Fuel Consumption Map Research Articles

Fuel Efficiency Optimization in Adaptive Cruise Control: A Comparative Study of Model Predictive Control-Based Approaches

This work investigates the fuel efficiency potential of Adaptive Cruise Control (ACC) systems, focusing on two optimization-based control approaches for internal combustion engine (ICE) vehicles. In particular, this study compares two model predictive control (MPC) designs. In the first approach, a strictly quadratic cost is adopted, and fuel consumption is indirectly minimized by adjusting the weights assigned to state tracking and control effort. In the second approach, a fuel consumption map is explicitly included in the MPC cost function, aiming to directly minimize it. Both approaches are compared to a globally optimal benchmark obtained with dynamic programming. Although these methods have been discussed in the literature, no systematic comparison of their relative performance has been conducted, which is the primary contribution of this article. The results demonstrate that, with proper tuning, the simpler quadratic approach can achieve comparable fuel savings to the approach with explicit fuel consumption minimization, with a maximum variation of 0.5%. These results imply that the first alternative is more suitable for online implementation, due to the more favorable characteristics of the associated optimization problem.

Generating fuel consumption maps on prescribed fire experiments from airborne laser scanning

Background Characterisation of fuel consumption provides critical insights into fire behaviour, effects, and emissions. Stand-replacing prescribed fire experiments in central Utah offered an opportunity to generate consumption estimates in coordination with other research efforts. Aims We sought to generate fuel consumption maps using pre- and post-fire airborne laser scanning (ALS) and ground measurements and to test the spatial transferability of the ALS-derived fuel models. Methods Using random forest (RF), we empirically modelled fuel load and estimated consumption from pre- and post-fire differences. We used cross-validation to assess RF model performance and test spatial transferability. Key results Consumption estimates for overstory fuels were more precise and accurate than for subcanopy fuels. Transferring RF models to provide consumption estimates in areas without ground training data resulted in loss of precision and accuracy. Conclusions Fuel consumption maps were produced and are available for researchers who collected coincident fire behaviour, effects, and emissions data. The precision and accuracy of these data vary by fuel type. Transferability of the models to novel areas depends on the user’s tolerance for error. Implications This study fills a critical need in the broader set of research efforts linking fire behaviour, effects, and emissions.

Fuel Consumption Efficiency Enhancement of Synchronous Diesel Generator Operating at Adjustable Speed using Adaptive Inertia Weight Particle Swarm Optimization Algorithm

Diesel generator is a reliable source of electricity, but requires quite high operational costs, especially for fuel. This paper describes a technique for reducing fuel consumption in Diesel Engine Synchronous Generator systems. The system is originally a Constant Speed Diesel Synchronous Generator (CSD-SG), but during certain conditions, the speed is reduced to minimize fuel consumption by adjusting the Specific Fuel Consumption (SFC) map. SFC is defined as the amount of fuel consumed by a diesel engine generator for each unit of power output. It shows various numbers depending on the speed and operating power. In this paper, we use the Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO) algorithm to select of the proper SFC curve at a certain speed and operating power. AIWPSO employs an adaptive inertial weight adjustment method, which enables this algorithm to achieve faster convergence than conventional Particle Swarm Optimisation (PSO) algorithms. The system is embedded with AC/DC/AC power electronics converter to regulate the frequency. Data set of 1000 kVA Cummins diesel engine generator from the oil and gas company in Central Java, Indonesia was taken for simulations. The results show that the AIWPSO algorithm calculates the fuel consumption as 1,678 liters per day on a typical condition, whereas the previous method, the linear line needs 1,693 liters per day. Therefore, using AIWPSO method can save up to 450 liters of fuel per month. The simulation results show that the proposed method can improve fuel efficiency compared to the previous model.

