Fuel Consumption Model Research Articles

Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle’s fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results.

Understanding freight drivers' behavior and the impact on vehicles' fuel consumption and CO2e emissions

Despite the significant impact of driver behavior on fuel consumption and carbon dioxide equivalent (CO2e) emissions, this phenomenon is often overlooked in road freight transportation research. We review the relevant literature and seek to provide a deeper understanding of the relationship between freight drivers’ behavior and fuel consumption. This study utilizes a real-life dataset of over 4000 driving records from the freight logistics sector to examine the effects of specific behaviors on fuel consumption. Analyzed behaviors include harsh acceleration/deceleration/cornering, over-revving, excessive revolutions per minute (RPM), and non-adherence to legal speed limits ranging from 20 to 70 miles per hour (mph). Our findings confirm existing literature by demonstrating the significant impact of certain driving characteristics, particularly harsh acceleration/cornering, on fuel consumption. Moreover, our research contributes new insights into the field, notably highlighting the substantial influence of non-adherence to the legal speed limits of 20 and 30 mph on fuel consumption, an aspect not extensively studied in previous research. We subsequently introduce an advanced fuel consumption model that takes into account these identified driver behaviors. This model not only advances academic understanding of fuel consumption determinants in road freight transportation, but also equips practitioners with practical insights to optimize fuel efficiency and reduce environmental impacts.

Freeway work zone effects on vehicular fuel consumption explored by a four-lane model

A four-lane model is put forward to explore the effects on vehicular fuel consumption of a freeway work zone which has a length of 100 m and occupies lanes III and IV. The model equations are solved numerically in the simulation of vehicular flows by a method called WENO5–RK3. A vehicular fuel consumption model is presented, with an additional fuel consumption defined in comparison with the case in the absence of a work zone. Simulation results show that the freeway work zone affects vehicular fuel consumption significantly, triggers a traffic jam as soon as initial density normalized by jam density is beyond 0.18.

Real-driving CO2, NOx and fuel consumption estimation using machine learning approaches

Real driving emissions (RDE) testing are gaining attention for monitoring and regulatory purposes because of providing more realistic emission and fuel consumption measurements compared to laboratory tests. This study aims to develop machine learning (ML) based emission and fuel consumption estimation models using real-driving measurement data. A light-duty diesel vehicle equipped with a portable emissions measurement system (PEMS) was driven in an urban test route by 30 participant drivers of disparate backgrounds to obtain a wide variety of data in terms of driving behaviour and traffic conditions. The Pearson correlation coefficient was used to select the input variables among 36 driving behaviours and 6 engine parameters. The CO2, NOx and fuel consumption prediction models were developed using linear regression (LR), support vector machine (SVM) and Gaussian process regression (GPR). The results showed that all three models could predict CO2 with an absolute relative error (ARE) of less than 9%. The GPR model showed the best performance in CO2 prediction with an R2 of 0.74 and ARE of 3.30%. LR model showed the best prediction accuracy for NOx with an R2 of 0.80 and ARE of 8.91%. All three models worked well for fuel consumption prediction, however, GPR showed the best accuracy with an R2 of 0.81 and ARE of 3.52%. This method lays a foundation for developing route/region specific emission and fuel consumption models that will help to monitor and reduce the environmental impact and the amount of burned fuel. Moreover, developing models from different driver classes will provide valuable insights into emission-optimal driving behaviour which could be used to train new drivers.

An improved A-ECMS energy management for plug-in hybrid electric vehicles considering transient characteristics of engine

The plug-in hybrid electric vehicles (PHEVs) are playing an increasingly important role in urban public transportation systems for their unique potential for energy saving and emission reduction. However, as an enabling technology for the cost-efficient operation of PHEVs, the current energy management strategies (EMSs) rarely consider the transient characteristics of the engine, especially the limit of engine transient performance and the extra fuel consumption due to engine state changes. To improve the energy-saving effect of EMSs, an optimization study on energy management considering engine transient characteristics for PHEV is carried out in this paper. Firstly, a high-precision transient fuel consumption model is established based on artificial neural network (ANN) to accurately evaluate the real fuel consumption of engine under steady and unsteady states. Secondly, an adaptive equivalent consumption minimization strategy (A-ECMS) is constructed for PHEV, and the engine transient performance boundary is defined in the strategy to avoid unreasonable engine power surge decision. Thirdly, the transient fuel consumption model is incorporated into the equivalent fuel consumption cost function of A-ECMS to fully consider the impact of engine transient fuel consumption on the real-time power allocation of PHEV. The results show that the improved strategy weakens the state fluctuation of the engine, and makes the engine run more smoothly, resulting in a 99.16% reduction in the extra fuel consumption due to engine state changes. Finally, the fuel economy of the PHEV under the combined driving cycle based on the C-WTVC improved by 3.37%.

