How to Build Simple Model of Fuel Cell SOFC Using Matlab Simulink

How to Build Simple Model of Fuel Cell SOFC Using Matlab Simulink

How to Build Simple Model of PEM Fuel Cells Using Matlab Simulink

How to Build Simple Model of PEM Fuel Cells Using Matlab Simulink

Playlist: Electric Vehicle Design Technology_ Simulation & Analysis

Playlist : Fuel Cell Technology Simulation & Analysis Using Matlab

Playlist_ Renewable Energy Systems : Simulation & Analysis Using Applications

Playlist _ Simulation & Analysis of Solar Energy System using Matlab

Design of NASA Robotic MARS Helicopter Using Matlab Simulink

This example shows how to use Simscape™ Electrical™ to model a helicopter with coaxial rotors suitable to fly on Mars.

This helicopter takes inspiration from Ingenuity, the robotic helicopter developed by NASA, which accomplished the first powered flight on another planet.

References:
https://in.mathworks.com/help/physmod/sps/ug/mars-helicopter-system.html?s_eid=psm_ml&source=15308

Withrow, S., Johnson, W., Young, L. A., Cummings, H., Balaram, J., & Tzanetos, T. (2020). “An Advanced Mars Helicopter Design”. ASCEND 2020. doi:10.2514/6.2020-4028 

Pipenberg, B. T., Keennon, M., Tyler, J., Hibbs, B., Langberg, S., Balaram, J. (Bob), Pempejian, J. (2019). “Design and Fabrication of the Mars Helicopter Rotor, Airframe, and Landing Gear Systems”. AIAA Scitech 2019 Forum. doi:10.2514/6.2019-0620

Balaram, B., Canham, T., Duncan, C., Grip, H. F., Johnson, W., Maki, J., Zhu, D. (2018). “Mars Helicopter Technology Demonstrator”. 2018 AIAA Atmospheric Flight Mechanics Conference. doi:10.2514/6.2018-0023

Contents
Introduction – MARS Helicopter Technology
Introduction - Mars Helicopter System-Level Design
Simulink Model – NASA Robotic MARS Helicopter
Model Overview & Battery Pack Variant
Flight Control System & Command Dashboard subsystem
Simulation & Result Analysis:
Altitude and Battery Cell Temperatures
Flight Duration Vs Number of Battery Cells

Introduction – MARS Helicopter Technology

The Mars Helicopter is an autonomous 1.8 kg co-axial, counter-rotating rotorcraft baselined to fly on the Mars 2020 mission to demonstrate aerial mobility on the surface of Mars.

The rotor system consisting of the rotor blades, hub mechanism, propulsion motors, swashplates and linkages, control servos, and primary helicopter structure.

The landing gear system consisting of 4 deployable legs, landing feet, and suspension mechanisms.

Auxiliary structures, including the structural elements of the Helicopter Warm Electronics Box (HWEB) that encloses the electronic core module (ECM) and battery, and the solar array substrate that serves as the structural element of the solar panel system.

The solar array located above the blades is used to recharge the helicopter batteries during the day to provide power for flight operations and overnight thermal survival.

This example shows how to use Simscape™ Electrical™ to model a helicopter with coaxial rotors suitable to fly on Mars.

This helicopter takes inspiration from Ingenuity, the robotic helicopter developed by NASA, which accomplished the first powered flight on another planet.

To control the helicopter altitude interactively, can use the blocks in the Command Dashboard subsystem.

The helicopter model comprises
Solar Panel,
Battery Pack,
Heater,
Motor & Drive,
Two gearboxes
Two contra-rotating coaxial rotors,
1D mechanical model of the gravity, drag, mass, and ground contact forces.

Click here to download the model:
2021a version:
https://drive.google.com/file/d/1M8IaiPS18JpqkPC8Qe4U7P4x1x_j4Q4c/view?usp=sharing

021b version
https://drive.google.com/file/d/1Lox1X363nFNrMaLXKTghBR7X4LhpONfu/view?usp=sharing

Modeling & FFT analysis on PCC- Inverter-based Micro grid with Droop Control Technique Using Matlab

This example shows the islanded operation of an inverter-based microgrid using droop control technique.

With the droop control technique, PLL are not required to achieve system-wide synchronization because all inverters reach the same frequency.