Analysis of the influence of passenger load on bus energy consumption a vehicle-engine combined model-based simulation framework

Analyzing of the energy consumption (EC) in bus operation is important for reducing operating costs, improving sustainable solutions and creating environmentally friendly cities. The purpose of this paper is to identify the factors, such as passenger load, speed and acceleration, that affect significantly EC in bus operation. This paper builds a simulation framework for describing the level of energy based on the vehicle-engine combined (VEC) EC model. On the basis of the relationship between engine torque, power, traction and EC, the simulation framework mainly includes the road model, vehicle model, engine model and driver strategy. Furthermore, the correlations between energy consumption, passenger load, vehicle speed and acceleration are analyzed in different station spacing. The results show that the passenger load has a significant impact on EC of buses, and is related to the vehicle’s speed and acceleration. Generally, the higher the maximum driving speed, the higher the EC of the bus. Acceleration strategies and maximum speed limits are critical factors determining the EC of bus for a certain passenger load and station spacing. For the same station spacing and maximum driving speed, the acceleration phase is under a greater contribution to the increase of EC. In addition, the greater the maximum speed limit or the acceleration, the greater the contribution percentage of EC increase in the acceleration phase. The simulation framework based on vehicle-engine combined EC model and specific fuel consumption maps can obtain the operating EC of buses for situations with different station spacing and maximum speed, which is conducive to vehicle operation EC analysis. Acceleration strategies and maximum speed limits are critical factors determining the EC of bus for a certain passenger load and station spacing. Therefore, energy savings can be obtained by optimizing the driving strategy.

Optimization for the minimum fuel consumption problem of a hybrid electric vehicle using mixed-integer linear programming

In this study, a mixed-integer linear programming (MILP) formulation for obtaining the operating points of hybrid electric vehicle (HEV) powertrains is proposed. This study focuses on linearizing the nonlinear terms, such as an engine fuel consumption map, or bilinear terms represented by the energy of the motor. To address this problem, a conventional piecewise linear (PWL) method and a multi-layer perceptron (MLP) regression approach are adopted. Although the optimal solution cannot be determined using a PWL approximation for the fuel consumption map, it can be obtained using an MLP regression. Furthermore, the PWL method achieves better results than the MLP approach in terms of the accuracy of its bilinear approximation. Obtaining the optimal solution using MILP helps in acquiring a Lagrange multiplication of the design variables by solving the dual problem, which allows an efficient design revision strategy to be obtained.

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.

Development of prediction methodology for CO2 emissions and fuel economy of light duty vehicle

Fuel economy prediction models usually require vehicle specifications such as a fuel consumption map which are not publicly available. Therefore, the present study proposed a new data analyzing procedure to predict CO2 emissions and fuel economy using on-road driving data without confidential specifications. Vehicle specifications such as gear ratios and vehicle mass which are provided in a service manual and driving data such as vehicle speed and CO2 emission were used to develop the prediction model. Instead of the fuel consumption map, linear equations for each gear between wheel power and CO2 emissions were used to predict CO2 emissions for various driving modes. Since higher gears exhaust less CO2 than lower gears (the seventh gear exhausted 24.4% less CO2 than the first gear), the accuracy of fuel economy prediction was improved by applying the equations for each gear stage. The accuracy of the prediction was verified by comparing it with measurement data. The comparisons showed that the equations for each gear can predict the fuel economy more accurately than one equation representing the entire gear. In worldwide harmonized light vehicles test cycle (WLTC) mode, the former had a maximum error of 6.1%, but the latter showed an error of 17.9%.

Fuel consumption comparison of heavy-duty commercial vehicles under CHTC and C-WTVC cycles based on VECTO

In this paper, the simulation method of calculating the fuel consumption of heavy-duty commercial vehicles based on VECTO (Vehicle Energy Consumption Calculation Tool) was introduced, and the fuel consumption of four vehicles under C-WTVC and CHTC cycles was calculated with VECTO. The differences between C-WTVC and CHTC cycles were analyzed by comparing the characteristic parameters, and the fuel consumption map and driving acceleration of two cycles were compared, so as to explore the influence of different test cycles on fuel consumption. The results show that compared with the C-WTVC cycle, the fuel consumption of CHTC cycle increases by 17.08g/km~28.12g/km, with an increase of 6.42%~17.26%. Most of the engine working points in the CHTC cycle work in the middle and low speed and torque regions and the acceleration range of CHTC cycle is wider are important reasons for the increase of fuel consumption.

