Vehicle Working Conditions Research Articles

High Stability Control of a Magnetic Suspension Flywheel Based on SA-BPNN and CNN+LSTM+ATTENTION

Compared to traditional, static-based flywheel systems, vehicle-mounted magnetic suspension flywheels face more complex operating conditions, and existing control strategies usually regard disturbances in vehicles under different operating conditions to be the same problem. Therefore, it is necessary to determine the interference from complex operating conditions and reasonably distinguish among them under different operating conditions to provide flywheel systems with strong stability (the rotor offset was less than 0.025 mm). Thus, this paper proposes a high-stability control strategy for flywheels based on the classification of vehicle-driving conditions and designs its control strategy by taking the vehicle-mounted magnetic suspension flywheel with a virtual inertia spindle as an example. First, according to the different vehicle working conditions and the varying interference intensities affecting the flywheel system, the working mode is divided into four modes. Considering the obvious differences in each working mode, it is proposed to use BP neural network optimization based on the simulated annealing algorithm (SA-BPNN) to determine the flywheel’s working condition. A relatively simple neural network can improve the response speed of the whole system. It also has a good effect. Secondly, it is proposed to use deep learning models based on convolutional neural networks, long short-term memory networks and attention mechanisms (CNN+LSTM+ATTENTION) to train the corresponding control parameters under each working condition to judge and predict the control parameters under different working conditions. Three evaluation parameters are used to evaluate the training results, and all achieved good results. Finally, the classification of working conditions and performance tests are carried out. The experimental results show the effectiveness and superiority of the proposed control strategy.

Life Cycle Assessment of Plug-In Hybrid Electric Vehicles Considering Different Vehicle Working Conditions and Battery Degradation Scenarios

This study establishes a life cycle assessment model to quantitively evaluate and predict material resource consumption, fossil energy consumption and environmental emissions of plug-in hybrid electric vehicles (PHEVs) by employing the GaBi software. This study distinguishes the environmental impact of different vehicle working conditions, power battery degradation scenarios, and mileage scenarios on the operation and use stages of PHEVs, BEVs, and HEVs. The findings indicate that under urban, highway, and aggressive driving conditions, PHEVs’ life cycle material resource and fossil fuel consumption exceed that of BEVs but are less than HEVs. Battery degradation leads to increased material resource consumption, energy use, and environmental emissions for both PHEVs and BEVs. When the power battery degrades to 85%, the material resource and fossil energy consumption during the operation and use phase increases by 51.43%, 72.68% for BEVs and 29.37%, 36.21% for PHEVs compared with no degradation, respectively, indicating that the environmental impact of BEVs are more sensitive than those of PHEVs to the impact of power battery degradation. Among different mileage scenarios, PHEVs demonstrate the lowest sensitivity to increased mileage regarding life cycle material resource consumption, with the smallest increase. Future projections for 2025 and 2035 suggest life cycle GWP of HEV, PHEV and BEV in 2035 is 1.21 × 104, 1.12 × 104 and 1.01 × 104 kg CO2-eq, respectively, which shows reductions of 48.7%, 30.9% and 36.1% compared with those in 2025. The outcomes of this study are intended to bolster data support for the manufacturing and development of PHEV, BEV and HEV under different scenarios and offer insights into the growth and technological progression of the automotive sector.

Power distribution optimization of a fully active hybrid energy storage system configuration for vehicular applications

As an effective solution to limitations of vehicle-mounted single-battery energy storage system, the super-capacitor (SC)/battery hybrid energy storage system (HESS) is a research topic that is receiving more and more attention and emphasis from scientific research institutions in various countries. Despite its current widespread recognition in vehicle field, low energy utilization and low work efficiency under the variable working conditions of vehicle particularly limit its practical application and popularity. The power distribution optimization is critical to obtain a HESS with superior performance such as good reliability and high efficiency. This work presents a variable voltage SC/battery HESS configuration based on series-parallel switching technology of SCs, which can realize multiple working modes of the HESS to meet the needs of complex and variable working conditions of vehicle, and increase the power output range of the HESS. Taking the proposed HESS as application object, an energy efficiency model of the HESS is established, as well as an optimization model with energy efficiency maximization as the optimization goal. Power distribution optimization methods based on Genetic Algorithm (GA) and Gray Wolf Algorithm (GWA) respectively are proposed, and compared to fixed rules-based and fuzzy-based control strategies. The optimization algorithms-based methods can almost obtain the highest energy efficiency (from 91% to 87%) within the entire power demand range (from 100 W to 500 W), have obvious advantages in comparison of several other methods. The efficiency in the whole New European Driving Cycle (NEDC) driving condition exceeds 85%, and in the most periods of this driving condition exceeds 90%. The results clearly demonstrate validity and reliability of the power distribution optimization methods proposed in this paper. This work aims to solve the problem concerning the optimal power distribution based on energy efficiency maximization of HESS, and provide solution for improving the power output capacity and energy utilization of HESS. We firmly believe that this work has considerable theoretical significance and engineering application value for the promotion of HESS.

