Design of Fuel Cell Electric Vehicle (FCEV) with Battery Model & Coolin...

Design of Fuel Cell Electric Vehicle (FCEV) with Battery Model & Cooling System _ Matlab Simulink

Source: Steve Miller (2021). Fuel Cell Vehicle Model in Simscape (https://github.com/mathworks/Fuel-Cell-Vehicle-Model-Simscape/releases/tag/21.1.1.2), GitHub. This example shows a Fuel Cell Powertrain modeled in Simscape. A single fuel cell stack in parallel with a battery powers a single motor that propels the vehicle. The fuel cell is modeled using a custom domain to track the different species of gas that are used in the fuel cell. The vehicle can be tested on custom drive cycles or using the Drive Cycle Source from Powertrain Blockset. The fuel cell and battery are connected on a DC electrical network to the motor. The control system determines how much power to draw from the battery and fuel cell. During braking events, power is fed back into the battery to recharge it. A thermal system modeled using a fluid network controls the temperature of the battery, DC-DC converters, and the motor. The flow of oxygen, hydrogen, nitrogen, and water is captured in a custom Simscape domain. Equations for reactions and heat generated are implemented in Simscape language. A thermal management system keeps the fuel cell at the optimal operating temperature. Plot shows how the current from the battery and fuel cell vary as the car is driven along a standard drive cycle. Note that the battery is recharged (current changes sign) while the fuel cell is only discharged. Click here to get the file: https://in.mathworks.com/matlabcentral/fileexchange/33309-fuel-cell-vehicle-fcv-power-train Kindly Subscribe My YouTube Channel... Please like, share and comments on My Videos 🙏 Please click the below links to Subscribe/Join & View my Videos https: //www.youtube.com/c/DrMSivakumar Telegram : t.me/Dr_MSivakumar website : drmsivakumar78.blogspot.com

 

Design of Solar PV DC Power System with Battery Backup Using Maximum Pow...

Stand-alone PV system in this example comprises seven operating modes. These modes are selected based on DC bus voltage, solar irradiance and state of charge of the battery. DC bus voltage level, solar irradiance and the battery state of charge are used to decide the suitable operating mode. Mode-0 - Start mode (Default simulation starting mode) Mode-1 - PV in output voltage control, battery fully charged and isolated Mode-2 - PV in maximum power point, battery is charging Mode-3 - PV in maximum power point, battery is discharging Mode-4 - Night mode, PV shutdown, battery is discharging Mode-5 - Total system shutdown Mode-6 - PV in maximum power point, battery is charging, load is disconnected A MATLAB® live script to design the overall standalone PV system. Simulink® to design/simulate the control logic for the system. Simscape™ to simulate the power circuit. Stateflow™ to implement the supervisory control logic. To track the maximum power point (MPP) of solar PV, We can choose between two maximum power point tracking (MPPT) techniques: INCREMENTAL CONDUCTANCE (IC) PERTURBATION AND OBSERVATION( P&O) Click here to download the simulink Model: https://drive.google.com/file/d/1vp0e... Kindly Subscribe My YouTube Channel... Please like, share and comments on My Videos 🙏 Please click the below links to Subscribe/Join & View my Videos https: //www.youtube.com/c/DrMSivakumar Telegram : t.me/Dr_MSivakumar website : drmsivakumar78.blogspot.com

Design & Simulation of Solar PV System with MPPT Using Boost Converter

This example shows the Design of a Boost Converter for controlling the power output of a solar PV system and helps you to: 1) Determine how the panels should be arranged in terms of the number of series-connected strings and the number of panels per string to achieve the required power rating.

2) Implement the MPPT algorithm using boost converter & Operate the solar PVsystem in the voltage control mode.

3) Select a suitable proportional gain, and phase-lead time constant  for the PI controller

We can choose between two maximum power point tracking (MPPT) techniques: INCREMENTAL CONDUCTANCE (IC)
PERTURBATION AND OBSERVATION( P&O)

Two MPPT techniques are implemented using the variant subsystem.

Set the variant variable MPPT to 0 to choose the perturbation and observation MPPT method.

