Reducing Electrical Consumption in Stationary Long-Haul Trucks

On average, long-haul trucks in the U.S. use approximately 667 million gallons of fuel each year just for idling. This idling primarily facilitates climate control operations during driver rest periods. To mitigate this, our study explored ways to diminish the electrical consumption of climate control systems in class 8 trucks through innovative load reduction technologies. We utilized the CoolCalc software, developed by the National Renewable Energy Laboratory (NREL), which integrates heat transfer principles with extensive weather data from across the U.S. to mimic the environmental conditions trucks face year-round. The analysis of the CoolCalc simulations was performed using MATLAB. We assessed the impact of various technologies, including white paint, advanced curtains, and Thinsulate insulation on reducing electrical demand compared to standard conditions. Our findings indicate that trucks operating in the eastern U.S. could see electrical load reductions of up to 40%, while those in the western regions could achieve reductions as high as 55%. Such significant decreases in energy consumption mean that a 10 kWh battery system could sufficiently manage the HVAC needs of these trucks throughout the year without idling. Given that many long-haul trucks are equipped with battery systems of around 800 Ah (9.6 kWh), implementing these advanced technologies could substantially curtail the necessity for idling to power air conditioning systems.

A discrete-event model to simulate the effect of truck bunching due to payload variance on cycle time, hauled mine materials and fuel consumption

Data collected from truck payload management systems at various surface mines shows that the payload variance is significant and must be considered in analysing the mine productivity,energy consumption,greenhouse gas emissions and associated cost.Payload variance causes significant differences in gross vehicle weights.Heavily loaded trucks travel slower up ramps than lightly loaded trucks.Faster trucks are slowed by the presence of slower trucks,resulting in‘bunching’,production losses and increasing fuel consumptions.This paper simulates the truck bunching phenomena in large surface mines to improve truck and shovel systems’efficiency and minimise fuel consumption.The study concentrated on completing a practical simulation model based on a discrete event method which is most commonly used in this field of research in other industries.The simulation model has been validated by a dataset collected from a large surface mine in Arizona state,USA.The results have shown that there is a good agreement between the actual and estimated values of investigated parameters.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption

The mining industry annually consumes trillions of British thermal units of energy,a large part of which is saveable.Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source.Cross vehicle weight,truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption.In this paper,an artificial neural network model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight,truck velocity and total resistance.The network was trained and tested using real data collected from a surface mining operation.The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage parameters considered in this study.

Application of Solid Oxide Fuel Cell-Auxiliary Power Unit on Different Trucks in Northeast China

A diesel engine of conventional trucks has a low efficiency under the idling condition,leading to a high cost for heating or cooling in the cab during night. The solution to this problem will have great significance on energy conservation and emission reduction. A new auxiliary power unit of solid oxide fuel cell( SOFCAPU) with high efficiency solves this problem perfectly. Heat pump air conditioner is considered as a promising device for the application of SOFC-APU with a high cooling and heating efficiency. To make a quantitative analysis for the application of SOFC-APU,a model is built in Matlab / Simulink. The diesel engine model and SOFC-APU model are fitted based on some experimental data of SOFC-APU and diesel engine during the idling operation. An analysis of the application of SOFC-APU on different trucks in Northeast China is comprehensively made,including efficiency and emission.

Chinese company launches hydrogen fuel cell heavy-duty truck

Beiben Trucks Group Co.,Ltd.,a Chinese heavy-duty truck producer,has launched a new vehicle powered by 100-kilowatt hydrogen fuel cells.The truck,to be used as a street sweeper,rolled off the production line on Monday in Beiben's plant in the.city of Baotou in north China's Inner Mongolia Autonomous Region.

Route Towards Road Freight Electrification in India:Examining Battery Electric Truck Powertrain and Energy Consumption

Medium-duty/heavy-duty trucks(MD/HDTs)are yet to be included in India’s electric mobility plans.With the improvement of electric vehicle(EV)technologies,there is a growing interest in battery-electric trucks(BETs)from original equipment manufacturers(OEMs).The time is opportune to consider electrification as a future direction for road freight in India.Accordingly,this article presents the results of an energy consumption simulation study of a BET under Indian conditions.This study specifically considered an MDBET over a domestic drive cycle.These energy consumption figures can facilitate future studies that analyze the technical and practical feasibility of BETs in the country.In addition,the article provides the requisite groundwork for BET modeling for a simulation study by reviewing available EV powertrain systems and components.Appropriate powertrain considerations are thereby obtained for a typical medium-duty/heavy-duty battery-electric truck(MD/HDBET)in the Indian context.

