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🚗 Vehicle-to-Grid · Project Guide · Hardware + Simulation · BE · M.Tech · PhD

Vehicle-to-Grid Technology Project From Idea to Viva

Planning a vehicle-to-grid (V2G) technology project? This guide shows how to choose the right project type, follow a 16-week roadmap, select hardware, size a bidirectional converter, simulate fleet behaviour in Python and pick from 20 project topics with the simulation tools each one uses.

4
Project Types
16
Week Roadmap
20
Project Topics
Power ElectronicsBattery & BMSGrid ControlSchedulingAI / RLCommunicationEconomics

What Does a V2G Technology Project Involve?

A V2G project asks a concrete question: how can a parked electric vehicle exchange energy with the grid safely, profitably and without hurting the driver? Answering it touches four layers: the battery, the power converter, the control or scheduling logic, and the communication and market layer. Good projects go deep in one layer and stay consistent with the others.

Choose one primary contribution

  • A converter design with measured efficiency
  • A controller with proven stability
  • A scheduling algorithm with cost savings
  • A grid impact study on a test feeder
  • A secure communication testbed
  • A techno-economic business case

Why examiners like V2G

It combines power electronics, energy storage, control, optimisation and data. That breadth lets a student demonstrate many skills, but it also makes scope creep the number one risk, so define what is out of scope on day one.

Four Kinds of V2G Project

Pick the type that matches your lab access, time and degree level.

🔧

Hardware Prototype

Build and test a low-power bidirectional converter or controller board. Highest effort, strongest demo.

🖥️

Simulation Study

Model converter, battery and grid in Simulink or PLECS and validate control. Fastest route to results.

🧠

Algorithm / AI

Scheduling, forecasting or reinforcement learning in Python with real or synthetic data.

🔬

Hardware-in-the-Loop

Run real controller code against a real-time model of the grid and vehicle. Ideal for M.Tech and PhD.

A 16-Week V2G Project Roadmap

Adjust the durations to your semester, but keep the order: define, model, build, then compare.

Week 1–2 · Define scope and V2G mode

Choose V2G, V2H or V2B, the use case (peak shaving, frequency support, backup) and whether the deliverable is hardware, simulation or both. Write measurable objectives.

Week 3–4 · Literature and standards study

Read recent papers and the relevant standards (ISO 15118-20, IEEE 1547, OCPP). Identify the research gap that your project fills.

Week 5–7 · Model and simulate

Build battery, converter and grid models, tune controllers and test grid-connected and islanded cases in MATLAB/Simulink, PLECS or Python.

Week 8–10 · Design hardware or the algorithm

Size the converter magnetics and filter, select devices, or implement the scheduling or learning algorithm with realistic data.

Week 11–13 · Prototype and integrate

Assemble a low-power bench prototype (for example 100 W to 1 kW) or a hardware-in-the-loop setup, add sensing and communications.

Week 14–16 · Test, compare, document

Compare against a baseline, report metrics, record demo video, write the thesis or paper and prepare for viva.

Prototype Architecture and Bill of Materials

A scaled bench setup uses low voltage and isolation so that the control and power stages can be proven safely.