The microgrid consists of three parallel inverters subsystems, with power ratings of 500 kW, 300 kW and 200 kW respectively, connected to the PCC (Point-of-Common-Coupling) bus.A dynamic load model is used to dynamically change the microgrid total load.

The Microgrid Supervisory Control system, when enabled, modifies the inverters P/F and Q/V droop set points in order to bring back the microgrid frequency and voltage at their nominal values (60 Hz and 600 Volts respectively).

The example illustrate the operation of an inverter-based microgrid disconnected from the main grid (islanded mode), using the droop control technique.

Each inverter subsystem contains a three-phase two-level power converter, an LC filter, a 480/600V transformer as well as an ideal DC source to represent the DC link of a typical renewable energy generation system (such as PV array, wind turbine, battery energy storage system).

Each subsystem also includes a control system and a PWM generator feeding the inverter.

The analysis is based on the frequency reference that capable in generating the output of voltage and current as well as the equality of load power sharing when a load disturbance occurs in the parallel-connected inverters.

The FFT Analyzer app allows we to perform Fourier analysis of simulation data and provides access to all the simulation data that are defined as structure-with-time variables in our workspace.

The app displays the spectrum as a bar graph or as a list in percentages relative to a base value or to the DC component of the signal.

To Open the FFT Analyzer App:
MATLAB command prompt:
Enter  powerFFT

Analysis :
At 1 s, the total micro grid load is increased from 450kW/100kvar to 850kW/200kvar. At 3 s, droop control is enabled on all inverters.
We can see that the micro grid load is now shared equally among the three inverters.
At 5 s, the supervisory control is enabled. The frequency is then being slowly increased to 60Hz and the line voltage to 600V.
The droop P/F is set to 1%, meaning that microgrid frequency is allowed to vary from 60.3 Hz (inverter produces no active power) to 59.7 Hz (inverter produces its nominal active power).

The droop Q/V is set to 4%, meaning that the microgrid voltage at the PCC bus is allowed to vary from 612 Vrms (inverter produces its full inductive power) to 588 Vrms (inverter produces its full capacitive power). Note that Qmax is specified as half of the nominal active power Pnom.

To demonstrate the impact of the inverters PWM carrier’s initial phase on the PCC bus voltage harmonic content, first open the FFT Analyzer App to perform an FFT analysis of the PCC phase A bus voltage.

In the App, set the Structure with time parameter to PCC , the Signal parameter to V_PCC, and the Dimension parameter to 1 to analyze the PCC phase A bus voltage. Set the Zoom on parameter to FFT window , the Start time parameter to 7.9 , and the Max frequency parameter to 7000. Click Compute FFT. In the FFT plot, the maximum harmonic occurs around the switching frequency (2700 Hz) and is close to 2%.

Now, double-click on the Inverter 2 (300 kW) subsystem and change the Carrier initial phase parameter to -90 degrees. Rerun the simulation and again, perform an FFT analysis on the PCC phase A voltage. We should see that this new carrier phase setting significantly reduces the harmonic content around the switching frequency (2700 Hz). This is due to the fact that Inverter 1 carrier phase is set to +90, so switching harmonics are then partially canceled.

Click here to download the file:
2020a version:
https://drive.google.com/file/d/15A6V1SPbP9hXL9zLO7j4bvgvJiYNwepl/view?usp=sharing
2021a version:
https://drive.google.com/file/d/1t6WCRfMjqF6ku2x04iCJ0nJmgFv5dQPZ/view?usp=sharing
2021b version:
https://drive.google.com/file/d/10sDfAa9AWhWMGn00mkRRgnnyEARy30hQ/view?usp=sharing

Modeling & Analysis of PEM Fuel Cell System Using Matlab Simulink

This example shows Modeling & Analysis of proton exchange membrane (PEM) fuel cell stack system to set up 1) Electrical Load : A) drive cycles, B) step C)Ramp 2)Power Produced & Consumed By The System 3) Plot the fuel cell I V curve, Efficiency & Utilization and Temperature in Fuel Cell system 4) Hydrogen consumed by the fuel cell.

