And when the training sample is large, the particle swarm optimization algorithm can be used to shorten the modeling time of the air conditioning load forecasting model.

Due to incredibly challenging social, economic, technical, and environmental aspects, decision-making must evaluate several conflicting agendas. Therefore, rule-based decision built on a single criterion is no longer capable of dealing with these issues. Generators, especially silent ones, are essential for industries, hospitals, schools, colleges and hotels. However, silent Genset (ŚĠ) of different companies and capacities exist in the market with various attributes/criteria such as power consumption, fuel tank capacity, fuel consumption etc. So, it becomes difficult for the firms/purchaser/wholesaler/retailer to pick the most appropriate ŚĠ. So, in this present work, multi-criteria decision making (MCDM) methodologies VIKOR (Vlsekriterijumsko kompromisno Rongiranje) and WASPAS (Weighted Aggregated Sum Product Assessment) are utilized to rank the different alternatives available in the market. First, the weights of importance to ŚĠ sets are applied identically and with objective weights. Then, the objective weights are assigned with the entropy weight method (ĖŴΜ) without the decision maker’s choice. Finally, the ranks obtained with VIKOR and WASPAS are displayed. Furthermore, more alternatives and criteria can be considered with varying MCDM techniques of weighting and ranking. As a result, for industrial engineers who play a crucial part in decision-making, the provided approach for Genset selection is convenient, practical, and productive.

Jaggery, an unrefined sweetener is prepared by continuous heating of fresh sugarcane juice in different types and capacities of old indigenous and modified plants. The old indigenous plants manufactured by the rural people without any technological advancements have very high fuel consumption (about 3 kg/kg to 4 kg/kg of jaggery produced) and low heat utilization efficiency (15% to 29%). Number of modifications have been suggested by the researchers for the optimization and improvement in the performance parameters of jaggery making processes and plants. The implementation of fins at the external bottom of pans, preheater and economiser in the chimney, efficiency booster in the furnace, freeze pre-concentration system, forced draft through the plant and controlled fuel feeding in the furnace are reported to improve the thermal efficiency in the range of 25% to 50% and reduce the bagasse consumption in the range of 1.75 kg/kg to 2.75 kg/kg of jaggery prepared. The recently published article adopting novel techniques of internal preheating of sugarcane juice in the gutter pan and automatic supply of bagasse in the furnace of a two pan jaggery making plant is claimed to improve the heat utilization efficiency up to 65% and reduce the fuel consumption up to 1.50 kg/kg of jaggery produced with an affordable investment. The present paper summarizes the various technological up-gradations adopted and tested by the researchers for the enhancement of various output parameters of jaggery making units.

Hybrid powertrains have widely been developed as eco-friendly system and commercialized in the passenger vehicle market with clear benefits over conventional powertrains. Accordingly, there have been various research topics on architectures of hybrid power systems to further improve the system performance ability and sizing optimization for the packaging and production cost reduction. In this study, a novel multimode power split hybrid architecture has been suggested, which is based on multiple driving modes such as input- and output-power split and parallel hybrid modes in order to achieve fuel efficiency improvement and sizing optimization. The performance ability and sizing aspects of the invented system have been analyzed in comparison with Toyota Hybrid System (THS), which is a kind of typical power split hybrid architecture. The fuel efficiency of the suggested system has been compared by a backward-facing simulation with Dynamic Programming (DP) for representative driving test cycles from Environmental Protection Agency (EPA). In terms of the component sizing, the maximum torque and speed variation trends of motors have been analyzed according to the velocity variation. In the simulation and analysis results, the invented system shows opportunities to improve fuel efficiency with multiple driving modes and to reduce component sizing of power electronics which is related to the production cost reduction as well as the vehicle packaging space minimization.

The article presents the results of the development and testing of a prototype device for the combustion of liquid fuels and the disposal of liquid toxic waste based on structured microfiber catalysts. The technology is based on a two-stage approach combining homogeneous and catalytic oxidation, which is carried out in a structured spiral cartridge with a modified glass fiber platinum-containing catalyst IC-12-S111. A prototype of a catalytic device with a thermal power of 18 kW was created and tested on a model fuel (diesel fuel). The optimal combustion mode is realized at a fuel consumption of 1.8 L/h, an air flow supplied to the burner - 12.3 st.m3/h and to the mixing zone in front of the catalytic block - 67 st.m3/h. At the same time, the air leaving the device contains practically no toxic impurities (CO, nitrogen and sulfur oxides). An important advantage of the proposed technology is its modularity, that is, the possibility of implementation in the form of low-power mobile devices, from which it is possible to form transportable and quickly deployable systems of any total capacity and a wide range of purposes.

