Vehicle Simulation Model Research Articles

Integrated electric vehicle design and simulation: Combining a mechanical and electrical model using Simscape/Multibody

This paper presents a novel methodology for modeling and implementing an electric vehicle (EV) simulation model using the Multibody approach. The proposed method provides a robust solution for studying EV performance, taking into consideration the dynamic behavior of its complex mechanical system. The mechanical design was carried out using SOLIDWORKS software and then exported to the Simscape/Multibody platform. This process was performed to combine the obtained mechanical design with an electrical model, developed using the Simscape and MATLAB/Simulink toolbox, in the same simulation platform. The proposed EV is powered by an electromechanical PowerDrive based on a permanent magnet synchronous machine (PMSM), supplied by a hybrid energy storage system (HESS). The implemented HESS, operating in a fully active topology, is controlled by a deterministic rule-based energy management system (EMS). To evaluate the proposed model performance, simulations were conducted under ECE-15.

Holistic Sensitivity Analysis for Long-Term Energy Demand Prediction of Battery Electric Vehicles

AbstractAccurate and robust range estimation algorithms for battery electric vehicles have the potential to reduce range anxiety, increase the acceptance of lower-range vehicles, and improve the overall driving experience. However, developing such algorithms faces challenges due to the complexity of the driver-vehicle-environment system and the multitude of factors influencing a vehicle's energy demand. To address these challenges, this paper introduces a sensitivity analysis focused on driver- and environment-related factors, which are notably difficult to predict. Employing a global sensitivity analysis for factor prioritization, this study delineates and assesses the parameters and their value distributions using a validated vehicle simulation model. The co-simulation of a powertrain and an auxiliaries model enables the parameter-specific investigation of parameters related to the thermal system. The results are scenario-individual parameter rankings that show the importance of the considered factors in prediction algorithms and guide the strategy for the development of these algorithms. The acceleration behavior of the driver, often emphasized in literature, is shown to be of secondary importance to energy consumption. Moreover, factors such as air density and wind speed are identified as crucial in highway driving scenarios, whereas outside temperature and the probability of stopping at traffic lights are critical in urban settings. For validation purposes, the resulting rankings of the sensitivity study are validated by means of a convergence analysis.

Modeling electric Vehicle's and improving battery lifetime using AI tools case study: Postal cars

The rapid development in the field of electric vehicles requires a careful evaluation of the design process. The presence of a simulation model of the electric vehicle can effectively detect many faulty areas during the development process without risks. The MATLAB and Simulink environment is considered one of the most important tools used in the simulation process. In this paper, we will present the model of electric car used for transporting postal parcels (postal cars). The model includes simulating the operation of a permanent magnet synchronous electric motor. We will assume that the car is moving according to a driving cycle. The results will show the torque forces required to achieve the required speed. We will further calculate the traction and the resistance forces during the driving cycle and the engine efficiency in addition. Perhaps the most important problem facing electric car designers is calculating the amount of energy consumed from the battery or hydrogen fuel, and this is what was achieved as the result of the simulation process in this research. In the end, use one of the artificial intelligence tools (fuzzy controller) to improve battery life by providing the electric car driver with an alert system that will increase the ability to monitor the battery condition and thus increase battery life. The benefit of this paper emerges in realizing the importance of modeling and using simulation using artificial intelligence in developing the design of the electric car, specially the electric motor and battery size, and thus achieving one of the most important goals of the United Nations of preserving the environment and reducing carbon emissions.

Research on Probabilistic Load Modeling and Simulation Model of Electric Vehicle Charging Station

Abstract The stochastic nature of electric vehicle charging station loads poses certain challenges in establishing relevant load models. This paper introduces a probabilistic load modeling approach that considers factors such as the time electric vehicles begin charging, daily travel distance, charging power, and charging duration. By comprehensively studying the influencing factors of charging station load modeling and combining Monte Carlo simulation with probabilistic statistical analysis, a comprehensive probability load model for typical charging stations is developed. Building upon this, a charging load simulation model is constructed using Simulink. This model can generate daily load curves consistent with the probability load model, making a positive contribution to the study of the impact of electric vehicles on the power grid.