Set the variable MPPT to 1 to choose the incremental conductance method. Click here to download the File: https://drive.google.com/file/d/18jxlcpsSJbIeURdfBnsRe0pL_KsBrFKi/view?usp=sharing

Modeling and Testing an NR RF Receiver with LTE Interference Using 5G & ...

The example shows how to characterize the impact of radio frequency (RF) impairments in the RF reception of a new radio (NR) waveform when coexisting with a long-term evolution (LTE) interference. The baseband waveforms are generated using 5G Toolbox™ and LTE Toolbox™, and the RF receiver is modeled using RF Blockset™.

This example demonstrates how to model and test the reception of an NR waveform when coexisting with an LTE waveform.

The RF receiver consists of bandpass filters, amplifiers and an demodulator. To evaluate the impact of the LTE interference, the example modifies the gain of the LTE waveform and performs ACLR and EVM measurements. Click here to download the Simulink File:

https://drive.google.com/file/d/1mHcRLXXKVChdHNk-p2NRoQHOxWWtKf85/view?usp=sharing

Modeling and Testing an NR RF Transmitter Using Matlab 5G Toolbox

Modeling and Testing an NR RF Transmitter Using Matlab 5G Toolbox

This example shows how to characterize the impact of RF impairments such as IQ imbalance, phase noise, and PA nonlinearities in the performance of an NR RF transmitter. To evaluate the performance, the example considers these measurements:

• Error vector magnitude (EVM): vector difference at a given time between the ideal (transmitted) signal and the measured (received) signal.

• Adjacent channel leakage ratio (ACLR): measure of the amount of power leaking into adjacent channels and is defined as the ratio of the filtered mean power centered on the assigned channel frequency to the filtered mean power centered on an adjacent channel frequency.

• Occupied bandwidth: bandwidth that contains 99% of the total integrated power of the signal, centered on the assigned channel frequency.

• Channel power: filtered mean power centered on the assigned channel frequency.

• Complementary cumulative distribution function (CCDF): probability of a signal's instantaneous power to be a level specified above its average power.

The model works on a subframe by subframe basis. For each subframe, the workflow consists of these steps:

1. Generate the baseband waveform using 5G Toolbox functions.

2. Upconvert the generated waveform to the passband frequency and apply RF filtering and amplification using RF Blockset.

3. Downconvert the transmitted waveform to baseband frequency.

4. Calculate the ACLR/ACPR, occupied bandwidth, channel power, and CCDF using the Spectrum Analyzer block.

5. Demodulate the waveform at the receiver to measure EVM                                                                                                                                                This example demonstrates how to model and test an NR RF transmitter in Simulink. The RF transmitter consists of an IQ modulator, a bandpass filter and amplifiers. To evaluate the performance, the Simulink model considers ACLR and EVM measurements. The example highlights the effect of HPA nonlinearities on the performance of the RF Transmitter.                                                                                                                                                                                                                                                                                                                                We can explore the impact of altering other impairments as well. For example:

• Increase I/Q imbalance by using the I/Q gain mismatch (dB) and I/Q phase mismatch (Deg) parameters on the IQ Modulator tab of the RF Transmitter block.
• Increase the phase noise by using Phase noise offset (Hz) and Phase noise level (dBc/Hz) parameters on the IQ Modulator tab of the RF Transmitter block.

Additionally, you can check the occupied bandwidth, the channel power, and the CCDF measurements by using the Spectrum Analyzer block.

If you change the carrier frequency or the values in the Waveform Parameters block, you may need to update the parameters of the RF Transmitter components as these parameters have been selected to work for the default configuration of the example. For instance, a change in the carrier frequency requires revising the bandwidth of the filter.

 If you select a bandwidth wider than 20MHz, you may need to update the Impulse response duration and Phase noise frequency offset (Hz) parameters of the IQ Modulator block. The phase noise offset determines the lower limit of the impulse response duration. 

If the phase noise frequency offset resolution is too high for a given impulse response duration, a warning message appears, specifying the minimum duration suitable for the required resolution. 

Click Here to Download the Simulink File: https://drive.google.com/file/d/13oMe1pd9ovxNYAeVzEyGmnV466Cds77J/view?usp=sharing