Optimization of Engine Control Strategies for Low Fuel Consumption in Heavy-Duty Commercial Vehicles

The reduction of fuel consumption in engines is always considered of vital importance.Along these lines,in this work,this goal was attained by optimizing the heavy-duty commercial vehicle engine control strategy.More specifically,at first,a general first principles model for heavy-duty commercial vehicles and a transient fuel consumptionmodel for heavy-duty commercial vehicles were developed and the parameters were adjusted to fit the empirical data.The accuracy of the proposed modelwas demonstrated fromthe stage and the final results.Next,the control optimization problem resulting in low fuel consumption in heavy commercial vehicles was described,with minimal fuel usage as the optimization goal and throttle opening as the control variable.Then,a time-continuous engine management approach was assessed.Next,the factors that influence low fuel consumption in heavy-duty commercial vehicles were systematically examined.To reduce the computing complexity,the control strategies related to the time constraints of the engine were parametrized using three different methods.The most effective solution was obtained by applying a global optimization strategy because the constrained optimization problem was nonlinear.Finally,the effectiveness of the low-fuel consumption engine control strategy was demonstrated by comparing the simulated and field test results.

The Effect of Lateral Offset Distance on the Aerodynamics and Fuel Economy of Vehicle Queues

The vehicle industry is always in search of breakthrough energy-saving and emission-reduction technologies.In recent years,vehicle intelligence has progressed considerably,and researchers are currently trying to take advantage of these developments.Here we consider the case of many vehicles forming a queue,i.e.,vehicles traveling at a predetermined speed and distance apart.While the majority of existing studies on this subject have focused on the influence of the longitudinal vehicle spacing,vehicle speed,and the number of vehicles on aerodynamic drag and fuel economy,this study considers the lateral offset distance of the vehicle queue.The group fuel consumption savings rate is calculated and analyzed.As also demonstrated by experimental results,some aerodynamic benefits exist.Moreover,the fuel consumption saving rate of the vehicle queue decreases as the lateral offset distance increases.

Research on ECMS Based on Segmented Path Braking Energy Recovery in a Fuel Cell Vehicle

Proton exchange membrane fuel cells are widely regarded as having the potential to replace internal combustion engines in vehicles.Since fuel cells cannot recover energy and have a slow dynamic response,they need to be used with different power sources.Developing efficient energy management strategies to achieve excellent fuel economy is the goal of research.This paper proposes an adaptive equivalent fuel minimum consumption strategy(AECMS)to solve the problem of the poor economy of the whole vehicle caused by the wrong selection of equivalent factors(EF)in traditional ECMS.In this method,the kinematics interval is used to update the equivalent factor by considering the penalty term of energy recovery on SOC changes.Finally,the optimized equivalent factor is substituted into the optimization objective function to achieve efficient energy regulation.Simulation results under the New European Driving Cycle show that compared with the traditional ECMS based on fixed SOC benchmarks,the proposed method improves fuel economy by 1.7%while ensuring vehicle power and increases SOC by 30%.

Optimization management of hybrid energy source of fuel cell truck based on model predictive control using traffic light information

Energy optimization management can make fuel cell truck(FCT)power system more efficient,so as to improve vehicle fuel economy.When the structure of power source system and the torque distribution strategy are determined,the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources.The paper simulates the assistance of the intelligent transport system(ITS)and carries out the eco-velocity planning using the traffic signal light.On this basis,in order to further improve the energy efficiency of FCT,a model predictive control(MPC)-based energy source optimization management strategy is innovatively developed,which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption.Under the scenarios of signalized intersections,based on the planned eco-velocity,the off-line simulation results show that the proposed MPC-based energy source management strategy(ESMS)can reduce hydrogen consumption of fuel cell up to 7%compared with the existing rule-based ESMS.Finally,the Hardware-in-the-Loop(HiL)simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.