Design of Two-Wheeler Hybrid Electric Vehicle using MATLAB-Simulink

Abstract: With the rising gap between the supply and demand of oil and the market monopoly of the countries with oil reserves, the world has turned its back on traditional fuels. Not to mention the everyday rising pollution. The work and advancement in electric vehicles are reaching their peak every day and new inventions are being made in this sector, just like the mega-market players and industries. A lot of research is concentrated on the four-wheelers nevertheless, two- wheelers are a major part of transportation in the world and as compared to the four-wheelers two-wheelers don’t find the same spotlight. Our project highlighted this problem and created the simulation model of a two-wheeler hybrid electric vehicle which is a series-parallel hybrid. This simulation is constructed using MATLAB-Simulink software. This is a simulation model of a two- wheeler hybrid electric vehicle which uses a DC source (Battery) as a power source accompanied by the IC Engine. This system can shift load between the battery and IC engine so that power output can be constant, and the system can run smoothly. A DCDC bidirectional chopper is used to convert power between battery and generator which increases the efficiency of the system. In a proposed system a two-wheeler is taken into consideration with an IC engine of rpm of 2000 (for reference) is taken into consideration for simulation.

Physical Modeling and Simulation of Reusable Rockets for GNC Verification and Validation

Reusable rockets must rely on well-designed Guidance, Navigation and Control (GNC) algorithms. Because they are tested and verified in closed-loop, high-fidelity simulators, emphasizing the strategy to achieve such advanced models is of paramount importance. A wide spectrum of complex dynamic behaviors and their cross-couplings must be captured to achieve sufficiently representative simulations, hence a better assessment of the GNC performance and robustness. This paper focuses on of the main aspects related to the physical (acausal) modeling of reusable rockets, and the integration of these models into a suitable simulation framework oriented towards GNC Validation and Verification (V&V). Firstly, the modeling challenges and the need for physical multibody models are explained. Then, the Vertical Landing Vehicles Library (VLVLib), a Modelica-based library for the physical modeling and simulation of reusable rocket dynamics, is introduced. The VLVLib is built on specific principles that enable quick adaptations to vehicle changes and the introduction of new features during the design process, thereby enhancing project efficiency and reducing costs. Throughout the paper, we explain how these features allow for the rapid development of complex vehicle simulation models by adjusting the selected dynamic effects or changing their fidelity levels. Since the GNC algorithms are normally tested in Simulink®, we show how simulation models with a desired fidelity level can be developed, embedded and simulated within the Simulink® environment. Secondly, this work details the modeling aspects of four relevant vehicle dynamics: propellant sloshing, Thrust Vector Control (TVC), landing legs deployment and touchdown. The CALLISTO reusable rocket is taken as study case: representative simulation results are shown and analyzed to highlight the impact of the higher-fidelity models in comparison with a rigid-body model assumption.

How Do Humanlike Behaviors of Connected Autonomous Vehicles Affect Traffic Conditions in Mixed Traffic?

Different methodologies are being used to study the effects of autonomous vehicles (AVs) in mixed traffic to exhibit the interactions between autonomous and human-driven vehicles (HVs). Microscopic simulation tools are popular in such an assessment, as they offer the possibility to experiment in economical, robust, and optimistic ways. A lack of reliable real-world data (also known as natural data) to calibrate and evaluate the connected autonomous vehicle (CAV) simulation model is a major challenge. To deal with this situation, one interesting methodology could be to deal with the CAVs as conventional human-driven vehicles and predict their possible characteristics based on the simulation inputs. The conventional human-driven vehicles from the real world, in this methodology, come to act as a benchmark to offer the measure of effectiveness (MoE) for the calibration and validation. For the three most common driving behaviors, a sensitivity analysis of the behaviors of AVs and an effective assessment of CAVs in a mixed traffic environment were performed to explore the humanlike behaviors of the autonomous technology. The findings show that, up to a certain point, which is directly related to the quantity of interacting vehicles, the impact of CAVs is typically favorable. This study validates the approach and supports past studies by showing that CAVs perform better than AVs in terms of their traffic performance and safety aspects. On top of that, the sensitivity analysis shows that enhancements in the technology are required to obtain the maximum advantages.