Pseudospectral convex optimization for on-ramp merging control of connected vehicles

It can be a daunting task for human drivers to merge into highways because of the intricate vehicle negotiations and potential risk within limited time and space. Connected vehicle (CV) technologies could be a solution to this problem and offer many benefits to the road safety, traffic mobility, and energy efficiency. However, real-time optimal control of CVs is still an open challenge, due to the nonlinear vehicle dynamics, non-convex fuel consumption model, and highly dynamic uncertain inter-vehicle interactions. To tackle these issues, a novel real-time optimal control approach that balances the computational efficiency and solution optimality is proposed for the purpose of onboard application. To this end, the pseudospectral collocation method is integrated with a sequential convex programming approach to develop two new optimization algorithms, which are implemented within a model predictive control (MPC) framework to allow for real-time generation of optimal merging speed profiles. One algorithm leverages the line search technique to improve convergence, and the other benefits from the trust region method for better computational efficiency. The optimality and convergence process of both proposed algorithms are investigated by comparing their solutions with a popular non-linear solver. Simulation results show that the proposed methods outperform the benchmark in terms of computational cost, fuel consumption, and traffic efficiency. In particular, the proposed fuel-economy merging rule can save 57.1% fuel consumption on average on four different traffic volumes. Meanwhile, the proposed optimal control algorithms can reduce 2.2% travel time on average comparing to the “first-in-first-out” merging rule.

Improved Genetic Algorithm for Solving Green Path Models of Concrete Trucks

In this paper, for the problem of high total fuel consumption of distribution trucks when multiple concrete-mixing plants distribute concrete together, we established a green fuel consumption model for distribution trucks and solved the model with an improved genetic algorithm to obtain a green distribution scheme for trucks. Firstly, the fuel consumption model is established for the characteristics of commercial concrete tankers; secondly, the adaptive elite retention strategy, adaptive crossover, mutation operator, and immune operation are added to the genetic algorithm to improve it; and finally, the model is solved to obtain the green distribution scheme. The total fuel consumption in this experiment was 189.6 L when the green distribution scheme was used; compared to the total fuel consumption under the original scheme (240 L), the total fuel consumption was reduced by 21.25%. The experimental results show that the total fuel consumption of delivery trucks can be significantly reduced based on the established green fuel consumption model, and the improved genetic algorithm can effectively solve the model.

Novel ship fuel consumption modelling approaches for speed and trim optimisation: Using engine data as auxiliary

An accurate fuel consumption model is essential for optimising ship operations. This study examined the impact of main engine data on the precision of ship fuel consumption models. When compared to models constructed without engine data, models utilising engine data can decrease prediction errors by 18.49%–31.25%. However, since speed and trim optimisation tasks necessitate control variables which are not available before the voyage, they cannot be employed as inputs to the fuel consumption model developed for the optimisation task. The first approach involves a two-stage process, while the second entails incorporating an auxiliary branch into a deep neural network. In the experimental findings of the second strategy, the mean absolute error was 0.001139, signifying a 20.9% reduction in fuel consumption model error compared to not utilising the main engine data. These strategies present novel methods for establishing precise fuel consumption models in ship operation optimisation research.

Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model

The development of hybrid technology can effectively solve the problems of the high pollution and energy consumption levels of automobiles. Therefore, an energy consumption prediction and control algorithm for hybrid vehicles based on a minimum equivalent fuel consumption model is proposed. The model’s battery power consumption is equivalent to the fuel consumption, and the sum of the engine fuel consumption and the battery equivalent fuel consumption is established as the objective function. By utilizing these factors, an innovative minimum equivalent fuel consumption model was constructed that could be used to measure the energy efficiency of hybrid vehicles. The longitudinal force result of braking force distribution control was obtained, as well as the energy consumption prediction structure of a hybrid electric vehicle. The rolling resistance, air resistance, and climbing resistance of the hybrid electric vehicles were calculated, and the energy consumption control algorithm for hybrid electric vehicles was constructed according to the calculation results. The experimental results indicated that under this research algorithm, the driving energy consumption of hybrid electric vehicles was relatively low and the energy consumption and energy efficiency measurements effectively met the actual demand, and the energy consumption prediction and control results were good.