A comprehensive investigation of loading variance influence on fuel consumption and gas emissions in mine haulage operation

The data collected from haul truck payload management systems at various surface mines show that the payload variance is significant and must be considered in analysing the mine productivity, diesel energy consumption, greenhouse gas emissions and associated costs. The aim of this study is to determine the energy and cost saving opportunities for truck haulage operations associated with the payload variance in surface mines. The results indicate that there is a non-linear relationship between the payload variance and the fuel consumption, greenhouse gas emissions and associated costs. A correlation model, which is independent of haul road conditions, has been developed between the payload variance and the cost saving using the data from an Australian surface coal mine. The results of analysis for this particular mine show that a significant saving of fuel and greenhouse gas emissions costs is possible if the standard deviation of payload is reduced from the maximum to minimum value.

Influence of driving cycles on exhaust emissions and fuel consumption of gasoline passenger car in Bangkok

The influence of different driving cycles on their exhaust emissions and fuel consumption rate of gasoline passenger car was investigated in Bangkok based on the actual measurements obtained from a test vehicle driving on a standard chassis dynamometer. A newly established Bangkok driving cycle （BDC） and the European driving cycle （EDC） which is presently adopted as the legislative cycle for testing automobiles registered in Thailand were used. The newly developed BDC is constructed using the driving characteristic data obtained from the real on-road driving tests along selected traffic routes. A method for selecting appropriate road routes for real driving tests is also introduced. Variations of keyed driving parameters of BDC with different driving cycles were discussed. The results showed that the HC and CO emission factors of BDC are almost two and four times greater than those of EDC, respectively. Although the difference in the NOx emission factor is small, the value from BDC is still greater than that of EDC by 10%. Under BDC, the test vehicle consumes fuel about 25% more than it does under EDC. All these differences are mainly attributed to the greater proportion of idle periods and higher fluctuations of vehicle speed in the BDC cycle. This result indicated that the exhausted emissions and fuel consumption of vehicles obtained from tests under the legislative modal-type driving cycle （EDC） are significantly different from those actually produced under real traffic conditions especially during peak periods.

Load reduction sintering for increasing productivity and decreasing fuel consumption

The technical and economical indexes and the physical properties of load reduction sintering processes with the supporting stands of installation at different height levels （300, 350, and 400 mm）： in a sintering bed were studied under the same conditions of raw material, bed height, and sintering parameters. Sintering pot tests with different bed heights and fuel ratios of the mixture with or without supporting stands were performed to decrease the fuel consumption. The airflow rate through the sintering bed was measured with an anemoscope fixed on the bed surface to reveal the effects of supporting stands. The utilization of load reduction sintering can improve the permeability of the sintering bed, and the airflow rate through the sintering bed is increased. When the stand height is half of the sintering bed, the productivity increases by 27.9%, and the drum index slightly decreases. Keeping at the same productivity level with normal sintering, the utilization of load reduction sintering can decrease the solid fuel consumption by 9.2%.

Combining artificial neural network and multi-objective optimization to reduce a heavy-duty diesel engine emissions and fuel consumption

Nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) is well known for engine optimization problem. Artificial neural networks(ANNs) followed by multi-objective optimization including a NSGA-Ⅱ and strength pareto evolutionary algorithm(SPEA2) were used to optimize the operating parameters of a compression ignition(CI) heavy-duty diesel engine. First, a multi-layer perception(MLP) network was used for the ANN modeling and the back propagation algorithm was utilized as training algorithm. Then, two different multi-objective evolutionary algorithms were implemented to determine the optimal engine parameters. The objective of the present study is to decide which algorithm is preferable in terms of performance in engine emission and fuel consumption optimization problem.

Fuel Consumption Potential of the Pushbelt CVT

The efforts to further reduce fuel consumption of vehicles equipped with a pushbelt type Continuously Variable Transmission(CVT) focus on different sources of loss.In this paper the magnitude of these losses and their potential for reduction is described.Inside the CVT,the variator,its control strategy and the hydraulic actuation circuit can be distinguished as the main potentials.A major opportunity is offered by a new control strategy that takes the actual slip between belt and pulley as the control parameter.The resulting decrease of clamping forces on the pushbelt leads to a reduction of variator and actuation losses.Further potential is found in the hydraulic actuation circuit by an improved tuning of the power supply to the actual power requirement.Outside the CVT additional potential is found in start-stop functionality as supported by measures inside the transmission.The paper describes the theoretical background as well as practical fuel savings of up to 5.5% that were obtained in tests on vehicle level.Slip control adds an inherent robustness to the operation of the pushbelt and opens up the fuel saving potential of the CVT thus reinforcing its position as the benchmark for the near future.