• This example shows how to model a proton exchange membrane (PEM) fuel cell stack with a custom Simscape block.
• The PEM fuel cell generates electrical power by consuming hydrogen and oxygen and producing water vapor.
• The custom block represents the membrane electrode assembly (MEA) and is connected two separate moist air networks: one for the Anode Gas Flow and one for the Cathode gas flow.
• The two moist air networks represents different gas mixtures.
• The anode network consists of nitrogen (N2), water vapor (H2O), and hydrogen (H2), representing the fuel.
• The hydrogen is stored in the fuel tank at 70 MPa.
• A pressure-reducing valve releases hydrogen to the fuel cell stack at around 0.16 MPa.
• Unconsumed hydrogen is recirculated back to the stack.
• The cathode network consists of nitrogen (N2), water vapor (H2O), and oxygen (O2), representing air from the environment.
• A compressor brings air to the fuel cell stack at a controlled rate to ensure that the fuel cell is not starved of oxygen.
• A back pressure relief valve maintains a pressure of around 0.16 MPa in the stack and vents the exhaust to the environment.
• The temperature and relative humidity in the fuel cell stack must be maintained at an optimal level to ensure efficient operation under various loading conditions.
• Higher temperatures increase thermal efficiency but reduce relative humidity, which causes higher membrane resistance.
• Therefore, in this model, the fuel cell stack temperature is kept at 80 degC.
• The cooling system circulates coolant between the cells to absorb heat and rejects it to the environment via the radiator.
• The humidifers saturate the gas with water vapor to keep the membrane hydrated and minimize electrical resistance.
• This plot shows the current-voltage (I-V) curve of a fuel cell in the stack.
• As the current ramps up, an initial drop in voltage occurs due to electrode activation losses, followed by a gradual decrease in voltage due to Ohmic resistances.
• Near maximum current, a sharp drop in voltage occurs due to gas-transport-related losses.
• This plot also shows the power produced by the cell.
• When the ramp scenario is selected, the power increases until a maximum power output, then decreases due to the high losses near maximum current.
• Click here to download the Simulink files: 2021a version:

https://drive.google.com/file/d/1XTnG4zOll1MR58gy_GU7F0K60uUBPCOh/view?usp=sharing

2020a version: https://drive.google.com/file/d/1VigTbfawAmigpxoK5eAka8gx-GU_UBN6/view?usp=sharing

2019a version: https://drive.google.com/file/d/1eGiWLTkdz0lS-o2odx1D8OCXRcOQuEMM/view?usp=sharing

2018a version: https://drive.google.com/file/d/1GReipArHs_fhwCMPjfkIm1AuS1BNTs6Y/view?usp=sharing Kindly Subscribe My YouTube Channel... Please like, share and worthy 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

Part 1_ Design an Energy System for a Hydrogen-Based Electric vehicle Using Matlab Simulink

This example shows Fuel Cell Electric Vehicle Model with a Motor-Generator, Battery, Direct-Drive Transmission, and Associated Powertrain Control Algorithms.

FCEVs are equipped with other advanced technologies to increase efficiency, such as regenerative braking systems that capture the energy lost during braking and store it in a battery.

This example shows how to create an Fuel Cell electric vehicle reference application project using Matlab.

Run the following command to create and open a working copy of the project files: >>autoblkFCEvStart According to the simulation results including FTP75 and WLTP cycles, it was understood that vehicle speed and cycle speed were the same.

Simulation Result: Displays vehicle-level performance, battery state of charge (SOC), and equivalent fuel economy results that are useful for powertrain matching and component selection analysis. At this point, it is concluded that the energy consumption data obtained from the model is also correct.

Part 2 _ Modeling of an Fuel Cell Electric Vehicle with MATLAB/Simulink

This example shows how to create an Fuel Cell electric vehicle reference application project using Matlab.

Contents

Introduction - Fuel Cell Electric Vehicle

• Fuel cell electric vehicles (FCEVs) are powered by hydrogen.
• They are more efficient than conventional internal combustion engine vehicles and produce no tailpipe emissions, they only emit water vapor and warm air.
• The U.S. Department of Energy leads research efforts to make hydrogen-powered vehicles an affordable, environmentally friendly, and safe transportation option.
• Powertrain Blockset & Simscape Driveline
• Built-in Controller Models
• Powertrain Blockset Blocks for Vehicle design
• Powertrain Design tradeoff studies
• Modeling of an Fuel Cell Electric Vehicle with MATLAB/Simulink
• Sample Output Comparison with Different Drive Cycles

Simulation & Result Analysis : Displays vehicle-level performance, battery state of charge (SOC), and equivalent fuel economy results that are useful for powertrain matching and component selection analysis. FCEVs use a propulsion system similar to that of electric vehicles, where energy stored as hydrogen is converted to electricity by the fuel cell. Unlike conventional internal combustion engine vehicles, these vehicles produce no harmful tailpipe emissions.

