Calculation of demand of electric power of small electric vehicle using Matlab GUI

Cubon, Peter; Šedo, Jozef; Radvan, Roman; Stanček, Ján; Špánik, Pavol; Uríček, Juraj

doi:10.1109/elektro.2014.6847891

Cited by 18 publications

(3 citation statements)

References 2 publications

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“…What should be noted and mulled over is the decision of fitting parts that coordinate the measure of the vehicle [1,20] . For model, if the maker chooses to apply this framework on a vehicle delegated little in weight and measurement, at that point the engines, alternator(s) and the batteries must be in relative extent and sizes [1,4,14] .…”

Section: Methodsmentioning

confidence: 99%

Alternator Based Self Charging Electric Car

Sridhar¹

2023

cienc.eng.

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This paper presents you about designing an electric car with in-built charging system for charging the batteries by the energy obtained from the front axle during running. This system can acts as multi-source charging system which the battery is charged by the in-built alternator, the solar panel and the plug-in AC voltage. Thus, the battery of an Electric vehicle can be charged on both the rest and running conditions. By this method, the charges can be reclaimed from alternators that were attached to the axle of the car. This paper also involves in design of Hybrid Electric Vehicle Intelligent Control System (HEVICS) for controlling the entire operation of the vehicle including the BLDC pulse control, battery management system (BMS), charge controller and Booster circuit. By using the available software's like Proteus 8, PSIM, and Matlab the vehicle can be simulated and be verified. By using this technique, the battery can be charged to about 16.67% of the spent energy can be reclaimed and can be utilized further.

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Section: Methodsmentioning

confidence: 99%

Alternator Based Self Charging Electric Car

Sridhar¹

2023

cienc.eng.

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“…Implementation of dependency equation 8 was added to ASM as Figure 5 [24], [25]. The task of ASM is to develop a matrix of potential solutions in accordance with the following equation:…”

Section: A Elements Of the Pastmentioning

confidence: 99%

The V2G Process With the Predictive Model

Mroczek

Kolodynska

2020

IEEE Access

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The paper proposes using a predictive model to optimize the use of electricity in the V2G (vehicle to grid) service. The novelty of the mechanism as a kind of model predictive control (MPC) is that it seeks an effective way of managing electric energy in an Electric Vehicle (EV). Additionally, it proposes a new method of predicting the electricity consumption which allows the battery of an electric vehicle to reconcile two sides: both the system's and the user's demand will be met at the same time. The model allows for very precise determination of the vehicle's demand for the energy related to the progressive movement, taking into account the parameters characteristic of a given vehicle model, its suspension structure and aerodynamics. In addition, the machine learning algorithm was proposed for the prediction model as a hybrid (offline and online) of supervised learning. As the first part of the research, by using Matlab/Simulink/dSpace software, a prediction of EV energy consumption was created on a selected route at different times of the day (offline data matrix). At the same time, the simulated route was travelled by a BMW i3 EV (online data matrix). Based on the developed machine learning algorithm the results of the electric energy consumption were compared. The research confirms that if the correct mechanism for prediction of energy consumption by the EV is used, it is possible to define the amount of energy needed for a V2G service. The measurement error was obtained at 0.5%. The added value is setting up the EV energy security of customers after the V2G service and a correct WIN-WIN relation between the Low Voltage grid and EV customers' needs.

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“…There are good references of designing GUIs for other applications such as investigation of EV power demand from the grid [3], control of a lifting mechanism [4], Simulation of Modelica Models [5] and a GUI-based simulator for sensor networks [6]. However, there is no similar tool for battery identification and simulation in the literature.…”

Section: Introductionmentioning

confidence: 99%

A MATLAB graphical user interface for battery design and simulation; from cell test data to real-world automotive simulation

Fotouhi

Shateri

Auger

et al. 2016

2016 13th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (

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This paper describes a graphical user interface (GUI) tool designed to support cell design and development of manufacturing processes for an automotive battery application. The GUI is built using the MATLAB environment and is able to load and analyze raw test data as its input. After data processing, a cell model is fitted to the experimental data using system identification techniques. The cell model's parameters (such as open-circuit-voltage and ohmic resistance) are displayed to the user as functions of state of charge, providing a visual understanding of the cell's characteristics. The GUI is also able to simulate the performance of a full battery pack consisting of a specified number of single cells using standard driving cycles and a generic electric vehicle model. After a simulation, the battery designer is able to see how well the vehicle would be able to follow the driving cycle using the tested cells. Although the GUI is developed for an automotive application, it could be extended to other applications as well. The GUI has been designed to be easily used by non-simulation experts (i.e. battery designers or electrochemists) and it is fully automated, only requiring the user to supply the location of raw test data.

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Calculation of demand of electric power of small electric vehicle using Matlab GUI

Cited by 18 publications

References 2 publications

Alternator Based Self Charging Electric Car

Alternator Based Self Charging Electric Car

The V2G Process With the Predictive Model

A MATLAB graphical user interface for battery design and simulation; from cell test data to real-world automotive simulation

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