Dynamics simulation of AVL CRUISE pure electric vehicle and analysis of influencing factors of urban cycle conditions

Nowadays, the world is in a critical period of energy utilization transformation, and the development of the automobile industry is greatly influenced by energy. New energy vehicles represented by electric vehicles are the new direction of the development of the automobile industry in the future. The AVL-Cruise software system has many features, such as simple modeling function, visual design analysis and research, which provides technical support for the simulation projects under complex working conditions of vehicles. Taking the front wheel drive pure electric vehicle as an example, this paper uses AVL_CRUISE software to establish the power system model of electric vehicle, and uses this software to calculate its dynamic performance. The simulation results show that the maximum speed is 178km/h, the 100km acceleration time is 11.59s, and the maximum climbing slope is 30%. The simulation results show that the electric vehicle meets good design requirements. The total energy consumption is 4608KJ. By changing the data such as air resistance coefficient and vehicle quality, the relationship between the dynamic performance and the influencing factors is explored. The corresponding expression is obtained by fitting the data, which provides data support and reference experience for the optimization of electric vehicles.

Quantitative diagnosis of PEMFC membrane humidity with a vector-distance based characteristic mapping approach

Membrane dehydration or flooding fault is one of the main causes of performance degradation for Proton Exchange Membrane Fuel Cell (PEMFC). Effective and accurate diagnosis of membrane humidity faults is necessary to ensure the optimal operation of PEMFC. However, the research gap lies in the quantitative diagnosis of membrane humidity values under complex working conditions of vehicle application. In this paper, characteristic spaces describing membrane humidity are established as multi-matrices consisting of the amplitude spectrum extracted from output voltage and pressure drop in PEMFC under different vehicle working conditions. In the multi-matrix, the relationship between the membrane humidity fault levels and the amplitude spectrum can be described as characteristic vectors. Then, a vector-distance based mapping approach under multi-information characteristic vectors is developed to describe the relationship between characteristic vectors and humidity values. Consequently, the membrane humidity of the PEMFC can be quantitatively evaluated with the projection length between characteristic vector and humidity-change vector, with which the humidity value of PEMFC membrane can be obtained under different working conditions in real-time. The performance of the proposed a vector-distance based characteristic mapping approach is verified by New European Driving Cycle (NEDC) and World Light Vehicle Test Procedure (WLTP) working conditions. The diagnostic results show that the quantitative humidity diagnosis strategy can achieve diagnostic accuracy of 98.16% and 92.24% under NEDC and WLTP working conditions, compared with the single information of output voltage-based and pressure drop-based diagnosis strategy, the accuracy is improved by 4.37% and 4.08% respectively.

Finite element analysis and optimization of tractor gearbox body under various kinds of working conditions

As the main component of the tractor gearbox, the box has the functions of shifting operation and carrying the cab, it also carries part of the framework function. If the strength, stiffness, or vibration characteristics of the box do not meet the allowable requirements, large vibration and noise may occur, and even there is the possibility of fracture in some limiting conditions. To solve this problem, according to the structural parameters of a gearbox, the three-dimensional model of the box was established by using the three-dimensional modeling software Creo. According to the dangerous degree of the transmission load when the tractor is working normally, three vehicle working conditions are selected: a round of suspension conditions, farm tool lifting conditions, and emergency turning conditions. In addition, according to the transmission ratio of each gear meshing inside the gearbox, two gear conditions are selected: gear condition one and reverse gear condition one. The forces of the box under these extreme conditions are analyzed. The static analysis and modal analysis of the tractor gearbox are carried out by using the Static Structural module of ANSYS Workbanch. The deformation, equivalent stress distribution, and modal vibration frequency of the gearbox are tested. The topology optimization method is used to improve structural defects and reduce box quality. The results show that the weight of the optimized box reduces by 8.44%, the deformation decreased by 15.89%, and the equivalent stress decreased by 18.34%. The strength and stiffness of the box are improved, the quality is lightweight, the waste of resources is reduced, and the heat dissipation performance and fracture resistance of the box are enhanced.