FCEVs are fueled with pure hydrogen gas stored in a tank on the vehicle. Similar to conventional internal combustion engine vehicles, they can fuel in less than 4 minutes and have a driving range over 300 miles. Motor torque arbitration and power management: 1) Implements a regenerative braking algorithm for the traction motor to recover the maximum amount of kinetic energy from the vehicle. 2) Implements a power management algorithm that ensures the battery dynamic discharge and charge power limits are not exceeded. 3) The algorithm outputs the dynamic discharge and charge power limits as functions of battery state of charge (SOC). 4) Implements a virtual battery management system. Click here to get the Simulink File: https://drive.google.com/file/d/1ph75ejjGFlWFF4EkC8pV7UWzLyIJpuPZ/view?usp=sharing Click here to get the Whole Project File: https://drive.google.com/file/d/1SuXjtS_-ar61tPWd3RWKSXoZbgqiG5D7/view?usp=sharing

Part 2_ FPGA Implementation of Automatic Dependent Surveillance–Broadcast Using Matlab Simulink

This example shows how to design packet-based airplane tracking application based on Automatic Dependent Surveillance Broadcast (ADS-B) standard, partitioned between FPGA and embedded processor.

Contents Introduction - Automatic Dependent Surveillance–Broadcast Introduction - SoC Blockset Introduction - Packet-Based ADS-B Transceiver ADS-B Transmitter Algorithm ADS-B Receiver Algorithm Matlab Simulink implementaion Simulation & Results Supported Hardware Platforms: Xilinx® Zynq® ZC706 evaluation kit + Analog Devices® FMCOMMS2/3/4 card. ZedBoard™ + Analog Devices FMCOMMS2/3/4 card. Automatic Dependent Surveillance–Broadcast (ADS–B) is a surveillance technology in which an aircraft determines its position via satellite navigation or other sensors and periodically broadcasts it, enabling it to be tracked.

ADS-B, which consists of two different services, "ADS-B Out" and "ADS-B In", could replace radar as the primary surveillance method for controlling aircraft worldwide.

ADS-B provides many benefits to both pilots and air traffic control that improve both the safety and efficiency of flight.

Traffic : When using an ADS-B In system, a pilot is able to view traffic information about surrounding aircraft if those aircraft are equipped with ADS-B out. This information includes altitude, heading, speed, and distance to aircraft.

Weather : Aircraft equipped with universal access transceiver (UAT) ADS-B In technology will be able to receive weather reports, and weather radar through flight information service-broadcast (FIS-B).

Flight information: Flight information service-broadcast (FIS-B) also transmits readable flight information such as temporary flight restrictions (TFRs) and NOTAMs to aircraft equipped with UAT. This example showed how SoC Blockset is used to design packet-based ADS-B standard to meet system requirements.

By simulating the design with memory channel as interface between the FPGA and the Processor, we validated that the system requirements of throughput and drop packets are met at the design time.

We implemented the design on SoC device from the model and verified the results on hardware. Although ADS-B is not a computationally intensive standard, it is useful to demonstrate the design process for packet-based systems intended for implementation on a SoC device.

We can follow the same design procedure for even more computationally intensive requirements for this application or another packet-based application. Click here to download the simulink model https://drive.google.com/file/d/1JHyIIEEn64gE2ljG1UOu9aMgkdE56xra/view?usp=sharing https://drive.google.com/file/d/16ODZH3JI3z7Jfe-jomOSWsKYqoyyICLO/view?usp=sharing 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

Part 1_ Design & FPGA Implementation of Automatic Dependent Surveillance–Broadcast Using Matlab

This example shows how to design packet-based airplane tracking application based on Automatic Dependent Surveillance Broadcast (ADS-B) standard, partitioned between FPGA and embedded processor.