Effects of Fuel Cell Size and Dynamic Limitations on the Durability and Efficiency of Fuel Cell Hybrid Electric Vehicles under Driving Conditions

In order to enhance the durability of fuel cell systems in fuel cell hybrid electric vehicles (FCHEVs), researchers have been dedicated to studying the degradation monitoring models of fuel cells under driving conditions. To predict the actual degradation factors and lifespan of fuel cell systems, a semi-empirical and semi-physical degradation model suitable for automotive was proposed and developed. This degradation model is based on reference degradation rates obtained from experiments under known conditions, which are then adjusted using coefficients based on the electrochemical model. By integrating the degradation model into the vehicle simulation model of FCHEVs, the impact of different fuel cell sizes and dynamic limitations on the efficiency and durability of FCHEVs was analyzed. The results indicate that increasing the fuel cell stack power improves durability while reducing hydrogen consumption, but this effect plateaus after a certain point. Increasing the dynamic limitations of the fuel cell leads to higher hydrogen consumption but also improves durability. When considering only the rated power of the fuel cell, a comparison between 160 kW and 100 kW resulted in a 6% reduction in hydrogen consumption and a 10% increase in durability. However, when considering dynamic limitation factors, comparing the maximum and minimum limitations of a 160 kW fuel cell, hydrogen consumption increased by 10%, while durability increased by 83%.

Power Management Strategy of a PEM Fuel Cell-Battery Powered Electric Vehicle

A MATLAB-Simulink based mathematical vehicle simulation model is developed in this investigation to analyze power management strategy of a proton exchange membrane (PEM) fuel cell and battery powered electric vehicle. The vehicle simulation model incorporated a real-world commercial passenger vehicle operator’s drive cycle inputs and associated vehicle dynamics to determine accurate estimation of total onboard power requirements and withdrawal of power from the fuel cell and battery sources to meet the demand. The power management strategy is designed to meet the vehicle’s onboard power demand based on the availability of hybrid combination of fuel cell and battery power sources. The fuel cell stack is the primary power source of the vehicle. The fuel cell is also charging the battery with excess power produced onboard and hence controls the state-of-charge of the battery. Battery is used as a supplemental power source for meeting the vehicle’s peak power demands. The parameters are optimized to implement the power management strategy by considering the battery state-of-the charge, the drive torque and the vehicle drive performance. The model simulation results with optimized parameters showed that the power requirement of the electric vehicle was significantly affected by the combination of fuel cell and battery power management system as well as the operating behaviors of the end user (driver).

ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network

The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems.

Stability Control Research of Tire Blowout Vehicle Simulation Model in Collaboration with Carsim

Stability Control Research of Tire Blowout Vehicle Simulation Model in Collaboration with Carsim

Matematičko modelovanje i simulacija sistema za oslanjanje vozila u poprečnoj ravni

Introduction/purpose: The study of vehicle suspension is a challenge for researchers in the field of vehicles regarding the impact of the suspension system on vehicle performances such as ride comfort, road holding, and working space. This paper presents the simulation of the Land Rover Defender 110 vehicle in the roll plane (half vehicle) in Simulink/MATLAB. The obtained results were compared with the results obtained in the ADAMS/CAR software package of the Land Rover Defender 110 simulation model previously experimentally validated. The Defender 110 vehicle has a dependent suspension system in both axles and a passive suspension type with four degrees of freedom (4 DOF). Methods: The equations of the system can be solved mathematically with a scheme in Simulink/MATLAB while half-vehicle modeling has been done in ADAMS/CAR. Results: The comparison of the vehicle characteristics obtained by the two simulation methods was done for three different scenarios, and it was noticed that there is a good correlation between them. Conclusion: It was concluded that the Defender 110 vehicle simulation model in Simulink/MATLAB is validated. The validated model can be used to perform suspension system optimization in future work.

Energy-efficient torque distribution strategy for four wheel drive electric vehicles based on Traffic zone

This paper presents a four-wheel electric vehicle control system. It incorporates a two-level control strategy for stability and optimal torque distribution to improve fuel efficiency and extend the vehicle’s operational range. The control architecture is structured hierarchically, with a higher level responsible for determining yaw moment and traction forces based on the desired vehicle dynamics. At the lower level, a multi-criteria constrained optimization method is employed, considering both vehicle stability and energy consumption. Additionally, this study takes into consideration the vehicle’s operational context, such as the specific Traffic zone, and the adhesion properties of the tires. This ensures a delicate balance between instantaneous power consumption and overall vehicle stability. The control system’s performance is tested and assessed through three distinct scenarios within the Matlab/Simulink environment. The co-simulation involves the nonlinear vehicle model simulation software, Carsim, and the results are comprehensively compared to the literature.