Deep Learning Based Distributed Meta-Learning for Fast and Accurate Online Adaptive Powertrain Fuel Consumption Modeling

Connected vehicle-based distributed meta-regression (CV-DMR) algorithms from our previous work facilitate online adaptive microscopic fuel consumption modeling, which can support model-based vehicle and powertrain control (MB-VPC) for fuel-efficient vehicle operations. By taking advantage of accessible online computational resources from connected vehicles (CVs) and CV remote data center, CV-DMR can adapt the microscopic fuel consumption model to an unseen driving condition with limited training data. However, the computational complexity of obtaining the normalized feature map in CV-DMR during training is high. It can take long time to adapt the model to a new driving condition, especially when the new driving condition is considerably different from the pre-learnt driving conditions. To reduce the computational complexity of learning/training, we propose to apply deep neural networks as the model representation method and newly design efficient deep learning based CV-supported distributed meta-learning (CV-DML) algorithms for adaptive fuel consumption modeling. To further generalize the model and reduce model adaptation time, the concept of meta-suggestion is newly proposed in CV-DML. Substantial proof-of-concept experiments are conducted with steady-state and transient vehicle engine data to evaluate the model performance in terms of model prediction accuracy, model adaptation speed, and fuel saving. Compared to the physical model and non-CV-supported model adaptation, the prediction accuracy of CV-DML is improved by 34% - 87% and 17% - 30%, respectively. Due to the improved accuracy, up to 9.4% of fuel is saved. To achieve the same the prediction accuracy and fuel saving, the model adaptation of CV-DML is 18 to 1,300 times faster than that of CV-DMR.

Eco-routing problem for the delivery of perishable products

Eco-routing problem (ERP) is concerned with selecting the optimal route for a vehicle to minimize fuel consumption. For delivery activities, the vehicle is often required to finish the trip within a given period, so we propose a time-constrained ERP for this case. Since the nonlinearities in the fuel consumption model and the time constraint make the ERP difficult to solve, this paper develops a method to linearize it into a mixed-integer linear programming (MILP) model that off-the-shelf optimizers can efficiently solve. When delivering perishables, the ERP should take the deterioration process into account. Thus, this paper further extends the time-constrained ERP to a bi-objective ERP, setting weighted normalized total deterioration value and fuel consumption as the objective function. Numerical experiments are implemented to investigate the performances of the proposed models and solution methods: The results demonstrate the computational efficiency of the proposed solution method. The sensitivity analysis confirms the existence of a trade-off between the total deterioration value (TDV) of perishable goods and the fuel consumption of the delivery vehicle. This trade-off can be efficiently balanced through the integration of speed optimization into the routing decision-making process. Furthermore, numerical results reveal that the vehicle can achieve a lower TDV with the same fuel consumption when traffic conditions are better.

The environmental impact assessment of greener trajectories: the GreAT project

GreAT (Greener Air Traffic Operations) is a project funded by the European Commission under the H2020 framework programme. The overall objective is to reduce the fuel consumption and gas emissions during “gate-to-gate” flight phases through developing and assessing environment-friendly air traffic operational concept, adaptive airspace and green trajectory optimization technologies, and supporting avionic systems. Based on the scientific description of the impact of aviation emissions on the climate, the work within GreAT is seeking the key factors of the impact about aviation emissions on climate change characteristics by using sensitivity analysis, such as greenhouse gases, pollutant gases and condensation, and then select these factors as environmental impact assessment indicators, including fuel consumption, aviation emissions, air quality and greenhouse effect, establishing a calculation model for evaluation indicators using the fuel consumption model, gas emission model and climate change model. System analysis methods are used to build an aviation emission environmental impact (EIA) assessment index system structure, apply environmental impact assessment indicators, construct a general environmental impact assessment index system, and propose a comprehensive assessment method for aviation environmental impact. The following step in the project is the environmental impact assessment of air traffic operations to determine how green air traffic performs. According to the existing air traffic operation patterns, the flight characteristics and trajectory characteristics of the aircraft are determined, and the environmental impact assessment index system is used to evaluate the environmental impact under the air traffic operation plan and the impact and improvement effect on climate change.

Estimation of fuel consumption in a jet engine based on vibration signal parameters

This paper proposes the use of vibroacoustic signal parameters to estimate the fuel consumption of a miniature GTM-400 engine. The method for testing engine vibrations is presented, followed by an analysis of the results obtained. Two vibration point measures were selected to build a fuel consumption model. The models obtained were verified, after which those that best describe the real fuel consumption of the engine were selected. The paper proves that the vibration signal, in addition to its applications in jet engine diagnostics, can be used to determine engine performance, which can contribute to reducing the complexity of construction and increasing the economics of engine operation.