Contents Introduction - Automatic Dependent Surveillance–Broadcast Introduction - SoC Blockset Introduction - Packet-Based ADS-B Transceiver ADS-B Transmitter Algorithm ADS-B Receiver Algorithm Matlab Simulink implementaion Simulation & Results Supported Hardware Platforms: Xilinx® Zynq® ZC706 evaluation kit + Analog Devices® FMCOMMS2/3/4 card. ZedBoard™ + Analog Devices FMCOMMS2/3/4 card. Automatic Dependent Surveillance–Broadcast (ADS–B) is a surveillance technology in which an aircraft determines its position via satellite navigation or other sensors and periodically broadcasts it, enabling it to be tracked.

ADS-B, which consists of two different services, "ADS-B Out" and "ADS-B In", could replace radar as the primary surveillance method for controlling aircraft worldwide.

ADS-B provides many benefits to both pilots and air traffic control that improve both the safety and efficiency of flight.

Traffic : When using an ADS-B In system, a pilot is able to view traffic information about surrounding aircraft if those aircraft are equipped with ADS-B out. This information includes altitude, heading, speed, and distance to aircraft.

Weather : Aircraft equipped with universal access transceiver (UAT) ADS-B In technology will be able to receive weather reports, and weather radar through flight information service-broadcast (FIS-B).

Flight information: Flight information service-broadcast (FIS-B) also transmits readable flight information such as temporary flight restrictions (TFRs) and NOTAMs to aircraft equipped with UAT. This example showed how SoC Blockset is used to design packet-based ADS-B standard to meet system requirements.

By simulating the design with memory channel as interface between the FPGA and the Processor, we validated that the system requirements of throughput and drop packets are met at the design time.

We implemented the design on SoC device from the model and verified the results on hardware. Although ADS-B is not a computationally intensive standard, it is useful to demonstrate the design process for packet-based systems intended for implementation on a SoC device.

We can follow the same design procedure for even more computationally intensive requirements for this application or another packet-based application. Click here to download the simulink model https://drive.google.com/file/d/1JHyIIEEn64gE2ljG1UOu9aMgkdE56xra/view?usp=sharing https://drive.google.com/file/d/16ODZH3JI3z7Jfe-jomOSWsKYqoyyICLO/view?usp=sharing

FPGA HDL Implementation of Contrast Limited Adaptive Histogram Equalization (CLAHE)U sing Simulink

This example shows how to implement a contrast-limited adaptive histogram equalization (CLAHE) algorithm using Simulink® blocks. This is an image contrast enhancement algorithm that overcomes limitations in standard histogram equalization (HE). %CLAHE Algorithm modelname = 'CLAHEExample'; open_system(modelname,'force'); set_param(modelname,'SampleTimeColors','off'); set_param(modelname,'Open','on'); set_param(modelname,'SimulationCommand','Update'); set(allchild(0),'Visible','off'); %Tile Generation system = 'CLAHEExample/CLAHEHDLAlgorithm/tileGeneration'; open_system(system,'force'); %Histogram Equalization Pipeline system = 'CLAHEExample/CLAHEHDLAlgorithm/histoEqPipeline/'; subsystem = [system 'histPipe1']; open_system(subsystem,'force'); %The remaining total excess value is passed to the Redistribute subsystem as excess value. system = 'CLAHEExample/CLAHEHDLAlgorithm/histoEqPipeline/'; subsystem = [system 'histPipe1/redistribute']; open_system(subsystem,'force'); %The bilinear interpolation equation to compute a pixel value in the output image. system = 'CLAHEExample/CLAHEHDLAlgorithm/bilinearInterpolation'; open_system(system,'force'); Simulink Model : Contrast – Limited Adaptive Histogram Equalization  The input image frame is converted to a pixel stream and pixelcontrol bus using a Frame To Pixels block.  The adjusted pixel values are given to the Pixels To Frame block and converted to a frame using the control signals.  The pixel value read from the imgBuffer subsystem is passed to CLAHEHDLAlgorithm for adjustment.  The pixel stream is passed to the CLAHEHDLAlgorithm subsystem for contrast enhancement and is also stored in the imgBuffer subsystem.  While processing, the CLAHEHDLAlgorithm subsystem generates the address to read image data from the imgBuffer subsystem. The Result subsystem shows the input image and output image once all the pixels in the frame have been received by the Pixels To Frame block. Click here to download the simulink file: https://drive.google.com/file/d/1ktc0B7Ov0P9XMGCs-eTIGjWNvfuOK3ZI/view?usp=sharing

FPGA Implementation of Low-Light Enhancement Algorithm_ To Enhance Low-Light Images/Videos Matlab

This example performs LLE by inverting an input image and then applying a de-haze algorithm on the inverted image.