A Novel Approach for a Predictive Online ECMS Applied in Electrified Vehicles Using Real Driving Data

To increase the efficiency of electrified vehicles, many energy management strategies (driving strategies) have been proposed. These include both offline optimization techniques to identify a system’s theoretical optimum and online optimization techniques created for onboard use in the vehicle. In the field of online optimization, predictive approaches can achieve additional savings. However, predictions are challenging, and robust usability in all driving situations of the vehicle is not guaranteed. In this study, a new approach for a predictive energy management strategy is presented. It is demonstrated how this so-called predictive Online Equivalent Consumption Minimization Strategy (ECMS) can achieve additional fuel savings compared to a non-predictive Online ECMS by predicting recuperation events using map data. As long as the route is known, map data are available, and the current position of the global navigation satellite system (GNSS) is given, the predictive Online ECMS can be applied. If these requirements are not met, the non-predictive basic implementation can still be used to ensure robust functionality. The methodology is investigated using a backward simulation model of a D-segment vehicle powered by a 48 V hybrid electric system in a P2 topology. A dataset including real driving cycles including map data from Open Street Map (OSM) is used. However, the investigations are limited to the consideration of traffic signal (TS) positions on the upcoming route. Simulation results focus on the interaction between the energy management strategy (EMS) and usable battery energy. More than 1 % average saving potentials compared to a non-predictive implementation are shown. The highest saving potentials are found with a usable battery energy of 100 Wh.

Non linear model predictive control strategy for the energy management of a P4 parallel hybrid electric vehicle

In this paper, the energy management strategies (EMS) as main fuel saving approaches are studied for a P4 parallel hybrid electric vehicle (HEV). The multiple power sources of the analysed HEV (one thermal engine and two electric motors) and the different vehicle driving conditions increase the complexity in designing an optimal EMS. To efficiently solve the fuel minimization problem, a non linear Model Predictive Control (NL-MPC) is proposed as energy optimization strategy of the examined HEV. First, a vehicle simulation model is developed in Matlab/Simulink environment. A NL-MPC-controller is designed, implemented into the adopted code and coupled to the vehicle model. The effectiveness of developed NL-MPC approach is evaluated in two different driving cycles, also including various initial battery State of Charge. A comparison with a well-recognized real-time EMS strategy, namely heuristic/rule based (RB) approach, is performed over WLTC and a Real Driving Cycle (RDC). The numerical outcomes demonstrate the capability of NL-MPC controller at significantly improving the fuel consumption with respect to the RB strategy (maximum advantage of 9% and 15% over WLTC and RDC), thus providing an excellent and robust method in the HEV powertrain control with satisfactory performance.

An electric road system or big batteries: Implications for UK road freight

An Electric Road System (ERS)—comprising a network of overhead cables to charge Heavy Goods Vehicles (HGVs) via a pantograph pick-up—is a cost-competitive solution to rapidly decarbonise the UK road freight sector. A major benefit over conventional battery electric HGVs is the reduction in battery capacities needed to fulfil logistics needs. In this study, we develop a detailed vehicle simulation model and use it to calculate the battery capacity requirements of real UK logistics journeys against a range of ERS network sizes and on-route static charging options. The results show that, averaged over all static charging scenarios, ERS reduces battery sizes by 41 %, 62 %, and 75 % for the ‘Light’ (2,750 km), ‘Medium’ (5,500 km) and ‘Heavy’ (8,500 km) ERS scenarios. Of the static charging scenarios, drop-off charging is shown to be more effective than rest stop charging at reducing battery sizes.