General-Purpose and Scalable Internal-Combustion Engine Model for Energy-Efficiency Studies

Hybrid powertrains that combine electric machines and internal-combustion engines offer substantial opportunities to increase the energy efficiency and minimize the exhaust emissions of vehicles and nonroad working machines. Due to the wide range of applications of such powertrains, simulation tools are used to evaluate and compare the energy efficiency of hybrid powertrains for application-specific working cycles in virtual environments. Therefore, the accurate modeling of the powertrain components of a hybrid system is important. This paper presents an agile calculation tool that can generate realistic fuel consumption data of a scalable diesel engine. This method utilizes a simple efficiency model of the combustion and crank train friction model to generate the fuel consumption map in the operating area of a typical diesel engine. The model parameters are calibrated to produce accurate fuel consumption data in the initial phase of system-level simulations. The proposed method is also validated by using three real engine datasets, and the comparison of results is presented.

Numerical and experimental investigation of fuel consumption and CO2 emission performance for a parallel hybrid vehicle

In this study, fuel consumption map was created by obtaining a large number of data from gasoline engine passenger car through high-speed data acquisition with ISO-15765–4-CAN Protocol. Subsequently, CO2 map was also generated theoretically. To obtain precise results, ANN was used while creating the maps. Later, conventional vehicle model was created with Matlab/Simulink. Afterwards, hybrid vehicle model was developed by implementing electrical system in compliance with single-shaft pre-transmission parallel configuration with one electric motor. Hybrid powertrain control algorithm was created by considering efficiencies of mechanical and electrical power sources and sinks. Charge-neutral simulations were run with conventional and hybrid models over WLTC and a local route which is created by collected in-city speed data. The hybrid and conventional vehicles were enabled to be compared with the developed method. Detailed comparison of ICE operations both between two vehicle models and two cycles was presented. Moreover, obtained results, including overall electrical system efficiencies, were compared over WLTC and the local route. Significant improvements both in consumption and CO2 values were concluded. It is also concluded that using the hybrid system in local route with higher proportion of in-city driving conditions caused higher improvements despite a slight decrease in overall electrical system efficiency.

Simulation Models of Skidder Conventional and Hybrid Drive

The paper presents a hypothetical conversion of a conventional cable skidder powertrain to its hybrid version. Simulations of skidder operation were made for two existing forest paths, based on the technical characteristics of the engine, transmission system and the characteristics of the winch. Fuel and time consumption were calculated per working cycle considering the operating conditions (slope, load mass). The model was then converted to a hybrid version by adding a battery energy storage system in parallel with the electrical power generator and by employing an energy management control strategy. The dimensions of the battery and the power generator were chosen based on the characteristics of the existing winch with the aim of completely taking over its operation. The management strategy was selected using the specific fuel consumption map. All simulations were repeated for the hybrid drive under the same operating conditions. The results show that fuel savings of around 13% can be achieved with the selected hybrid drive and steering strategy.

A Reverse-Engineering Method for Powertrain Parameters Characterization Applied to a P2 Plug-In Hybrid Electric Vehicle with Automatic Transmission

<div class="section abstract"><div class="htmlview paragraph">Over the next decade, CO2 legislation will be more demanding and the automotive industry has seen in vehicle electrification a possible solution. This has led to an increasing need for advanced powertrain systems and systematic model-based control approaches, along with additional complexity. This represents a serious challenge for all the OEMs. This paper describes a novel reverse engineering methodology developed to estimate relevant powertrain data required for fuel consumption-oriented hybrid electric vehicle (HEV) modelling. The estimated quantities include high-voltage battery internal resistance, electric motor and transmission efficiency, gearshift thresholds, torque converter performance diagrams, engine fuel consumption map and front/rear hydraulic brake torque distribution. This activity provides a list of dedicated experimental tests, to be carried out on road or on a chassis dynamometer, aiming at powertrain characterization thanks to a suitable post-processing algorithm. In this regard, the methodology was applied on a P2 Diesel Plug-in HEV equipped with a 9-speed AT. Voltage and current sensors are used to measure the electrical power exchanged between battery and electric motor; a torque sensor on the propeller shaft measures the total torque coming out from the automatic transmission. The hydraulic pressures in the four brake calipers are measured and CAN data is logged. The results of the testing campaign are then presented and discussed. Functional models of powertrain subsystems are introduced and their parameters estimated using least square method. The good match between models and experimental data proved that the proposed methodology, if properly adapted to the specific layout, is a suitable tool for powertrain parameter estimation.</div></div>

Ecological Adaptive Cruise Control for Vehicles With Step-Gear Transmission Based on Reinforcement Learning