After inverting the low-light image, the pixels representing non-sky region have low intensities in at least one color channel.

The algorithm consists of six stages. Step 1: Scaling & Inversion The input image   is converted to range [0,1] by dividing by 255 and then inverting pixel-wise. Step 2: Dark Channel Estimation The dark channel is estimated by finding the pixel-wise minimum across all three channels of the inverted image. Step 3: Refinement The airlight image from the previous stage is refined by iterative smoothing. This stage consists of five filter iterations with a 3-by-3 kernel for each stage. Step 4: Non-Linear Correction To reduce over-enhancement, the refined image is corrected using a non-linear correction. Step 5: Restoration Restoration is performed pixel-wise across the three channels of the inverted and corrected image. Step 6: Inversion To obtain the final enhanced image, this stage inverts the output of the restoration stage, and scales to the range [0,255].

The figure shows the input image and the enhanced output images obtained from the LLESimplified subsystem and the LLEHDL subsystem. Click here to get the simulink file: https://drive.google.com/file/d/1nBBRRzjqXsKmImOCqdXWTZ8NXM6hmi3m/view?usp=sharing

Implementation of Haze Removal Algorithm to Enhance Low-Light Images Using Matlab

This example shows how to enhance low-light images using an algorithm suitable for FPGAs.

Haze removal or image dehazing is required in real-world weather conditions to obtain a fast and high-quality hazy free image which is used in various fields like satellite systems and aircraft systems.

Some of the wide important areas in which the haze removal methods used are air- crafts, remote sensing, intelligent transportation systems, underwater image processing, Object detection, outdoor surveillance, railway systems, aerial imagery, computational photography/vision applications, video analysis and recognition, image classification, military and defense surveillance system, etc. Example Code: % Import an RGB image captured in low light. A = imread('lowlight_21.jpg'); % Invert the image. AInv = imcomplement(A); % Apply the dehazing algorithm. BInv = imreducehaze(AInv,'ContrastEnhancement','none'); % Invert the results. B = imcomplement(BInv); %Display the original image and the enhanced images, side-by-side. montage({A,B}); click here to download the Matlab Livescript file: https://drive.google.com/file/d/1S8iBE42W0akwTQp2BksTsSd-NRb5MvV5/view?usp=sharing

Design & Implementation of Delta Robot for Pick-and-Place Operations Using Simulink

This example shows how to model a delta robot performing a pick and place task.

Contents

Introduction – Delta Robot Model Matlab KinematicsSolver object Simulink Model Delta Robot Subsystem Planning and Control Subsystem: Forward and Inverse Kinematics Planning and Control Subsystem: Path Planner Planning and Control Subsystem: Controller Simulation Results from Scopes The robot picks up a part using a vacuum gripper, moves the part to each of the four markers on the table, drops the part at the first marker, and then returns to the home position.

These forward and inverse kinematics computations are done using KinematicsSolver objects.

The Planning and Control/Controller subsystem contains a simple PID controller that drives the actual positions and velocities of the actuators to their desired values. The objects are defined as persistent variables in the functions sm_pick_and_place_robot_fk and sm_pick_and_place_robot_ik. These functions are called by the MATLAB function blocks Planning and Control/Forward Kinematics and Planning and Control/Inverse Kinematics 

Modeling and Simulation of a Piezoelectric Vibration Energy Harvester Using Matlab Simulink

This example shows how to model a device that harvests energy from a vibrating object by using a piezo bender.

This example shows how to model a device that harvests energy from a vibrating object by using a piezo bender.

The device uses this energy to charge a battery and power a load.

These devices are common in low-power applications that require energy autonomy, such as wearable devices or sensors in vehicles.

This energy harvester consists of a piezo bender, a rectifier, and a DC-DC converter. The left end of the piezo bender is clamped to a vibrating object, forcing the motion.