Fuel characteristics, loads and consumption in Scots pine forests of central Siberia

Forest fuel investigations in central and southern Siberian taiga of Scots pine forest stands dominated by lichen and feather moss ground vegetation cover revealed that total aboveground biomass varied from 13.1 to 21.0 kg/m 2.Stand biomass was higher in plots in the southern taiga,while ground fuel loads were higher in the central taiga.We developed equations for fuel biomass(both aerial and ground)that could be applicable to similar pine forest sites of Central Siberia.Fuel loading variability found among plots is related to the impact and recovery time since the last wildfi re and the mosaic distribution of living vegetation.Fuel consumption due to surface fi res of low to high-intensities ranged from 0.95 to 3.08 kg/m 2,that is,18–74%from prefi re values.The total amount of fuels available to burn in case of fi re was up to 4.5–6.5 kg/m 2.Moisture content of fuels(litter,lichen,feather moss)was related to weather conditions characterized by the Russian Fire Danger Index(PV-1)and FWI code of the Canadian Forest Fire Weather Index System.The data obtained provide a strong foundation for understanding and modeling fi re behavior,emissions,and fi re eff ects on ecosystem processes and carbon stocks and could be used to improve existing global and regional models that incorporate biomass and fuel characteristics.

Design and Fuel Consumption Optimization for a Bio-Inspired Semi-floating Hybrid Vehicle

Based on a bionic concept and combing air-cushion techniques and track driving mechanisms, a novel semi-floating hybrid concept vehicle is proposed to meet the transportation requirements on soft terrain. First, the vehicle scheme and its improved duel-spring flexible suspension design are described. Then, its fuel consumption model is proposed accordingly with respect to two vehicle operating parameters. Aiming at minimizing the fuel consumption, two Genetic Algorithms （GAs） are designed and implemented. For the initial one （GA-1）, despite getting an acceptable result, there still existed some problems in its optimiza- tion process. Based on an analysis of the defects of GA-1, an improved algorithm GA-2 was developed whose effectiveness and stability were embodied in the optimization process and results. The proposed design scheme and optimization approaches can provide valuable references for this new kind of vehicle with industry, military or scientific exploitations, etc. promising applications in the areas of agriculture, petroleum industry, military or scientific explaitations, etc.

A comparative study of hybrid electric vehicle fuel consumption over diverse driving cycles

Environmental pollution and declining resources of fossil fuels in recent years,have increased demand for better fuel economy and less pollution for ground transportation.Among the alternative solutions provided by researchers in recent decades,hybrid electric vehicles consisted of an internal combustion engine and an electric motor have been considered as a promising solution in the short-term.In the present study,fuel economy characteristics of a parallel hybrid electric vehicle are investigated by using numerical simulation.The simulation methodology is based on a fast forward facing simulation model of a parallel hybrid and an internal combustion engine powertrains.The objective of this study is to present the main parameters which result in an optimum combination of hybrid powertrain components in order to obtain a better fuel economy of hybrid powertrains regarding different driven cycles and hybridization factors.Then,the fuel consumption of the parallel hybrid electric vehicles are compared considering various driven cycles and hybridization factors.The results showed that the better fuel economy of hybrid powertrains increases by decreasing average load of the test cycle and the point of the best fuel economy for a particular average load of the cycle moves towards higher hybridization factors when the average load of the test cycle is reduced.

Multi-objective comprehensive optimization of fuel consumption and emission for hybrid electric vehicles

Aiming to reduce fuel consumption and emissions of a dual-clutch hybrid electric vehicle during cold start, multiobjective optimization for fuel consumption and HC/CO emission from a TWC(three-way catalytic converter) outlet is presented in this paper. DP(dynamic programming) considering dual-state variables is proposed based on the Bellman optimality principle. Both the battery SOC(state of charge) and the temperature of TWC monolith are considered in the algorithm simultaneously. In this way the global optimal control strategy and the Pareto optimal solution of multi-objective function are derived. Simulation results show that the proposed method is able to promote the TWC light-off significantly by decreasing the engine's load and improving exhaust temperature from the outlet of the engine, in comparison with original DP considering the single battery SOC. Compared to the results achieved by rule-based control strategy, fuel economy and emission of TWC outlet for cold start are optimized comprehensively. Each indicator of Pareto solution set shows the significant improvement.