Electric Vehicle Simulink Model Download [HOT]
Roseanna Diomede
In designing suitable isolators to reduce unwanted vibration in vehicles, the response from a mathematical model which characterizes the transmissibility ratio of the input and output of the vehicle is required. In this study, a Matlab Simulink model is developed to study the dynamic behaviour performance of passive suspension system for a lightweight electric vehicle. The Simulink model is based on the two degrees of freedom system quarter car model. The model is compared to the theoretical plots of the transmissibility ratios between the amplitudes of the displacements and accelerations of the sprung and unsprung masses to the amplitudes of the ground, against the frequencies at different damping values. It was found that the frequency responses obtained from the theoretical calculations and from the Simulink simulation is comparable to each other. Hence, the model may be extended to a full vehicle model.
Simulation of electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles over driving schedules within a full dynamic hybrid and electric vehicle simulator requires battery models capable of predicting state-of-charge, I-V characteristics, and dynamic behavior of different battery types. A battery model capable of reproducing lithium-ion, nickel-metal hydride, and lead-acid I-V characteristics (with minimal model alterations) is proposed. A battery-testing apparatus was designed to measure the proposed parameters of the battery model for all three battery types and simulate driving schedules with a programmed source and load configuration. A multiple time-constant battery model was used for modeling lithium-ion batteries; verification of time constants in the seconds to minutes and hour ranges has been shown in numerous research articles and a time constant in the millisecond range is verified here with experiments. Lack of significant time constants in the millisecond range is validated through direct testing. A modeled capacity-rate effect within the state-of-charge determination portion of the proposed model is verified experimentally to ensure accurate prediction of battery state of charge after lengthy driving schedules. The battery model was programmed into a Matlab/Simulink environment and used as a power source for plug-in hybrid electric vehicle simulations. Results from simulations of lithium-ion battery packs show that the proposed battery model behaves well with the other subcomponents of the vehicle simulator; accuracy of the model and prediction of battery internal losses depends on the extent of tests performed on the battery used for the simulation. Extraction of model parameters and their dependence on temperature and cycle number is ongoing, as well as validation of the Simulink model with hardware-in-the-loop "driving schedule" cycling of real batteries.
N2 - Simulation of electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles over driving schedules within a full dynamic hybrid and electric vehicle simulator requires battery models capable of predicting state-of-charge, I-V characteristics, and dynamic behavior of different battery types. A battery model capable of reproducing lithium-ion, nickel-metal hydride, and lead-acid I-V characteristics (with minimal model alterations) is proposed. A battery-testing apparatus was designed to measure the proposed parameters of the battery model for all three battery types and simulate driving schedules with a programmed source and load configuration. A multiple time-constant battery model was used for modeling lithium-ion batteries; verification of time constants in the seconds to minutes and hour ranges has been shown in numerous research articles and a time constant in the millisecond range is verified here with experiments. Lack of significant time constants in the millisecond range is validated through direct testing. A modeled capacity-rate effect within the state-of-charge determination portion of the proposed model is verified experimentally to ensure accurate prediction of battery state of charge after lengthy driving schedules. The battery model was programmed into a Matlab/Simulink environment and used as a power source for plug-in hybrid electric vehicle simulations. Results from simulations of lithium-ion battery packs show that the proposed battery model behaves well with the other subcomponents of the vehicle simulator; accuracy of the model and prediction of battery internal losses depends on the extent of tests performed on the battery used for the simulation. Extraction of model parameters and their dependence on temperature and cycle number is ongoing, as well as validation of the Simulink model with hardware-in-the-loop "driving schedule" cycling of real batteries.