A Deep Transfer NOx Emission Inversion Model of Diesel Vehicles with Multisource External Influence

By installing on-board diagnostics (OBD) on tested vehicles, the after-treatment exhaust emissions can be monitored in real time to construct driving cycle-based emission models, which can provide data support for the construction of dynamic emission inventories of mobile source emission. However, in actual vehicle emission detection systems, due to the equipment installation costs and differences in vehicle driving conditions, engine operating conditions, and driving behavior patterns, it is impossible to ensure that the emission monitoring data of different vehicles always follow the same distribution. The traditional machine learning emission model usually assumes that the training set and test set of emission test data are derived from the same data distribution, and a unified emission model is used for estimation of different types of vehicles, ignoring the difference in monitoring data distribution. In this study, we attempt to build a diesel vehicle NOx emission prediction model based on the deep transfer learning framework with a few emission monitoring data. The proposed model firstly uses Spearman correlation analysis and Lasso feature selection to accomplish the selection of factors with high correlation with NOx emission from multiple sources of external factors. Then, the stacked sparse AutoEncoder is used to map different vehicle working condition emission data into the same feature space, and then, the distribution alignment of different vehicle working condition emission data features is achieved by minimizing maximum mean discrepancy (MMD) in the feature space. Finally, we validated the proposed method with the diesel vehicle OBD data that were collected by the Hefei Environmental Protection Bureau. The comprehensive experiment results show that our method can achieve the feature distribution alignment of emission data under different vehicle working conditions and improve the prediction performance of the NOx inversion model given a little amount of NOx emission monitoring data.

Effect of tire slip losses on the energy demand and fuel consumption of a light-duty vehicle

The contribution of the tire-road slip of traction wheels to the total resistance opposing the motion of a light-duty commercial vehicle has been investigated through the simulation of several homologation and custom driving cycles. The calculation of the contribution of the tire slip losses was based on the estimation of the longitudinal tire slip, by means of Pacejka’s MF5.2 tire model. In this work, the computational steps required to evaluate this contribution were implemented in a previously developed fuel consumption simulation tool. Simulations were performed under several vehicle loading conditions and tire inflation pressures on traction and non-traction wheels, and considering different tire-road adherence conditions, in order to obtain a characterization of the tire slip losses over a wide range of working conditions. An analysis of the results shows that, although the contribution of tire slip losses to the total vehicle energy demand and fuel consumption may be relevant – especially under low-load, low adherence conditions – the sensitivity of the average on-cycle vehicle energy/fuel consumption to changes in the tire inflation pressure is only affected slightly by tire slip losses. Therefore, tire slip losses can be neglected in practice, when the aim of a simulation is to optimize the tire pressure to achieve average vehicle working conditions over a driving cycle.

A variable-voltage ultra-capacitor/battery hybrid power source for extended range electric vehicle

Energy storage system of extended-range electric vehicle faces great challenges in working efficiency and energy utilization to meet the requirement of various working conditions of vehicle, efficient ultra-capacitor/battery hybrid power source is an effective way to tackle these challenges. This paper takes an ultra-capacitor/battery hybrid power source based on series-parallel switchover technology of ultra-capacitors as the research object, launches a fundamental study on its operation principle, control method and simulation/experimental verification. A significative series-parallel switchover technology of ultra-capacitors is adopted to achieve variable-voltage output of the hybrid power source, which overcomes the technical limitation of the existing hybrid power source with only a fixed working voltage output, enables the voltage-conversion ratio of the power converter to be controlled in a highly efficient range. The bi-directional four working modes of the hybrid power source that can fit the various working conditions of vehicle are analyzed. Following this, an incremental digital PID controller suitable for this energy storage system is presented. After that, a comprehensive comparison and analysis of the simulation and small-power experiment based on the simulated working conditions of vehicle are performed. The results have verified the feasibility and effectiveness of the proposed hybrid power source and its control method.