The right end of the piezo bender is connected to an extra mass.

Due to the elasticity, mass, and inertia of the piezo bender, the motion of the right end is not synchronous to the left end.

The deformations produce then a charge and voltage across the electrical terminals of the piezo bender, that are harvested into power.

The full-wave rectifier transforms the AC power generated by the piezo bender into DC power. It comprises four diodes and a capacitor that acts as a filter to smooth the DC voltage.

The buck converter regulates the voltage to transfer the maximum possible power to the load and ensures that the transfer of power is unidirectional.

In this example a pulse generator controls the converter in open-loop with a fixed switching frequency and duty cycle.

If the vibration source does not have a constant frequency or it contains harmonics, you can design a more sophisticated closed-loop controller to optimize the transfer of power and improve the efficiency of the energy harvester in different conditions. Initially, the energy harvester charges a battery.

Then both the energy harvester and the battery power up a constant power load.

Simulation & Analysis of Grid Connected Photo Voltaic Residential System with MPPT Controller

This example shows the operation of a photovoltaic (PV) residential system connected to the electrical grid.

Run the simulation and observe the resulting signals on the various scopes. 1) At 0.25s, with a solar irradiance of 1000 W/m2 on all PV modules, steady state is reached. The solar system generates 2400 Watts and the DC link is maintained at 400 volts with a small 120-Hz ripple due to the single-phase power extracted from the PV string. The Utility meter indicates that the system takes almost no power from the grid to supply the home total load. 2) At 0.3s, a partial shading condition is created by reducing the irradiance on some PV modules. When steady-state is reached at 0.35s, the MPPT controller has set the boost duty cycle at 0.44, generating a PV string voltage of 225 V. With this voltage, 920 W is extracted from the PV string. As you can see on the PV curve characteristic, the system is operating at a local maximum power point but not at the global maximum power point. 3) At 0.4s, a duty cycle scan of 0.25 seconds is performed by the MPPT controller to find the GMPP point. 4) At 0.7s, the MPPT controller has set the boost duty cycle at 0.58 generating a PV string voltage of 168 V. With this voltage, 1364 W is extracted from the PV string which is the GMPP value. The Utility meter indicates that it takes now around 1100 W (2500 W residential load - 1364 W supplied by PV) from the grid to supply the home total load.

Modeling and Control of PMSG-Based Energy System With Battery Charging

This example shows how to use a permanent magnet synchronous generator (PMSG) to charge a battery.

An ideal angular velocity source is used to maintain the rotor speed constant.

The Control subsystem uses Field Oriented Control to regulate the torque of the PMSG.

The torque reference is obtained as a function of dc-link voltage.

The initial battery state of charge is 25%.

The Scopes subsystem contains scopes that allow you to see the simulation results. The plot below shows the generator torque and the battery voltage and state of charge. Click here to download the simulink File: https://drive.google.com/file/d/1jydEGkk18Qfq-WIfU-HVu5SePWtRUY4V/view?usp=sharing

Modeling and simulation of Automotive Battery Pack for Electric vehicle DC Fast Charging Tasks

The example models a battery pack connected to an auxiliary power load from a chiller, a cooler, or other EV accessories.

This example shows how to model an automotive battery pack for DC fast charging tasks.

In this example, a battery pack is created by connecting three battery modules in series.

A resistance models the cable connection between individual modules.

A DC current source models the charger current and it is connected to the battery pack using a cable modeled as a resistance.

A power load across the battery terminals models the power consumption due to the chiller or the heater for coolant circuit. 

This example uses the parameters defined in the ee_lithium_pack_DCFC_ini.m file. 

Three cases are considered:

Case 1: The vehicle is parked in the parking area for a long time. The initial cell temperature is the same as the ambient temperature. The battery is heated during charging, with the initial battery state of charge equal to 20%. Case 2: The vehicle is driven and immediately charged. The initial battery cell temperature is equal to 285 K. The battery is heated during charging, with the initial battery state of charge equal to 20%. The cellInitialTemp workspace variable, defined in the ee_lithium_pack_DCFC_ini.m file, is changed to a value equal to the value of the Amb port plus 15.  Case 3: The vehicle is driven and immediately charged. The initial battery cell temperature is 285 K. The battery is not heated during charging (no auxillary power consumption), with the initial battery state of charge equal to 20%. The cellInitialTemp workspace variable, defined in the ee_lithium_pack_DCFC_ini.m file, is changed to a value equal to the value of the Amb port plus 15 and auxLoad is set to a low value equal to 1e-4. The coolant flow rate FlwR is set to zero by turning off the coolant flow inside the Controls/Flow_Control subsystem, setting NoFlow to 0. Click here to download the simulink file: https://drive.google.com/file/d/1EDdBN9WU1bcp6rogdQZcJyhyCrjZiKRZ/view?usp=sharing 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 https://www.paypal.com/paypalme/DrMSivakumar?locale.x=en_GB