AB - Simulation of electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles over driving schedules within a full dynamic hybrid and electric vehicle simulator requires battery models capable of predicting state-of-charge, I-V characteristics, and dynamic behavior of different battery types. A battery model capable of reproducing lithium-ion, nickel-metal hydride, and lead-acid I-V characteristics (with minimal model alterations) is proposed. A battery-testing apparatus was designed to measure the proposed parameters of the battery model for all three battery types and simulate driving schedules with a programmed source and load configuration. A multiple time-constant battery model was used for modeling lithium-ion batteries; verification of time constants in the seconds to minutes and hour ranges has been shown in numerous research articles and a time constant in the millisecond range is verified here with experiments. Lack of significant time constants in the millisecond range is validated through direct testing. A modeled capacity-rate effect within the state-of-charge determination portion of the proposed model is verified experimentally to ensure accurate prediction of battery state of charge after lengthy driving schedules. The battery model was programmed into a Matlab/Simulink environment and used as a power source for plug-in hybrid electric vehicle simulations. Results from simulations of lithium-ion battery packs show that the proposed battery model behaves well with the other subcomponents of the vehicle simulator; accuracy of the model and prediction of battery internal losses depends on the extent of tests performed on the battery used for the simulation. Extraction of model parameters and their dependence on temperature and cycle number is ongoing, as well as validation of the Simulink model with hardware-in-the-loop "driving schedule" cycling of real batteries.
ASM is a tool suite for simulating combustion engines, vehicle dynamics, electric components, and the traffic environment. The open Simulink models are used for model-based function development and in ECU tests on a hardware-in-the-loop (HIL) simulator.
Detailed driving simulation coupled with detailed traffic simulation realized by co-simulations with dSPACE ASM for detailed vehicle models and PTV Vissim for realistic traffic provide surprisingly detailed insights.
Well-coordinated tools for simulation, testing and visualization are indispensable in validating modern driver assistance systems. Developers need a quick, easy way to model the properties of the vehicle under test, as well as road networks, traffic and electronic control units (ECUs), and to visualize driving maneuvers realistically. Together, the Automotive Simulation Models (ASMs), ModelDesk and MotionDesk from dSPACE form a perfectly coordinated tool chain.
No modern vehicle ever reaches the market without first undergoing exhaustive tests. And test vehicles alone are no longer enough to test the complex electronic control units. This job is performed by simulation models, which shift ECU development into the virtual reality of a virtual vehicle. Dr. Hagen Haupt, head of dSPACE's Modeling Group, explains how the dSPACE simulation models are meeting this challenge.
This paper presents a method for designing and tuning suspensions purposefully and quickly with the help of vehicle dynamics simulation. The method is based on the Automotive Simulation Models (ASMs) from dSPACE, which have been extended for this use case. The ASMs support design engineers through all phases, from creating a virtual prototype up to close-to-production fine tuning during the test phase. This paper describes the necessary properties of the vehicle dynamics model that go beyond the functional scope of common handling models. At Daimler AG, the ASMs accompany the development during test drives, both for the pure vehicle dynamics design of the vehicle and for coupling the vehicle dynamics control systems to hardware-in-the-loop (HIL) systems.
ASM: Simulation Tool SuiteThe ASMs are a tool suite which consists of simulation models for automotive applications that can be combined as needed. The models support a wide spectrum of simulations, starting with individual components like combustion engines or electric motors, to vehicle dynamics systems, up to complex virtual traffic scenarios. The models can be handled easily and intuitively with ModelDesk, the graphical user interface.
The implementation of each model is open and traceable right down to the Simulink basic block level, so it is easy to supplement or replace components with customer-specific models. This means that the properties of each model can be optimally adapted to individual projects. The standardized interfaces of the ASMs make it easy to extend models and even create entire virtual vehicles. Road networks and traffic maneuvers can be easily and intuitively created using graphical parameterization tools with preview and clear visualization.
Vehicle electrical systems, electric drives and inverters, as well as starter batteries and high-voltage batteries, are all virtualized precisely by the simulation model for electric components. The model supports tasks such as developing and testing hybrid ECUs, battery management systems and indicator light controls. Users can parameterize the modeled components graphically to fit the real controlled system exactly.
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