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.

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%.

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.

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 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.

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.

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.

The First Complusory National Standard on Automobile Fuel Consumption Issued

On October 28th, hosted by the General Adminis tration of Quality Supervision, Inspection and Quarantine of China (AQSIQ) and Standardization Administration of China (SAC), the press conference of the compulsory national standard Limits of Fuel Consumption for Passenger Cars (GB 19578-2004) was held in Beijing. The national standard was approved by AQSIQ and SAC on September 2nd 2004 and will be formally implemented on July 1st 2005.

Variability of fuel consumption and CO_(2) emissions of a gasoline passenger car under multiple in-laboratory and on-road testing conditions

An increasing divergence regarding fuel consumption(and/or CO_(2)emissions) between realworld and type-approval values for light-duty gasoline vehicles(LDGVs) has posed severe challenges to mitigating greenhouse gases(GHGs) and achieving carbon emissions peak and neutrality. To address this divergence issue, laboratory test cycles with more real-featured and transient traffic patterns have been developed recently, for example, the China Lightduty Vehicle Test Cycle for Passenger cars(CLTC-P). We collected fuel consumption and CO_(2)emissions data of a LDGV under various conditions based on laboratory chassis dynamometer and on-road tests. Laboratory results showed that both standard test cycles and setting methods of road load affected fuel consumption slightly, with variations of less than 4%. Compared to the type-approval value, laboratory and on-road fuel consumption of the tested LDGV over the CLTC-P increased by 9% and 34% under the reference condition(i.e., air conditioning off, automatic stop and start(STT) on and two passengers). On-road measurement results indicated that fuel consumption under the low-speed phase of the CLTC-P increased by 12% due to the STT off, although only a 4% increase on average over the entire cycle. More fuel consumption increases(52%) were attributed to air conditioning usage and full passenger capacity. Strong correlations(R2> 0.9) between relative fuel consumption and average speed were also identified. Under traffic congestion(average speed below 25 km/hr), fuel consumption was highly sensitive to changes in vehicle speed. Thus,we suggest that real-world driving conditions cannot be ignored when evaluating the fuel economy and GHGs reduction of LDGVs.

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.

On-road pollutant emission and fuel consumption characteristics of buses in Beijing

On-road emission and fuel consumption （FC） levels for Euro Ⅲ and Ⅳ buses fueled on diesel and compressed natural gas （CNG） were compared, and emission and FC characteristics of buses were analyzed based on approximately 28,700 groups of instantaneous data obtained in Beijing using a portable emissions measurement system （PEMS）. The experimental results revealed that NOx and PM emissions from CNG buses were decreased by 72.0% and 82.3% respectively, compared with Euro IV diesel buses. Similarly, these emissions were reduced by 75.2% and 96.3% respectively, compared with Euro III diesel buses. In addition, CO2, CO, HC, NOx, PM emissions and FC of Euro IV diesel buses were reduced by 26.4%, 75.2%, 73.6%, 11.4%, 79.1%, and 26.0%, respectively, relative to Euro Ⅲ diesel buses. The CO2, CO, HC, NOx, PM emissions and FC factors all decreased with bus speed increased, while increased as bus acceleration increased. At the same time, the emission/FC rates as well as the emission/FC factors exhibited a strong positive correlation with the vehicle specific power （VSP）. They all were the lowest when VSP 〈 0, and then rapidly increased as VSP increased. Furthermore, both the emission/FC rates and emission/FC factors were the highest at accelerations, higher at cruise speeds, and the lowest at decelerations for non-idling buses. These results can provide a base reference to further estimate bus emission and FC inventories in Beijing.

On the estimation of ship’s fuel consumption and speed curve:A statistical approach

When fuel efficiency is at stake,along with the reduction of the environmental foot print of air pollution,a need is presented to estimate a ship’s fuel consumption for a forthcoming voyage,and means for decision making and for cost saving.This paper suggests an operational approach for obtaining an accurate fuel consumption and speed curve,on the basis of major factors affecting it,namely,ship’s draft and displacement,weather force and direction,hull and propeller roughness.A statistical analysis on 418 noon reports of a Pure Car and Truck Carrier case ship is carried out and the influence of the above factors is calculated.As expected,stronger wind and head weather increases the fuel consumption,and the difference between several weather conditions could be quantified.A simple and accurate algorithm is proposed in order for ship owners,managers and operators to be in a position to apply the suggested method on their fleet.Finally,applications of the structured algorithm are introduced with examples,in estimating the fuel consumption of the case ship for a future voyage,and also the same for a sister ship.Furthermore,voyage planning in several scenarios is proposed in order to assist the stakeholders with decision making aimed to fuel saving and environmental friendliness of their ships.

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.