In this paper an ecological adaptive cruise controller to reduce the fuel consumption and ensure the safe inter-vehicle distance for vehicles with step-gear transmissions is presented. An optimal control strategy using reinforcement learning with a novel actor-gear-critic architecture is proposed to obtain the continuous traction force trajectory and the discrete gear shift schedule. The traction force is determined from an actor network to maintain a desired inter-vehicle distance which improves the driving safety in a car-following process. The gear shift schedule is derived from a gear network to reduce the fuel consumption. The control strategy is model-free and allows continuous online learning for different driving situations without look-ahead velocity information. Particularly the nonlinear vehicle dynamics, the nonlinear transmission efficiency map for different gear ratios, and the nonlinear fuel consumption map are learned for fuel consumption reduction. The proposed controller is evaluated for different driving scenarios to demonstrate its robustness. Furthermore simulation comparisons for different gear shift schedules and velocity trajectories are given underling the advantages in terms of fuel economy and driving safety.

Model-based estimation of light-duty vehicle fuel economy at high altitude

In order to estimate the light-duty vehicle fuel economy at high-altitude areas, the coast-down tests of a passenger car on level road were conducted at different elevations, and the coast-down resistance coefficients were calculated. Furthermore, a fuel economy model for a light-duty vehicle adopting backward simulation method was developed, and it mainly consists of vehicle dynamic model, internal combustion engine model, transmission model, and differential model. The internal combustion engine model consists of the brake-specific fuel consumption maps as functions of engine torque and engine speed, and the brake-specific fuel consumption map near sea level was constructed based on engine experimental data, and the brake-specific fuel consumption maps at high altitudes were calculated by GT-Power Modeling of the internal combustion engine. The fuel consumption rate was calculated from the brake-specific fuel consumption maps and brake power and used to calculate the fuel economy of the light-duty vehicle. The model predicted fuel consumption data met well with the test results, and the model prediction errors are within 5%.

Investigation of onboard fuel separation for passenger vehicles

Improving the efficiency of the internal combustion engine will continue to play an important role in reducing greenhouse gas (GHG) emissions in the transport sector. One promising technology that offers improved engine efficiency is the Octane-on-Demand (OoD) concept. This engine-fuel system makes use of two fuels with different octane quality to match the specific anti-knock requirements of the engine under different driving conditions. Previous research has mainly focused on enabling the OoD concept using a two-tank system, whereby the consumer must refuel the vehicle with two different fuels.In this work, a volatility-based flash distillation system is instead utilized to separate a market gasoline (RON 91) into lower-octane and higher-octane fuel streams. Single cylinder engine tests are performed to develop fuel consumption maps for both the separated and unseparated fuels. These maps are then used to evaluate the fuel economy and GHG emissions of a light-duty vehicle across different driving conditions. The fuel separation system is shown to offer improvements in both fuel economy and GHG emissions when compared to the equivalent vehicle utilizing the unseparated market gasoline. This suggests that onboard fuel separation could be an enabling technology for improving the efficiency of future passenger vehicles.

Spatial distribution of building energy use in the United States through satellite imagery of the earth at night

Despite the importance of geospatial analysis of energy use in buildings, the data available for such exercises is limited. A potential solution is to use geospatial information, such as that obtained from satellites, to disaggregate building energy use data to a more useful scale. Many researchers have used satellite imagery to estimate the extent of human activities, including building energy use and population distribution. Much of the reported work has been carried out in rapidly developing countries such as India and China where urban development is dynamic and not always easy to measure. In countries with less rapid urbanization, such as the United States, there is still value in using satellite imagery to estimate building energy use for the purposes of identifying energy efficiency opportunities and planning electricity transmission. This study evaluates nighttime light imagery obtained from the VIIRS instrument aboard the SUOMI NPP satellite as a predictor of building energy use intensity within states, counties, and cities in the United States. It is found that nighttime lights can explain upwards of 90% of the variability in energy consumption in the United States, depending on conditions and geospatial scale. The results of this research are used to generate electricity and fuel consumption maps of the United States with a resolution of less than 200 square meters. The methodologies undertaken in this study can be replicated globally to create more opportunities for geospatial energy analysis without the hurdles often associated with disaggregated building energy use data collection.