🔋 Battery / Emulator
(48 V + BMS)
↔
🔁 Isolated DC-DC
(DAB / CLLC)
↔
⚡ DC Link
+ Inverter / PFC
↔
🔌 LCL Filter
+ Grid / Simulator
🎛️ MCU / DSP
(PWM, PLL, PI)
↔
📏 Sensors
(V, I, T)
↔
📡 CAN / MQTT
/ OCPP Link
↔
🖥️ Dashboard
+ Data Logger
BlockTypical choicePurpose
Bidirectional DC-DC (DAB / CLLC)Silicon-carbide or GaN MOSFET half-bridges, 20–100 kHzIsolated power transfer between battery and DC link
Grid-tied inverter / PFC stageSingle-phase full bridge or three-phase 2-level, LCL filterConverts DC link to AC and back, controls current and power factor
Battery or emulator48 V Li-ion pack with BMS, or programmable DC sourceStores energy; BMS enforces voltage, current and temperature limits
ControllerTI C2000 (F28379D), STM32G4 or dSPACE / OPAL-RT targetRuns PWM, PLL, current loops and the scheduling logic
SensorsHall current sensors, isolated voltage sensors, energy meterFeedback for control and for measuring efficiency and power quality
ProtectionContactor, fuses, pre-charge circuit, anti-islanding logicSafe connection and fault handling
CommunicationCAN, Modbus, MQTT, OCPP / ISO 15118 simulator on a PC or Raspberry PiCharger-to-vehicle and charger-to-backend signalling
Test equipmentOscilloscope, power analyser, grid simulator or isolation transformerEfficiency, THD and transient measurement

Work only at low voltage unless your lab supervisor approves the setup. Mains-connected testing needs isolation, protection and trained supervision.

Sizing the Bidirectional DC-DC Stage

The dual active bridge is a popular choice because power flow reverses simply by changing the sign of the phase shift.

DAB power: P = V₁·V₂ · d(1 − d) / (2·n·f·L) where d = φ/π (0 ≤ d ≤ 1)
Design for rated power at d ≈ 0.25: L = V₁·V₂ · d(1 − d) / (2·n·f·P)
Example: V₁ = V₂ = 400 V, n = 1, f = 50 kHz, P = 7 kW, d = 0.25
L = 400 × 400 × 0.1875 / (2 × 50 000 × 7 000) ≈ 43 µH (series inductance, including transformer leakage)

Design checks to include in the report

  • Zero-voltage-switching range versus load
  • Inductor and transformer core saturation
  • Dead-time selection for SiC / GaN devices
  • Efficiency map for charge and discharge
  • Grid current THD against IEEE 1547 limits
  • Thermal rise at rated and overload power
  • Fault response and anti-islanding trip time

Python Model: How Much Power Can a Fleet Offer?

Before designing control, estimate the capacity a fleet could give. This Monte Carlo model draws arrival times, departure times and initial charge for 500 residential EVs, then computes hourly power they could inject or absorb while keeping a 30% driver reserve. The output below is what the script printed when run.

fleet_capacity.py — illustrative Monte Carlo
import numpy as np
rng = np.random.default_rng(7)
N, CAP, P_AC = 500, 40.0, 7.0                    # fleet size, kWh, kW per charger
RESERVE, SOC_MIN, ETA = 0.30, 0.20, 0.95         # driver reserve, floor, one-way efficiency
HOURS = np.arange(24)

# --- synthetic residential fleet: evening arrival, morning departure --------------------
arr = np.clip(rng.normal(18.5, 1.6, N), 15, 23).astype(int)
dep = np.clip(rng.normal(7.5, 1.0, N), 5, 10).astype(int)
soc_arr = np.clip(rng.normal(0.45, 0.15, N), 0.15, 0.85)
plug_prob = 0.85                                  # not every car is plugged in
plugged = rng.random(N) < plug_prob

def present(h, a, d):                             # plugged-in window wraps past midnight
    return (h >= a) | (h < d)

up, down, n_on = np.zeros(24), np.zeros(24), np.zeros(24, int)
for h in HOURS:
    mask = plugged & present(h, arr, dep)
    n_on[h] = mask.sum()
    # SOC at hour h if the car charged from arrival at P_AC (simple baseline)
    hrs_plugged = np.where(h >= arr, h - arr, h + 24 - arr)
    soc = np.minimum(0.95, soc_arr + ETA * P_AC * hrs_plugged / CAP)
    can_dis = mask & (soc > RESERVE + 0.05)       # export only above reserve
    can_chg = mask & (soc < 0.90)
    up[h]   = can_dis.sum() * P_AC                # kW the fleet can inject
    down[h] = can_chg.sum() * P_AC                # kW the fleet can absorb