Support vector machine and ROC curves for modeling of aircraft fuel consumption

To estimate the fuel consumption of a civil aircraft,we propose to use the receiver operating characteristic(ROC)curve to optimize a support vector machine(SVM)model.The new method and procedure has been developed to build,train,validate,and apply an SVM model.A conceptual support vector network is proposed to model fuel consumption,and the flight data collected from routes are used as the inputs to train an SVM model.During the training phase,an ROC curve is defined to evaluate the performance of the model.To validate the applicability of the trained model,a case study is developed to compare the data from an aircraft performance manual and from the implemented simulation model.The investigated aircraft in the case study is a Boeing 737-800 powered by CFM-56 engines.The comparison has shown that the trained SVM model from the proposed procedure is capable of representing a complex fuel consumption function accurately for all phases during the flight.The proposed methodology is generic,and can be extended to reliably model the fuel consumption of other types of aircraft,such as piston engine aircraft or turboprop engine aircraft.

Use of light-emitting diode(LED)lamps in combination with metal halide(MH)lamps reduce fuel consumption in the Vietnamese purse seine fishery

The use of high-power lights during night-time purse seining is common in Vietnam.Typically,metal halide(MH)lamps are used in the commercial fishery to attract fish,however these lights require more energy,have a shorter lifespan,and lower chromatic performance than light emitting diode(LED)lamps.This study examined catch efficiency and fuel consumption when using LED lamps in combination with reduced numbers of MH lamps(10.24 kW),compared to conventional lighting(28.6 kW),used during purse seining off the coast of Ninh Thuan province,Vietnam.The economic performance associated with using LED lamps in this fishery was also assessed.We found no significant differences in catch rates between the different light treatments,however fuel consumption was significantly reduced.Fuel consumption per nightly trip using LED with MH lamps was 70.8 l(11.1 l/h)compared to114 l(17.45 l/hr)using MH lamps alone,an estimated 37.9%reduction in fuel consumption.An investment in LED lamps by a fishing enterprise will require additional initial costs,however our analysis revealed the financial break-event point can be reached after approximately 101 nightly trips when the fuel price is at the 2015 level of USD$0.74 per l.Fishing enterprises can increase their profitability,and reduce CO2 emissions,by using LED lamps in the Vietnamese purse seine fishery.

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used.The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers＇ information and vehicles＇ parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively.The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.

Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone

In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach,this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a smartphone.First,three driving modes were developed for maize sowing scenarios:manual driving assisted driving and unmanned driving.While sowing,smartphone software and CAN(Controller Area Network)storage devices collected both positional data and engine operating conditions.Second,the tractor trajectory points were divided into kinematic sequences,with six driving cycle indicators built in each series based on the time window.Based on the semantic information of the kinematic sequences,the three operations of sowing,seeds filling,and turning round were well recognized.Last,a model for maize sowing fuel consumption forecast was advanced using the principal component analyses and random forest algorithm,regarding three factors:driving cycles,operating behaviors,and driving patterns.When compared to the traditional K-means algorithm,the results demonstrated that the harmonic mean of the precision and recall(F1 score)of sowing behavior recognition,seeds filling behavior recognition,and turning behavior recognition were enhanced by 2.06%,8.99%,and 21.79%,respectively.In terms of the impacts of driving modes and operating behaviors on fuel consumption,assisted driving mode had the lowest fuel usage for both sowing and turning behavior.Therefore,assisted driving is the most fuel-efficient mode for maize sowing.Combining the three driving modes,the relative error of the fuel consumption prediction model was 0.11 L/h,with the manual driving mode having the lowest relative error at 0.09 L/h.This research method lays the foundation for the optimization of tractor operation behavior,the selection of tractor driving mode,and the fine management of tractor fuel consumption.

Torque control strategy with V2X information for HEVs to minimize fuel consumption

Today,much information from traffic infrastructures and sensors of ego vehicle is available.Using such information has a potential for internal combustion engine vehicle to reduce fuel consumption in real world.In this paper,a powertrain controller for a hybrid electric vehicle aiming to reduce fuel consumption is introduced,which uses information from traffic signals,the global positioning system and sensors,and the preceding vehicle.This study was carried out as a benchmark problem of engine and powertrain control simulation and modeling 2021(E-COSM 2021).The developed controller firstly decides reference acceleration of the ego vehicle using the traffic signal and the position information and the preceding vehicle speed.The acceleration and deceleration leading to increase in unnecessary fuel consumption is avoided.Next,the reference engine,generator,and motor torques are decided to achieve the reference acceleration and minimize fuel consumption.In addition,the reference engine,generator and motor torques were decided by the given fuel consumption map for the engine,and by the virtual fuel consumption maps for the generator and the motor.The virtual fuel consumption is derived from the efficiency maps of the generator and the motor using a given equivalent factor,which converts electricity consumption to fuel for the generator and the motor.In this study,a controller was designed through the benchmark problem of E-COSM 2021 for minimizing total fuel consumption of the engine,the generator,and the motor.The developed controller was evaluated in driving simulations.The result shows that operating the powertrain in efficient area is a key factor in reducing total fuel consumption.

An adaptive hyper parameter tuning model for ship fuel consumption prediction under complex maritime environments

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 shrink-age and selection operator(Lasso).The hyperparameter tuning proportionally increased the amplitudes of the coefficients of determination(R 2)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 hyper-parameter tuning can effectively improve the fuel consumption prediction accuracy,and our proposed model can therefore serve as a significant reference for calculating fuel consumption.