Electric Vehicle Simulation Research Articles

Comparative Analysis of Electric Vehicle Simulator for Accurate Battery Pack Internal Signal Generation

Comparative Analysis of Electric Vehicle Simulator for Accurate Battery Pack Internal Signal Generation

Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques

One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics.

Modeling and Simulation of Hybrid Electric Vehicles for Sustainable Transportation: Insights into Fuel Savings and Emissions Reduction

Most motor vehicles have historically utilized Internal Combustion Engines powered by a fossil fuel. Despite the technological advancements in fuel-efficient engines, further improvements are required to reduce the effect of fuel costs and global warming. This study models and simulates Hybrid Electric Vehicles (HEVs) to evaluate their potential for fuel cost savings and emissions reduction compared to traditional vehicles. Using MATLAB® version R2023a Update 5 (9.14.0.2337262) Simulink version 10.7 (R2023a) and the Advanced Vehicle Simulator (ADVISOR) version 2003-00-r0116, the research examines Series and Parallel HEV configurations. The simulation explores various battery configurations, engine capacities, and power unit models to analyze their impact on energy utilization. The data, collected from the simulations, show significant fuel savings and emissions reduction with HEVs. The Parallel HEV configuration consistently saves fuel with fewer battery modules, while the Series HEV configuration performs better but requires more modules to maintain the system’s State of Charge.

Research regarding the simulation of the autonomy for the electric vehicles in the case of the WLTC test cycle

Abstract In current mainstream research, energy consumption and autonomy are just a few of the the major objectives anticipated for electric road vehicles. The production of electric vehicles is continuously expanding, offering to the market a large variety of constructive variants in order to satisfy the requirements for their exploitation. Also, in the current energy context, it is expected from the electric car manufacturers, to deliver high quality models which can be characterized as the actual alternatives to actual existing solutions equipped with an internal combustion engine. Under these circumstances, the continuous research it is a high priority for everyone electric vehicle producers, where simulation on the computer is basically not absent as a go-between step prior to the validation of the final draft proposals. According to these assumptions, in the first part of the article you will find the AMESim simulation platform points, where it describes the model made with it and the simulated test cycle. In the second part of the article, as a consequence of the simulation of the experimental electric vehicle, are highlighted some aspects of the autonomy behaviour in the case of simulation on the World-wide harmonized Light duty Test Cycle (WLTC), for all four speed zones specific to it.

Sizing and simulation of modular electric off-road vehicle using Scilab Xcos

Abstract As a sustainable transportation alternative, particularly in the realm of off-road vehicles, electric vehicles are rapidly gaining popularity. Despite the limitations that records have imposed including short-lived ranges and low speed as opposed to conventional vehicles, their eco-friendly nature and efficiency renders them to be appealing. The ongoing technological leaps in motor and battery technologies have improved the standing of long-distance electric vehicles, optimizing their performance by tailoring motors and batteries to suit specific regions and driving patterns. This study aims to size the motor and battery components of an off-road modular vehicle using Scilab Xcos, open-source software. The data obtained from the driving data accessed by driving the electric vehicle around 1 km of Dhulikhel is used as input for the simulation. The output of the simulation is used to understand the requirements of the motor and battery specifications. The motor was able to provide a maximum torque of 100 Nm and a nominal power of 10kW with peak power of 25 kW, while the battery was able to provide a battery power of 16kWh. The design went through theoretical validation and was compared against the established experimental data of the vehicle. Altogether, results affirm that the customized model aligns with the vehicle’s performance requirements by delivering adequate power, torque, and energy efficiency.

Design and Simulation of a Hybrid Electric Vehicle

Design and Simulation of a Hybrid Electric Vehicle

Design, Simulation, and Prototype of an 18-Wheeler Electric Vehicle with Range Extension using Solar PV and Regenerative Braking