Automatic adjustment of tire inflation pressure through an intelligent CTIS: Effects on the vehicle lateral dynamic behavior

The paper investigates the effect of tire inflation pressure on the lateral dynamics of a passenger car, and presents a possible control-oriented methodology aimed at adapting tire pressure to the current vehicle loading condition targeting a reference characteristic. Starting from the tire characteristics at several inflation pressure levels, the paper investigates the effect of changing selectively tire pressure on each of the two axles, through theoretical calculation of the curvature gain based on the computation of the derivatives of stability, and compares the obtained sensitivity to the results of a multibody simulation model validated through on-track tests. Finally, the work presents a possible algorithm that could be implemented on-board vehicle ECU to provide, for the current loading condition of the vehicle, a tire pressure combination that targets a specific lateral dynamics characteristic. The algorithm is intended as part of the control logic of an intelligent Central Tire Inflation System (CTIS) able to adjust automatically tire pressure according to the actual vehicle working conditions.

A New Method of Insulation Detection on Electric Vehicles Based on a Variable Forgetting Factor Recursive Least Squares Algorithm

The lithium-ion batteries of an electric vehicle belong to a high-voltage direct-current system. The high-voltage insulation performance of electric vehicles is very important for their safe operation. To solve the problems of slow response and the poor estimation accuracy of the insulation resistance under complex vehicle working conditions, a real-time insulation resistance detection method based on the variable forgetting factor least squares algorithm is proposed in this paper. Based on the low-frequency signal injection method and considering the influence of the Y capacitor, the corresponding circuit model and the mathematical model of the reflected wave voltage are established, and the mathematical model is linearized by a first-order Taylor expansion. By analyzing the influence of the forgetting factor on model parameter identification and setting appropriate shutdown criteria, the least squares algorithm with a variable forgetting factor is designed to quickly and accurately estimate the insulation resistance and Y capacitance. The experimental test results show that the proposed method can quickly track the changes in the insulation resistance and Y capacitance under the condition of noise interference and that the root mean square error of the estimation resistor is within 0.012.

An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery

An accurate state-of-charge (SOC) can not only provide a safe and reliable guarantee for the entirety of equipment but also extend the service life of the battery pack. Given that the chemical reaction inside the lithium-ion battery is a highly nonlinear dynamic system, obtaining an accurate SOC for the battery management system is very challenging. This paper proposed a gated recurrent unit recurrent neural network model with activation function layers (GRU-ATL) to estimate battery SOC. The model used deep learning technology to establish the nonlinear relationship between current, voltage, and temperature measurement signals and battery SOC. Then the online SOC estimation was carried out on different testing sets using the trained model. The experiments in this paper showed that the GRU-ATL network model could realize online SOC estimation under different working conditions without relying on an accurate battery model. Compared with the gated recurrent unit recurrent neural (GRU) network model and long short-term memory (LSTM) network model, the GRU-ATL network model had more stable and accurate SOC prediction performance. When the measurement data contained noise, the experimental results showed that the SOC prediction accuracy of GRU-ATL model was 0.1–0.4% higher than the GRU model and 0.3–0.7% higher than the LSTM model. The mean absolute error (MAE) of SOC predicted by the GRU-ATL model was stable in the range of 0.7–1.4%, and root mean square error (RMSE) was stable between 1.2–1.9%. The model still had high prediction accuracy and robustness, which could meet the SOC estimation in complex vehicle working conditions.

Optimization methods of operating costs on road transport in international transportation

Transport processes research are closely related to the economic and operational performance of road transport. During planning road transportation, the most important factor is the transport services cost. Known fact that most of the transportation costs are car fuel. The share of car fuel in the transport tariff cost can be 25-50% (depending on the working conditions of vehicles). Therefore, research that is associated with improving the vehicles efficiency by reducing variable costs is appropriate and relevant. The article presents the results of research on the planning of transport processes in the road transport performance in international road transport for the return flight and calculating methods the demand for the amount of fuel purchased in different countries with different prices. The author presents the results of theoretical and experimental research the adequacy of the mathematical model (methodology). The specified mathematical model consists in substantiation of a technique of calculation of quantity of car fuel, definition of the variable factors influencing volumes of fuel expenses, calculation of correction factors (total) received on the basis of technical and operational indicators of vehicles and the influencing factors connected with features of road transport work. . The second part of the research contains the results of the development of an algorithm development to calculate the total cost of automotive fuel, which was purchased at different prices in several countries. Thus, the main purpose of research is the task of substantiating the algorithm (mathematical model – methodology) for determining fuel costs by the criterion of the highest use of fuel in countries at the lowest prices.