Architecture Optimization of Hybrid Electric Vehicles with Future High-Efficiency Engine

The great development of engine technologies can help to improve the engine characteristics and performance: a better thermal efficiency and an extending fuel economy area, which will subsequently decrease the fuel consumption and thus influence the overall architecture of the vehicle. In this paper, an investigation is carried out to assess the influence of the high-efficiency engine on the transmission gear numbers. First, according to the relevant studies and the integration of the advanced engine technology, a future engine fuel consumption map is obtained, based on which, the preliminary simulations are applied to explore the best match between the transmission and the proposed future engine from the perspective of fuel consumption. The simulation results indicate that the transmission with four gears is the best option to match the future engine while maintaining good fuel economy and meeting the driving demands. Then, based on this conclusion, a new hybrid powertrain architecture, which includes four gears for the engine, is introduced and analyzed in detail, with the advantage of seamless gear shift due to the compensation torque of the motor. Finally, to further examine the fuel economy and gear shift quality of the proposed powertrain, the dynamic model is established and the simulation results demonstrate that the new powertrain architecture shows a good fuel consumption performance and the gear shift process can be achieved without power interruption.

Hybrid Vehicle Energy Management: Singular Optimal Control

Hybrid vehicle energy management is often studied in simulation as an optimal control problem. Under strict convexity assumptions, a solution can be developed using Pontryagin's minimum principle. In practice, however, many engineers do not formally check these assumptions resulting in the possible occurrence of so-called unexplained “numerical issues.” This paper intends to explain and solve these issues. Due to the binary controlled-state variable considered (e.g., switching on/off an internal combustion engine) and the use of a lookup table with linear interpolation (e.g., engine fuel consumption map), the corresponding Hamiltonian function can have multiple minima. Optimal control is not unique. Moreover, it is defined as being singular. Consequently, an infinite number of optimal state trajectories can be obtained. In this paper, a control law is proposed to easily construct a few of them.

Synergistic engine-fuel technologies for light-duty vehicles: Fuel economy and Greenhouse Gas Emissions

Advanced engine technologies will play a central role in achieving future greenhouse gas (GHG) emissions targets for light-duty vehicles. However, these technologies will place greater emphasis on optimizing the engine and fuel as a synergistic system, since many technologies will require higher octane gasolines to realize their full social and environmental benefits. The most extreme example of a synergistic engine-fuel system is the Octane-on-Demand concept. This technology platform makes use of an oil-derived fuel at low and intermediate loads where the octane requirement of the engine is comparatively low, while a second high octane fuel is introduced at higher loads to suppress knock. This paper presents the first comprehensive study of vehicle fuel economy and well-to-wheel GHG emissions for the Octane-on-Demand concept with respect to a regular grade E10 gasoline (RON 93) and a high octane E30 gasoline (RON 101). Experimental fuel consumption maps are first used to evaluate the drive cycle fuel economy and GHG emissions for a light-duty vehicle equipped with two alternative powertrains. The upstream GHG emissions arising from the production of the fuels are then quantified, with consequent uncertainties assessed using Monte Carlo analysis based on probability distribution functions for critical input parameters. The results demonstrate that the Octane-on-Demand concept used in conjunction with either methanol or ethanol generally provides comparable well-to-wheel GHG emissions to the high octane E30 gasoline, with up to a 10% improvement in the vehicle fuel economy. The use of a non-traditional engine calibration strategy that maximizes the trade-off between thermal efficiency and fuel energy density also enables the amount of high octane fuel required to suppress knock to be reduced significantly. This increases the distance that the vehicle can be driven before the secondary tank requires refueling by a considerable margin, but comes at the expense of marginally higher well-to-wheel GHG emissions than could otherwise be achieved. These findings are shown to be largely insensitive to uncertainties in the upstream fuel production GHG emissions, with the exception of the land use change (LUC) for bioethanol. Overall, this study has implications for the design of engine-fuel systems for future light-duty vehicles.

Perodua Myvi engine fuel consumption map and fuel economy vehicle simulation on the drive cycles based on Malaysian roads

This paper presents the fuel consumption engine map for a 1.3L Perodua Myvi passenger car. The engine dynamometer and the engine throttle are controlled, to create the operating conditions for the engine map. Interpolation work is done in MATLAB, to create a 3D fuel consumption engine map. The engine map is used in a fuel-economy estimation simulation, using the city and the highway drive cycles based on Malaysian roads. The fuel economy values generated from the simulations are similar to experimental fuel consumption results.