In response to California's initiative to propel the advancement of hybrid and electric vehicles toward achieving zero emissions, this study undertakes the design of a hybrid 18-wheeler aligning with the state's stringent standards. The motivation stems from the imperative need to address the research and development in the domain of hybrid/electric class 8 vehicles, specifically focusing on the pivotal segment of 18-wheelers. The research team developed a realistic theoretical design, verified it using MATLAB Simulink simulation, and ultimately built a prototype. The truck's novel features, namely, 60% electric power, regenerative braking, and solar PV for range extension, are included in the design and partially implemented in the prototype. Two motors, a DC and an AC, are used as the prototype drives where the system control is mostly automatic. Simulation of the electric 18-wheeler design shows that the model works properly. It follows the speed command closely while the acceleration and deceleration behaviors are normal. Testing of the prototype shows that it functions appropriately. The DC motor speed can be regulated over a wide speed range while the AC motor can run at two different speeds as designed. The prototype microcontroller logic is followed, ensuring safe operation of the solar PV system and the battery, and effective control of the motors. Overall, the project succeeded in achieving a harmonious blend of simulation, design, and physical implementation. It can be used as an engineering and public education tool. Further, by exploring cutting-edge technologies such as regenerative braking and solar power for truck range extension, the project contributes to raising vehicle efficiency and finding sustainable transportation solutions, which make the transportation sector more friendly to the environment.

Design and Simulation of the Power Transmission System of extended range electric Vehicle based on MATLAB/Simulink

With the rapid growth of automobile ownership, energy shortage and exhaust pollution have become the two major problems restricting the development of automobiles. Electric vehicles with zero fuel consumption and pollution-free advantages are the future direction of development. But battery technology is not mature at this stage, and the driving distance of electric vehicles is short. Extended range electric vehicle is a hybrid electric vehicle equipped with a range extender based on a pure electric vehicle. When the power is sufficient, the whole vehicle runs with pure electric power. When the power is insufficient, the range extender works to drive the vehicle. As a kind of electric vehicle that can extend driving range, the extended-range electric vehicle has great research significance at present. In this paper, based on MATLAB/Simulink platform, the simulation model of the vehicle power transmission system is built by using the experimental modeling method. According to the model, the dynamic performance simulation of the extended-range electric vehicle is carried out, and the rationality of parameter matching of the vehicle components is verified.

Electric vehicles survey and a multifunctional artificial neural network for predicting energy consumption in all-electric vehicles

This study contains a survey on the architecture of electric vehicles and an artificial neural network application for prediction of energy consumption in all-electric vehicles. In this study, the term “electric vehicles” (EVs) refers to various types of electrified vehicles. The technologies behind these electric vehicles were also discussed. The survey focuses on hybrid electric vehicles (HEVs), pure electric vehicles (PEVs), and plug-in hybrid electric vehicles (PHEVs). The study also presents the design simulation of a typical hybrid electric vehicle. A hybrid electric vehicle was designed using ADVISOR, and it was compared with another car known as the targeted car. The fuel consumption of the designed car was found to be lower than that of the targeted car. The study also introduced a multifunctional artificial neural network model for predicting electrical energy consumption in all-electric vehicles. The proposed model has nine input variables, which are virtual functions calculated from the nine selected parameters using a virtual function formula. The number of input variables was made to be equal to the number of output variables so that the artificial neural network could simulate a unique solution. The proposed model was compared with a multi-output inverse function model of an artificial neural network. The accuracy of the proposed model was 1.23–6.85 times higher than that of the inverse function model for the nine case studies considered in terms of mean square error.

Modelling and Simulation of Hybrid Electric Vehicle Based on MATLAB/ Simulink

Abstract: Due to the more vigorous regulations on carbon gas emissions and fuel economy, Fuel Cell Electric Vehicles (FCEV) are becoming more popular in the automobile industry. This paper presents a neural network based Maximum Power Point Tracking (MPPT) controller for 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC), supplying electric vehicle powertrain through a high voltage-gain DC-DC boost converter. The proposed neural network MPPT controller uses Radial Basis Function Network (RBFN) algorithm for tracking the Maximum Power Point (MPP) of the PEMFC. High switching frequency and high voltage gain DC-DC converters are essential for the propulsion of FCEV. In order to attain high voltage gain, a three-phase high voltage gain Interleaved Boost Converter (IBC) is also designed for FCEV system. The interleaving technique reduces the input current ripple and voltage stress on the power semiconductor devices. The performance analysis of the FCEV system with RBFN based MPPT controller is compared with the Fuzzy Logic Controller (FLC) in MATLAB/Simulink platform.