General modeling and energy management optimization for the fuel cell electric tractor with mechanical shunt type

The fuel cell electric tractor has emerged as a new development direction in modern agriculture due to its energy-controllable and emission-reducing advantages. This paper focuses on improving the universality of basic research by studying the fuel cell electric tractor with mechanical shunt type (FCETMST). The bond graph model has the benefits of cross-physical system integration. To fill the gap in general modeling of electric tractors, an electromechanical hydraulic integrated machine model by utilizing the bond graph model, which includes energy system, drive system, lifting system and power take-off (PTO) system, is developed. The general power flow model of the electric tractor with mechanical shunt type is also derived. Lastly, the accuracy of the model is validated by analyzing the tractor’s traction characteristics. To further enhance energy efficiency, this paper proposes an energy management strategy (EMS) utilizing a multi-population genetic algorithm (MPGA) and compares it with a power following control strategy (PFCS) for a fuel cell/lithium battery composite energy system. The results base on the co-simulation by using AVL-Cruise and Matlab/Simulink to build a vehicle simulation model and EMS models, respectively, show that under ploughing and transportation conditions, compared with the traditional PFCS, using MPGA leads to reduce state of charge (SOC) fluctuations and the remaining SOC is 11.22 % and 6.1 % higher, respectively. The theoretical hydrogen fuel consumption is decreased by about 26.49 % and 36.21 %, respectively. This study can provide theoretical support for the modeling and energy management optimization of the electric tractor system.

Design of ABS sliding mode control system for in-wheel motor vehicle based on road recognition

Aiming at the problem that the current ABS control algorithm cann’t make full use of the ground braking force to complete the braking when the complex road surface is in emergency braking, the ABS sliding mode variable structure control method based on road surface identification is proposed. Combined with the in-wheel motor of in-wheel motor electric vehicle, a coordinated control method of motor hydraulic composite is designed. Based on the fuzzy logic control method, the road adhesion coefficient is estimated to realize the identification of typical roads and dynamically obtain the optimal slip rate of different roads. The ABS sliding mode variable structure controller is designed with the optimal slip ratio and the actual slip ratio as input, and the saturation function is used to replace the sign function in the traditional sliding mode variable structure control to weaken the ’ chattering ’ phenomenon in the sliding mode variable structure control, and then the ABS controller is designed. Taking the experimental prototype vehicle driven by four-wheel hub motor as the research object, an eight-degree-of-freedom dynamic simulation model of the whole vehicle is established. Compared with the traditional PID controller, the braking time is shortened by 0.2 s and the braking distance is shortened by 2.3 m, which shows the feasibility of the designed controller. Through the simulation braking experiment of the docking road, the adaptability and real-time performance of the ABS sliding mode controller are verified, and the importance of the road adhesion coefficient identification to the ABS controller is verified.

Real Driving Cycle Simulation of a Hybrid Bus by Means of a Co-Simulation Tool for the Prediction of Performance and Emissions

This work is focused on the simulation of a complete hybrid bus vehicle model performing a real-world driving cycle. The simulation framework consists of a coupled co-simulation environment, where all the vehicle sub-system models are linked to achieve a real time exchange of input and output signals. In the vehicle model also the electric devices of the powertrain and accumulation system are included. This co-simulation platform is applied to investigate the hybridization of a 12-m city bus, performing a typical urban driving mission. A comparison between the conventional powertrain is performed against the hybridized version, to highlight the advantages and challenges. In particular, the novelty of this modeling approach is that the IC engine simulation does not rely on pre-processed look-up tables, but exploits a high-fidelity one-dimensional thermo-fluid dynamic model. However, it was necessary to develop a fast simulation methodology to exploit this predictive tool, achieving a low computational cost. The 1D engine model is first validated against the experimental engine map data available, showing a good model predictivity. Then the 1D engine model and the other models of the powertrain are coupled to the vehicle model, in order to follow the prescribed velocity profile of the driving cycle. The complete model is applied under different conditions, to evaluate the impact on performance and emissions and assess the simulation predictivity.

Simulation analysis of dynamical performance and economic performance for the electric vehicle

Aimming at the problem that it is difficult to evaluate whether the dynamical performance and economic performance meet the expected indicators after selecting powertrain for the special vehicle, a vehicle simulation model was built based on the vehicle dynamics theory for the simulation analysis of dynamical performanc and economic performance. The maximum speed, acceleration time, maximum climbing slope and the biggest continue voyage course were obtained by simulating. The simulation results show that its dynamical performance and economic performance meet the expected targets, and the vehicle simulation model has high accuracy and short modeling cycle, which can provide reference for the development of the electric vehicle.