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Simulation of DC Fast Charging Station Connected With the Battery Pack of an Electric Vehicle

This example models a DC fast charging station connected with the battery pack of an Electric Vehicle (EV). The main components of the example are: Grid: Model the AC supply voltage as a three-phase constant voltage source. DC Fast Charging Station: Model the power electronic circuits to convert the AC supply voltage from the grid to the DC voltage level that the EV battery pack requires. EV battery pack : Model the battery pack as series of battery cells. Filter & AC Measurements to filter the harmonics in the line current and measure the three-phase supply voltage and line current. Unity Power Factor (UPF) Front End Converter (FEC) to control output DC voltage at 800 V. The converter circuit is modeled with three levels of fidelity: Average Inverter Fidelity Two Level Inverter Fidelity Three Level Inverter Fidelity Isolated DC-DC converter supply constant charging current to the EV battery. These are the main components of the isolated DC-DC Converter: 1) Inverter, 2) HF Isolation Transformer, 3) Diode-Bridge Rectifier The EV Battery Pack models the battery cells connected in series and the sensors to measure the battery terminal voltage and output current. The plot below shows the DC bus voltage and current, battery terminal voltage, and charging current.

Click here to download the simulink files: 2021a version: https://drive.google.com/file/d/1bzFIVnNkwvPWWmBVgbErJVwjcC48EAkd/view?usp=sharing

2020a version: https://drive.google.com/file/d/1BL2z9bEMk5JP31ZOYpZ5Rj2QkhtuwBVf/view?usp=sharing

2019a version: https://drive.google.com/file/d/1FFhBgO74eGNuwu7xtlSi-rNOhiRY3XZR/view?usp=sharing

2018a version: https://drive.google.com/file/d/1zcSPaKp__tp-4ceNRutR9gxVnv2WJlTj/view?usp=sharing

2017a version: https://drive.google.com/file/d/1oFYrCT9iLlkaG-i_xFQ8Uu_K4C5BJe7m/view?usp=sharing

Modeling and simulation of Vehicle Engine Braking System Using Matlab Si...

What is engine braking?
Engine braking is basically the process of slowing the car down by releasing the accelerator and shifting down through gears, rather than using the footbrake.

Why Should we use it?
Using the footbrake is the most common way to reduce your speed, both in an emergency and in normal conditions.
Depending on how hard you press the brake pedal, you control the speed at which you slow down, when you stop, and when you start again.
But using the footbrake isn’t the only way to slow down. Though less commonly used, engine braking is a great way to improve your vehicle’s efficiency and help your brakes last longer.

Benefits of Engine Braking
It reduces wear and tear on your brakes.
The brake system relies on friction to slow down your vehicle.
Engine braking is especially helpful on long descents on mountains or hills.
Riding the brakes down a long slope can cause them to overheat, which decreases braking ability and damages the braking system.

Click here to download the model:
https://drive.google.com/file/d/1SMnxPJnZlYgpCNlqGnXr0rzVeT_YhM0N/view?usp=sharing

Modeling and Simulation of CAN based Model with Anti-lock Braking Sy...

This example shows how Stochastic Network Traffic causes timing Latency and Uncertainty in an anti-lock braking system (ABS) that uses Control Area Network (CAN) communications. This example shows an anti-lock braking system using CAN communications and highlights the negative effect that increased network utilization can produce on latency and response times. The model is representative of a real-world heavily-loaded network and also illustrates a domain-specific model of a distributed system. This example shows an anti-lock braking system using CAN communications and highlights the negative effect that increased network utilization can produce on latency and response times. Click here to download the simulink modal: https://drive.google.com/file/d/1VoDf...