Numerical Evaluation of Fuel Consumption and Transient Emissions of Different Hybrid Topologies for Two-Wheeler Application

<div>In Asian countries, small two-wheelers form a major share of the automobile segment and contribute significantly to carbon dioxide (CO<sub>2</sub>) emissions. Hybrid drives, though not widely applied in two-wheelers, can reduce fuel consumption and CO<sub>2</sub> emissions. In this work three hybrid topologies, viz., P2 (electric motor placed between engine and transmission), P3 (electric motor placed between transmission and final drive), and power-split concepts (with planetary gear-train) have been modeled in Simulink, and their fuel consumption and emissions under the World Motorcycle Test Cycle (WMTC) have been evaluated. A physics-based model for the Continuously Variable Transmission (CVT) was used which is capable of predicting its transient characteristics. A map-based fuel consumption model and a Neural Network (NN)-based transient emission model were used for the engine. The NN-based transient emission model avoids the need to model the air path and fuel path in transient conditions, which is time consuming. The fueling characteristics of the Engine Control Unit (ECU) in transients need not be known if an NN model is built and tuned with sufficient experimental data. Several transient experiments were performed with speed-load profiles similar to the WMTC for tuning the NN emission models. Simulation results show that the P2 hybrid, P3 hybrid, and power-split drives have fuel economy benefits of about 27%, 37%, and 49%, respectively, compared to the conventional powertrain. However, nitrogen oxides (NOx) emissions are much higher for the hybrid powertrains due to the operation of the engine at higher load ranges for efficiency but are still within the prevailing BS6 Indian emission limits. A significant portion of the wheel energy input can be recovered through efficient regenerative braking in the WMTC. This will be even more significant under peak traffic city driving conditions. The belt losses in the CVT significantly reduce the potential benefits of the hybrid powertrain, and hence, an efficient transmission to replace it will be beneficial.</div>

Fuel Consumption Prediction Models Based on Machine Learning and Mathematical Methods

An accurate fuel consumption prediction model is the basis for ship navigation status analysis, energy conservation, and emission reduction. In this study, we develop a black-box model based on machine learning and a white-box model based on mathematical methods to predict ship fuel consumption rates. We also apply the Kwon formula as a data preprocessing cleaning method for the black-box model that can eliminate the data generated during the acceleration and deceleration process. The ship model test data and the regression methods are employed to evaluate the accuracy of the models. Furthermore, we use the predicted correlation between fuel consumption rates and speed under simulated conditions for model performance validation. We also discuss applying the data-cleaning method in the preprocessing of the black-box model. The results demonstrate that this method is feasible and can support the performance of the fuel consumption model in a broad and dense distribution of noise data in data collected from real ships. We improved the error to 4% of the white-box model and the R2 to 0.9977 and 0.9922 of the XGBoost and RF models, respectively. After applying the Kwon cleaning method, the value of R2 also can reach 0.9954, which can provide decision support for the operation of shipping companies.

Multi-objective vaccine delivery problem considering low carbon and customer loss aversion

The high energy consumption and on-time delivery requirements of vaccine last-mile delivery require us to rethink deeply about the vaccine delivery problem. First, using a fuel consumption model, we examine the main factors affecting vaccine distribution vehicles' fuel consumption and carbon emission. And then, we construct a delivery delay penalty function based on the characteristics of customer behavior for loss aversion. Second, we develop a dual-objective vaccine distribution model that includes minimizing the total logistics cost and minimizing the total distribution delay. Lastly, we design a new crossover mechanism of multi-population evolution and embed the simulated annealing criteria into the multi-objective algorithm. In numerical experiments, we demonstrate the effectiveness of our multi-objective algorithm in solving vaccine distribution problems of this type. Besides, the results show that we can scientifically configure the logistics route by reducing the usage of delivery vehicles without increasing fuel consumption while achieve to balance both logistics cost and delivery efficiency.

ПОВЫШЕНИЕ ЭФФЕКТИВНОСТИ РАБОТЫ СУШИЛЬНЫХ УСТРОЙСТВ ПУТЁМ ИСПОЛЬЗОВАНИЯ ТЕПЛОТЫ ОТРАБОТАННОГО АГЕНТА СУШКИ

The humidity of harvested grain in many regions of the Russian Federation is higher due to the peculiarities of climatic conditions. The efficiency of drying devices is determined by the use of energy-saving technologies. Reuse of waste coolant in dryers is proposed in the technological process of grain drying. The assessment of the main factors influencing the process of drying a wet grain heap has been carried out. Simulation of grain drying when heated air, obtained by cooling the grain after drying, is supplied to the combustion device of the dryer. A special computer program was used to calculate technological indicators. The program allows you to determine the economically feasible mode of operation of the drying equipment in the process of post-harvest processing of grain. The numerical values of the variables that are set depending on the grain processing mode and the productivity of the dispenser and dryer are defined for working with the program. The consumption of diesel fuel and natural gas for drying grain, depending on the moisture content of the grain heap and the productivity of the dryer, was determined during the simulation of this process. The hourly consumption of diesel fuel and natural gas for drying grain, depending on the moisture content of the grain heap, was determined. The dryer capacity varied from 5 to 40 t/h. The results of fuel consumption modeling showed that drying grain using the heat obtained by cooling the seeds and sent to the furnace units of the dryers can reduce fuel consumption by 20%.