ОГЛЯД ЗАВДАНЬ МАРШРУТИЗАЦІЇ, ЩО ЗВОДЯТЬСЯ ДО ЗАДАЧІ КОМІВОЯЖЕРА

The solution to the problem of improving the management of the transport process depends not only on the level of modernization of vehicles and the degree of use of modern information technologies, but also on the choice of routes that reduce the cost of transporting goods and passengers. Actual working conditions of vehicles in road networks put forward a number of tasks for optimizing closed routes, which are based on the classic routing problem (VRP - Vehicle Routing Problem).VRP is one of the generalizations of the hard-to-solve traveling salesman problem. The traveling salesman task is NP-complete. It refers to the main tasks of combinatorial optimization and, forming a continuously replenished set of applications and generalizations, remains an urgent research topic. An exact solution to the traveling salesman problem can be found only by reducing the enumeration of the type of branches and boundaries, which are not always applicable in operational planning by vehicle traffic. Therefore, the development of new and improvement of currently known methods for solving routing problems, reducible to the traveling salesman problem, and their software implementation is both a theoretical and practically important problem.The article considers the class of routing problems reducible to the traveling salesman problem. It is shown that optimization tasks for closed routes (routing problems), which are an important part of transport logistics, occupy key positions in the management of the processes of moving goods and passengers with the support of modern information technologies. An obvious feature that combines the considered list of routing problems (the symmetric traveling salesman problem, the problem of packing in containers, the school bus problem) is that they are formulated as generalizations or variants of the NP-complete traveling salesman problem with restrictions that narrow the scope of feasible solutions. The strongest restrictions become insufficient solvability conditions, stimulating interest in the study of combinatorial optimization problems associated with the traveling salesman problem.

The design and implementation of vehicle-mounted satellite communication on the move based on MEMS gyro

As an important satellite communication technology, mobile communication has broad application prospects in the field of military and civilian applications. Low cost and high reliability have become one of the goals and difficulties in engineering design. In order to overcome the shortage of course information provided by inertial navigation or electronic compass, a beacon space search algorithm without course information is designed. A low cost gyro stabilization platform based on ARM and MEMS gyroscope is designed for the high mobility and complex working conditions of vehicles. The servo motor and photoelectric encoder are used to construct the multi-closed-loop control system to improve its fast response ability, and the pitch compensation and beacon space search algorithm are used to improve the precision of the system for star and tracking. It has good engineering application and popularization value.

Investigation of the effect of humidity at both electrode on the performance of PEMFC using orthogonal test method

Abstract Under the vehicle working conditions, operating temperature and relative humidity (RH) of gas supplies have an important influence on the performance and durability of PEMFC. In this paper, the influence of hydrogen RH, air RH, operating temperature and air stoichiometric ratio on the performance of PEMFC was studied with the help of 5 factors and 4 levels orthogonal test table. The fifth factor was set to be empty to carry out the variance analysis. Using variance analysis method, the influence degree of random errors on the experiments was analyzed. The credibility of the experimental results was verified along with the polarization curve and the electrochemical impedances spectroscopy (EIS) results. The experiment results show that the most important factor which affects the performance of PEMFC is air stoichiometric ratio, followed by air RH, and then the operating temperature. The effect of hydrogen RH on PEMFC performance is minimal and negligible. Low hydrogen RH can be used during operation to reduce energy consumption of auxiliary system.

Consistency analysis of polymer electrolyte membrane fuel cell stack during cold start

This paper investigates the cold start characteristic of the polymer electrolyte membrane fuel cell stack in the constant voltage startup mode. The stack is used in actual vehicle working conditions, so it is practical significance to study the cold start characteristics of the stack. The evolution of the temperature and current density distribution at different regions in the constant voltage mode of the stack are studied. Consistency analyzes from two dimensions, in-plane and through-plane are studied. Then, the performance degradation of the stack after the cold start is explored through the polarization curve and the voltage consistency.The cold start characteristics and performance degradation mechanism of the stack are found. The stack has better cold start performance than that of the single cell, which can realize a rapid cold start at −15 °C in 95 s. Also, the performance of the single cell in the middle position is obviously better than that of the single cell on both sides. The internal behavior of each single cell during cold start process is different and the performance consistency is poor after the failed cold start. The performance degradation gradually decreases from the single cell near the inlet to the single cell far away from the inlet. This paper can provide direction for the optimization cold start strategy.