The industrial sector is one of the biggest oil consumers in Cyprus, corresponding to 20% of the fuel consumption. A parabolic trough collector (PTC) system seems the best option to produce clean thermal energy at higher temperatures than those that can be achieved from the already widely used flat plate collectors on the island. This paper presents a simulation model built to investigate the performance of the first industrial PTC system in Cyprus, installed at the Cyprus biggest soft drinks factory. The simulation model is built in TRNSYS and is validated using data from the real PTC system installed at the factory. The results show a very good fitting between the operating parameters and the power output of the Solar Field (SF), Concrete Thermal Storage System (CTES), and the Steam Generator (SG). The average percentage relative error of the system's contribution to the process is less than 6.32% for the daily steam production and during a week did not exceed 6.45%. The novelty of this study is the development of a design tool that can be used by potential interested industries to identify the suitable system that fits their needs. All data are provided in the form of graphs and allow anyone to use as input data the thermal energy demand and required steam temperature of the industry to retrieve information about the size of a suitable system which satisfies these requirements depending on each case. The payback period for all cases examined varies from 2 to 6 years, depending on the size of the system.

In this paper, role of heavy duty vehicles in contributing pollution is addressed with identified areas of improvement for enhancing their existing fuel efficiency. Potentials of thermo-electric generator (TEG), piezoelectric generator and regenerative braking are studied. It is suggested to develop integrated solution having all three technologies to harvest the waste energy through various means. This solution brings the individual little energy harvested through the three discussed solutions together and supply back the significant amount of energy back to the HDV’s. Combined energy harvest is suggested to optimize the cost involved and reduce the losses as well as maintenance expenses by indirectly increasing the life of associated components of the vehicles. These technologies have several limitations related to efficiency and materials which may further get improvised with the research in future.

Future asteroid landing and sample return missions will seek for landing sites with high scientific value, such as hazardous terrains, which means that the spacecraft needs the ability of autonomous hazard avoidance. Nowadays, the guidance algorithm based on artificial potential function plays an increasingly important role in hazard avoidance, but the local minimum problem makes that the spacecraft cannot reach the desired target landing point in some complex terrains. In this paper, a novel hazard avoidance guidance method which improves the traditional artificial potential function is developed. This method focuses on the impact zone analysis of hazards and defines anti-collision zone. In addition, a new repulsive potential function in logarithmic form is designed with continuity and fast numerical change rate in the anti-collision zone on this basis, and the problem of spacecraft falling into local minimum zone in complex terrains can be effectively avoided. Considering the practical application of constant thrust engine, the hazard avoidance control law with constant thrust sliding mode is designed to reduce switching frequency and fuel consumption. The performance of the proposed guidance law is verified through a set of simulations, as well as the global stability of the control system under uncertainty and perturbation conditions.

Hybrid–electric powertrains offer the potential for performance improvements in unmanned aerial systems. However, for small unmanned aerial systems, potential gains in range and endurance can depen...

Biodiesel from mahua is a promising source of energy in different forms like oil, gas, and char at present and for the future as well. Nations like India, which have adequate environmental conditions favouring the mahua crops, can produce oil from mahua, and this energy can be used in many efficient ways. This is an experimental approach made to test the engine performance of mahua biodiesel with nano metal oxide additives for its combustion and emission characteristics. The tests were conducted on a BSII commercial, single-cylinder, four strokes, water-cooled diesel engine. The test and operating conditions were set stringent, to generate the real-time environment. Tests were conducted at varying engine loads for determining the performance parameters like brake thermal efficiency, brake specific fuel consumption and emissions parameters like unburned hydrocarbons, CO2, CO and NOx. The results obtained from the tests showed mahua biodiesel has a higher fuel consumption rate which can be normalized to diesel by adding aluminium oxide. Moreover, improved brake thermal efficiency and reduction in ignition delay was achieved by adding nano metal oxides additives in varying proportions.

Concern over growing fossil fuel depletion has led to the search for renewable fuels with efficiencies comparable to those seen today for conventional fuels. The present article discusses the potential and opportunities to be used in various applications for alternative fuels. The properties of these fuels and their fuel efficiency are discussed in detail. Vegetable oils provide a very positive scenario for fossil fuels to be used as alternatives. The use of biodiesel leads to a major reduction of unburned hydrocarbons, carbon Monoxide, particulate matter and nitrogen oxides in traditional diesel engines. Different alternative fuels have been compared to traditional fuels and it is clear that the use of these fuels can be greatly reduced by the use of blended fuels. Many alternative fuels have properties close to those of conventional fuels, so it is already well known the technologies needed to deal with them. Hydrogen is an exception, and its ignition characteristics differ considerably from those of conventional fuels. Therefore, difficulties in the storage, transport and use of hydrogen as a fuel are important. Therefore, difficulties are essential in storing, transporting and using hydrogen as fuel. There is also discussion of other technologies, such as alternate routes to conventional fuel alternatives, such as CTL. The discussion on emulsified oils, including the use of oxygenates, eventually took place.