A fuel consumption model for mixed traffic flow in multiple connected and autonomous scenarios

AbstractThe potential increase/decrease in energy consumption of traffic operation modes is a hot topic in the studies of connected and autonomous vehicles (CAVs), but there are few theoretical studies on the fuel consumption of human driving vehicles (HDVs) and CAV mixed platoon as a result of limited data. Based on the power distribution of light‐duty vehicles, this study developed a fuel consumption model for HDV and CAV mixed platoons by theoretical hypothesis considering the oscillation along with the platoon. Firstly, a vehicle‐specific power (VSP) distribution conforming to normal distribution is constructed with hypothesis‐based VSP standard deviation and constant VSP average value at each speed bin under different platoon arrangement conditions. Then, the fuel factors (mL/km) are calculated by weighted fuel rate (mL/s) according to the VSP distribution. Finally, a library of fuel consumption values for multiple penetration rates, platoon intensities, and speeds was established. In the case, at a speed of 30 km/h, the fuel consumption in the CAV group was reduced by 0.8–5%, 1.9–12.5%, 8.6–12%, and 12.4%, respectively, at the penetration rate of 20%, 50%, 80%, and 100%, compared to the HDV group. The fuel consumption reduction effect of CAV was negatively correlated with the speed.

Evaluation of Real-World Fuel Consumption of Hybrid-Electric Passenger Car Based on Speed-Specific Vehicle Power Distributions

Fuel consumption differs between the hybrid electric vehicle (HEV) and the conventional vehicle (CV). However, traditional fuel consumption models developed for CVs are commonly applied to HEVs, which leads to uncertainties in the quantitative evaluation of energy consumption for passenger cars in traffic road networks. Considering the internal combustion engine (ICE) operating modes of hybrid vehicles among varying vehicle specific power (VSP) demand, we present a method to incorporate the HEV ICE speed to develop speed-specific VSP distributions for real-world driving conditions. Using vehicle trajectory and fuel consumption data in real traffic conditions, the results of this study show that the application of methods developed for CVs leads to a significant underestimation of fuel consumption for HEVs when the average speed is in the high-speed range (over 50 km/h) and a significant overestimation of fuel consumption when the average speed is in the low-speed range (below 30 km/h). The average relative error of the measured fuel consumption factor in each speed bin is 7.1% compared with real-world observations, which is an unacceptably large error. This paper proposes a method to develop the speed-specific VSP distribution, considering whether the internal combustion engine (ICE) of HEVs is on or off. This approach reduces the average relative error of the obtained fuel consumption compared with real-world observations to 2.2%, and the measuring accuracy at different average speeds is significantly improved. This method enhances the functionality and applicability of the VSP theory-based traffic energy model for HEVs.

High-precision transient fuel consumption model based on support vector regression

Due to the increasingly severe environmental issues caused by transportation, energy conservation and emission reduction have become one of the most important research directions in the field of transportation. As the key tool for estimating transient fuel consumption, fuel consumption models are crucial for quantifying the energy consumption of automobiles and achieving energy conservation and emission reduction. However, most existing fuel consumption models suffer from problems such as a complex structure, difficult-to-determine model coefficients, and difficult-to-measure input parameters. Therefore, it is necessary to propose a high-precision fuel consumption model with good applicability and easy implementation. The model proposed in this paper adopted a structure of steady-state estimation and transient correction and used support vector regression to predict fuel consumption. First, the engine torque was fitted based on the steady-state data, and the steady-state module was established. Then, based on data analysis using the Gaussian mixture model, the transient motion was divided into three driving conditions, and the transient module was obtained by transient correction for different driving conditions. Ultimately, the performance of the model was validated. The results show that the mean absolute percentage error of the new model is less than 14%, and the mean absolute error is less than 0.16 cc/s, which is a significant improvement compared with other classic models, indicating that the new model has a high prediction accuracy. In addition, the new model takes the most common engine speed, vehicle speed, and acceleration as inputs, making it easy to use and has good applicability and popularization value.