ROAD PAVEMENT LONGITUDINAL EVENNESS QUANTIFICATION AS STATIONARY STOCHASTIC PROCESS

One of the requirements concerning pavement quality is the evenness of its surface. Pavement unevenness has a random character and has an adverse influence to rolling resistance, tyre–pavement coherence, safety and the driving comfort. Knowledge of “longitudinal unevenness” has been long recognized as an important criteria of road performance, not only for safety by causing vehicle vibrations and affecting ride comfort but also as a major factor in pavement deterioration and working conditions of vehicles. The paper presents two original devices for the measurement of pavement longitudinal unevenness designed as a reaction to results and experiences gathered from a few years’ research activities, measurements and evaluations of road pavement evenness carried out in the authors' work place (University of Žilina – UNIZA). The first equipment has been designed as a single-wheel trailing vehicle and has been constructed on the Double-mass Measuring Set (DMS) principle and it is referred to as UNIZA single-wheel vehicle JP VSDS. The main reason for designing the device were authors’ findings that the reference quarter car model (used for calculation of International Roughness Index – IRI) can provide evaluation, which can be in contradiction with ride safety. This fact is determined by overvaluation of the short wavelengths and undervaluation the longer wavelengths by reference model. The second one is a profiler with very high resolution of surface scanning using mathematical models for unevenness evaluation. The device is referred to as Dynamic Road Scanner (DRS). The reason for designing of this equipment was in the first place insufficient repeatability of transversal unevenness measurements of device used by Slovak Road Administration, but for the purpose of correctness and measurements accuracy verifying were also results of longitudinal unevenness measurements compared. The paper presents results of evaluation by international established dynamic quantifiers of longitudinal unevenness based on measurements performed by these devices on three selected road sections in Slovakia. In the next part of the paper are compared IRI values obtained by mathematical calculations using reference quarter car model “driving” on road section profile measured by geodetic survey with IRI values obtained by conversion of the unevenness degree C (measured by UNIZA single-wheel vehicle JP VSDS) and IRI values measured by profilometer DRS.

Torque Optimal Allocation Strategy of All-wheel Drive Electric Vehicle

The difference in the efficiency distribution of the shaft motor caused by the optimal load matching and motor manufacturing process, the traditional torque average distribution strategy is not applicable to the torque distribution of the all-wheel drive power system. Aiming at the above problems, this paper takes the energy efficiency of power system as the optimization goal, and proposes a dynamic allocation method to realize the torque distribution method of electric vehicle all-wheel drive power system, and analyzes and verifies the adaptability of this optimization algorithm in China urban passenger vehicle working conditions. The simulation results show that compared with the torque average distribution method, the proposed method can effectively solve the problem that the difference of the efficiency distribution of the two shaft motors in the power system affects the energy consumption of the power system. The energy consumption rate of the proposed method is reduced by 5.96%, respectively, compared with the average distribution method under the urban road driving condition of Chinese passenger vehicles.

Potential impact of active tire pressure management on fuel consumption reduction in passenger vehicles

Active tire pressure management, through an automatic, electro-pneumatic, central tire inflation system, is here proposed as a means of improving fuel consumption in passenger vehicles, as well as safety and drivability. A brief description of the active tire pressure control system, which has been set up at the Politecnico di Torino, is provided as a reference. Different strategies, aimed at reducing rolling resistance, through inflation pressure management, under specific vehicle working conditions, are then illustrated. The fuel benefits that can be achieved by adopting these strategies in passenger vehicles are studied by means of computer simulations using a proprietary software for vehicle performance and fuel consumption estimation. Coast-down coefficients, evaluated experimentally during deceleration tests on a closed track, are generally available at the reference tire pressure prescribed by the original equipment manufacturer of the vehicle. These fixed coefficients can then be used to describe the vehicle in simulation environments. LaClair’s relation, which illustrates the influence of tire inflation pressure on rolling resistance, has therefore been used to recalculate the coast-down coefficients as functions of the tire pressure. This has allowed fuel consumption simulations to be performed on the reference B-segment passenger car under different working conditions. In particular, the following pressure management strategies have been studied: adaptation of the inflation pressure to the vertical load, variation of the inflation pressure during tire warm-up, and adjustment of the inflation pressure, according to the average speed (urban/highway driving). The performed simulations have demonstrated that if the standard tire pressure is maintained, fuel consumption could be reduced by up to 2% in real-world driving; further advantages could be obtained by varying the target pressure as a function of the current working conditions of the vehicle.