The growing price of fossil oils, the gradual decline of petroleum supplies and the unsustainable emissions of motor vehicles led researchers to look for substitute fuel, such as waste lubricant oil and waste transformation oil (WTO). In power transformers, welding transformers, and electromotive systems, a significant volume of transformer oil is used as a coolant. The electrical transformer is an integral part of electrical power transmission and distribution equipment used in smaller, intermediate and larger electrical distribution stations. The efficiency and lifespan of an electrical transformer depend upon the transformer’s efficient cooling and insulation. During the working of a transformer, transformer oil is exposed to electrical and thermal stress due to which it started to degrade after some time. Thus a huge amount of degraded transformer oil is rejected as waste. The paper details the utility of waste transformer oil by using simple batch distillation operation at atmospheric pressure by analyzing the physiochemical properties of the product. The treated transformer oil is further tested in the diesel engine to examine fuel efficiency. Diesel engine runs were performed with various combinations of blends consists of diesel fuel and treated transformer oil. Results reveal that brake thermal efficiency of the blends found at 10%, 20%, 30% and 40% is 28.19%, 27.52% 26.21% and 25.98% respectively. The brake specific fuel consumption of blends at 10%, 20%, 30% and 40% is 0.29 KJ/Kwhr, 0.30 KJ/Kwhr 0.31 KJ/Kwhr and 0.315 KJ/Kwhr respectively.

Heavy duty vehicles e.g., trucks are one of the important mediums of ground transportation in every sector like defence, road transport etc. which provides immediate support whenever required. Innovative technological development in the area of automobile has been growing very rapidly, especially in the developing scenario. This development leads to the requirement of reducing the weight of the vehicle specially the chassis frame which serves as a skeleton to provide the proper structural safety to a vehicle & hence affects the vehicle’s performance along with the requirement of fuel consumption. It is a critical part in the vehicle, and various sorts of failures happen because of static loading conditions. Decreasing the heaviness of the vehicle parts is an ideal answer for the issue. The presented work focuses on optimizing a heavy-duty TATA 1612 truck chassis (ladder type frame) by substituting its material from conventional steel with potential metal matrix composite light weight Graphite Al GA 7–230 MMC material without compromising the safety and strength. CAD modelling & structural analysis is accomplished on the modelled frame using ANSYS and optimal space filling design scheme of response surface method respectively. Analysis shows around 70% reduction in mass of chassis and thus subsequently affect the functioning of the vehicle in a positive approach.

The European transportation sector employs 10 million people and accounts for 4.6 % of the European Union GDP. Due to climate change, this workforce is increasingly affected by high temperatures and radiant loads, particularly during summer. They rely on air conditioning (AC) to minimize heat inside the truck cabins, increasing fuel consumption and tailpipe emissions. Because sustainable transportation is crucial for climate change mitigation, we developed a numerical investigation on the dynamic thermal exchanges of cabins of heavy-duty trucks in realistic conditions of a summer workday, to quantify the potential impact of interventions in the glazing and paint optical properties, over the truck AC loads. We observed that the changes in air temperature and solar irradiation throughout the workday imply substantial variations in the truck���s AC loads and, consequently, in its fuel consumption and tailpipe emissions. Furthermore, windshields and side windows with transmissivity of 0.33 instead of typical 0.79 and 0.84, respectively, can reduce AC loads by up to 16 %. External paints with reflectivity of 0.70 instead of 0.04 can reduce the AC loads by up to 30 %, whereas cumulative changes to glazing and paint can reduce the AC load by up to 40 %. These interventions can lower fuel consumption and emissions by up to 0.4 %. These results show that important improvements in fuel efficiency and tailpipe emissions are possible, if the research community, policy makers and industry stakeholders successfully promote the adaptation of the European transportation fleet.

This chapter describes a system which recovers waste heat to be used for baking, improving the fuel efficiency of the oven by at least 15%. The heat recovery system uses heat from the burner flues. This may be used to heat one or two final zones of the oven. These zones would not require burners, giving a saving in capital and running costs.