Simulation of Electric Vehicle with DC Fast Charging for V2G and G2V Operation

Simulation of Electric Vehicle with DC Fast Charging for V2G and G2V Operation

Research of Regenerative Braking Strategy for Electric Vehicles

In the context of global energy instability caused by the transformation of global demand for energy and energy resources, one of the most important areas in the automotive industry is the development of electric vehicles. Serial production of high-tech electric vehicles with a long range contributes to the stabilization of the energy market and the sustainable development of the whole fuel-energy sector. To evaluate the possibility of optimizing the electric vehicles energy consumption, various regenerative braking strategies are discussed in the article based on the Nissan Leaf electric vehicle, which simulation model includes submodules of the traction electric motor, hybrid braking system, traction rechargeable battery and tires. In order to test the adequacy of the simulation model to reproduce the relationship between the operating parameters of electric vehicles various systems and evaluate their ability to regenerate energy during braking the simulation results were compared with the actual experimental data published by the Lab Avt research laboratory (USA). The relative error of the mathematical modeling results of the braking energy regeneration processes is 4.5 %, which indicates the adequacy of the electric vehicle simulation model and the possibility of its using as a base for research and comparison of the energy efficiency of various regenerative braking strategies. As the results of experiments have shown, the usage of the proposed control strategy of the regenerative braking maximum force allows increasing 2.14 times the energy recharging traffic to the battery as compared with the basic control strategy of fixed coefficient braking forces distribution with an increase in braking distance by 10 m. An alternative control strategy of regenerative braking optimal efficiency as compared to the basic control strategy provides a reduction in braking distance by 13.2 % at increasing by 84.4 % the amount of energy generated by the electric motor for recharging the batteries. The carried out investigations confirm the available significant potential for improving the efficiency of the electric vehicles usage by developing the control strategy and algorithms of the braking energy regeneration.

Powertrain modeling and performance simulation of a novel flywheel hybrid electric vehicle

To improve vehicle performance and energy utilization, a novel planetary gear set based flywheel hybrid electric powertrain (PGS-FHEP) is proposed. The PGS-FHEP involves an internal combustion engine, a planetary gear set that integrated a control motor and an energy storage flywheel, which combines the high efficiency of the mechanical flywheel energy storage system with the flexible and controllable characteristics of the electric motor. The powertrain is analyzed and modeled using lever analogy method, and a rule-based control strategy is designed and verified under different test cycles. The simulation results indicate that compared with the traditional manual transmission vehicle, the fuel economy of the vehicle equipped with PGS-FHEP can be improved by more than 50%, and the acceleration performance can be increased by 28.01%. Up to 60.61% of vehicle kinetic energy can be recovered by PGS-FHEP, among which 37.85% can be directly captured by the energy storage flywheel. In addition, the battery charging power is reduced, which is beneficial to prolong the battery life.

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.

A multivariable output neural network approach for simulation of plug-in hybrid electric vehicle fuel consumption

This study is laser focused on the simulation of fuel consumption and fuel economy label parameters of plug-in hybrid electric vehicles. While fuel economy is a key factor in the design of plug-in hybrid electric vehicles, a fuel economy label can educate customers about the economic advantage of purchasing a particular car. The fuel economy label of a PHEV consists of parameters like driving range, electrical energy consumption, fuel economy for city, highway, and combined use, battery recharge time, and fuel consumption rates. The study used an inverse function model of an artificial neural network to simulate and calculate the parameters of the fuel economy labels of PHEVs. Firstly, the selected parameters of the fuel economy label of plug-in hybrid electric vehicles were used to develop a single output model. The output variable of the single output model was then merged with dummy functions to form input variables for the inverse function model. The output variables simulated were engine size in litres; estimated driving range when the battery is fully charged in km, battery recharged time in hours, city fuel consumption (L/100 ​km), highway fuel consumption (L/100 ​km), combined fuel consumption (L/100 ​km), estimated driving range when the tank is full, carbon dioxide (CO2) emission in grams/km, electric motor power in kW, number of cylinders, and electrical charges consumed in kWh/100 ​km. Different cases of input variables were considered for the inverse function model. The accuracy of the model was 29.1 times greater than that of the conventional inverse artificial neural network model.

Pure Electric Vehicle Power System Matching Design and Simulation

According to the design requirements of pure electric drive modification of a certain A-class car and the vehicle’s fundamental parameters, the longitudinal dynamics theory of the vehicle is used to analyze the driving motor, power battery and fixed speed ratio transmission, and other key components. With the help of Cruise simulation software, a pure electric vehicle powertrain model was established and simulation tests were carried out on the New European Driving Cycle, 60km/h isokinetic range cruise, 100km/h acceleration time, and hill-climbing degree and other operating conditions. The results show that the parameters of the power system of pure electric vehicles are reasonably selected, and the power indicators, driving range, and power consumption per 100 kilometers meet the performance design requirements of the whole vehicle, which have theoretical guiding significance for the development of the whole vehicle.

An Online Condition-Based Parameter Identification Switching Algorithm for Lithium-Ion Batteries in Electric Vehicles

Safely and reliably managing the lithium-ion batteries in electric vehicles for daily operations relies on accurate state-of-charge estimation. To pursue accurate state-of-charge estimation, battery electric circuit model-based algorithms demand minimized modeling error from accurate parameter identifications. Typical online parameter identification algorithms: least square fitting approach and recursive least square fitting are commonly adopted but have their limitations and issues with continuously providing accurate identification results. To overcome their problems and utilize their strengths, this paper proposes a condition-based parameter identification switching algorithm. The proposed algorithm accurately and robustly identifies the lithium-ion batteries' parameters based on the battery data condition and the adoption of Bollinger bands. By minimizing the modeling error, the accuracy of the state-of-charge estimation is thus improved. The electric vehicle simulation results indicate that the proposed algorithm can improve the parameter identification accuracy by an average of 70.80% and 44.95% reduction in the mean absolute parameter identification error, compared to typical least square fitting and recursive least square fitting approaches. With the help of the minimized modeling error, the state-of-charge estimation accuracy can thus be improved, rendering an average of 62.44% and 21.49% improvement.

Estimation of state parameters and road adhesion coefficients for distributed drive electric vehicles based on a strong tracking SCKF

To address the difficulties in accurately measuring driving state parameters and the road adhesion coefficient, in this paper, the permanent magnet synchronous motor (PMSM) was selected as the automotive in-wheel motor, and the distributed drive electric vehicle (DDEV) simulation model was built based on CarSim and MATLAB/Simulink software. PMSM speed estimation by Adaptive Sliding Mode Observer (ASMO) was used as input information for subsequent studies of state parameters and road adhesion coefficient estimation. Next, this paper established a nonlinear three-degrees-of-freedom whole-vehicle dynamics reference model based on the Dugoff tire model. We derived a suboptimal fading factor using strong tracking theory (STT) and introduced it into the square root of the prediction error covariance of the square-root cubature Kalman filter (SCKF) algorithm. A strong tracking square-root cubature Kalman filter (ST-SCKF) observer was constructed. Improved algorithm estimation accuracy and the ability to cope with sudden changes in operating conditions improve the estimation accuracy of the DDEV driving state parameters and road adhesion coefficient. Finally, a joint CarSim and MATLAB/Simulink simulation and experiments on real vehicles were carried out. The results show that, compared to the traditional CKF (cubature Kalman filter, CKF) and SCKF algorithms, the ST-SCKF algorithm is able to estimate vehicle state parameters and road adhesion coefficients more accurately while maintaining good estimation accuracy for docked road conditions with sudden changes in the road adhesion coefficient.

Simulation of Hybrid Electric Vehicle Using Solar and Wind With MATLAB

This paper proposes a Hybrid Electric Vehicle (HEV) system which resolves the major glitches of fuel and pollution. The renewable energy is vibrant for today’s world as in near coming the non-renewable sources that we are using are going to get bushed. The hybrid electric vehicle is a phase in saving these non-renewable sources of energy. The elementary principle of solar vehicle is to usage energy that is kept in a battery during and after charging it from a solar panel. Power generated by renewable energy sources has newly become one of the most hopeful resolutions for the electrification of islands and distant rural areas. But high reliance on weather conditions and the impulsive nature of these renewable energy sources are the main drawbacks. To overcome this feebleness, changed green energy sources and power electronic converters need to be combined with each other. The charged batteries are used to initiative the motor which attends here as an engine and interchanges the vehicle in reverse or forward direction. This idea, in future, may help to shield our fuels from getting quenched.

Validation and Calibration of a Measurement System for Testing Electric Motors

The authors have been dealing with the modelling and simulation of electric vehicles and motors for many years. Knowing their electromagnetic and dynamic characteristics is essential for the simulation of electric motors. These data have to be determined experimentally because the manufacturers usually do not provide them. In the last two years, we have designed and realised a measurement system for testing electric motors at the Faculty of Engineering of the University of Debrecen. In this publication, we present the validation and calibration of the individual components of the above system, the applied methods and procedures, and the obtained results.