print(f"{&#x27;hour':>4} {'plugged':>8} {'V2G up (kW)':>12} {'absorb (kW)':>12}")
for h in [0, 3, 6, 9, 12, 15, 18, 19, 20, 21, 22, 23]:
    print(f"{h:04d} {n_on[h]:8d} {up[h]:12.0f} {down[h]:12.0f}")
print(f"\nPeak injectable power : {up.max():.0f} kW at {up.argmax():02d}:00  ({up.max()/(N*P_AC):.0%} of nameplate)")
print(f"Energy exportable 18-22h (at 20 % participation): {0.2*up[18:22].sum():.0f} kWh")
print(f"Average availability   : {n_on.mean():.0f} of {N} vehicles plugged in")
output
hour  plugged  V2G up (kW)  absorb (kW)
0000      429         3003           91
0003      429         3003            0
0006      303         2121            0
0009        2           14            0
0012        0            0            0
0015       27          112          189
0018      297         1820         1827
0019      373         2450         2044
0020      415         2814         1799
0021      426         2919         1169
0022      429         3003          553
0023      429         3003          252

Peak injectable power : 3003 kW at 00:00  (86% of nameplate)
Energy exportable 18-22h (at 20 % participation): 2001 kWh
Average availability   : 236 of 500 vehicles plugged in

All distributions are assumed. The baseline assumes each car starts charging on arrival, so most batteries are full by midnight and available capacity peaks then. Real fleets differ, so replace the distributions with logged data and add owner opt-in rates before using any figure.

20 Vehicle-to-Grid Project Topics

Each topic lists the tools commonly used and a difficulty level.

MATLAB / SimulinkPLECSTyphoon HILOpenDSSHOMER ProPython / PyomoLTspiceTI C2000pandapowerANSYS Maxwell
#Project TopicSimulation / ToolsLevel
01Bidirectional DAB Converter for V2G Charger with Phase-Shift ControlMATLAB Simulink, PLECS, TI C2000⭐⭐ Intermediate
02Single-Phase Grid-Tied Bidirectional Charger with PR Current ControlSimulink, Typhoon HIL⭐⭐ Intermediate
03Three-Phase V2G Inverter with SRF-PLL and dq Current ControlSimulink Simscape, PLECS⭐⭐ Intermediate
04Vehicle-to-Home Backup System with Seamless IslandingSimulink, Arduino/STM32 prototype⭐⭐ Intermediate
05V2G Frequency Regulation Using Droop-Based Power ControlMATLAB Simulink, OpenDSS⭐⭐ Intermediate
06Reactive Power Compensation by EV Chargers in Distribution FeederOpenDSS, pandapower, Python⭐⭐ Intermediate
07Optimal V2G Scheduling with Time-of-Use Tariff Using LP / MILPPython Pyomo, Gurobi, HiGHS⭐ Basic
08Fleet Aggregator Model for V2G Capacity Estimation (Monte Carlo)Python NumPy, Pandas⭐ Basic
09Reinforcement Learning V2G Scheduling under Price and Arrival UncertaintyPython, Stable-Baselines3, Gymnasium⭐⭐⭐ Advanced
10Battery Degradation-Aware V2G Dispatch with Rainflow Cycle CountingMATLAB, Python, PyBaMM⭐⭐⭐ Advanced
11V2G Impact Study on Transformer Ageing and Feeder VoltageOpenDSS, DIgSILENT, MATLAB⭐⭐ Intermediate
12Blockchain-Based V2G Energy Trading and Smart ContractsEthereum / Hyperledger, Solidity, Python⭐⭐⭐ Advanced
13Secure ISO 15118 / OCPP Communication Testbed for V2GPython, Wireshark, Mosquitto, OCPP simulators⭐⭐⭐ Advanced
14Wireless Bidirectional Power Transfer for Vehicle-to-GridANSYS Maxwell, Simulink, PLECS⭐⭐⭐ Advanced
15Solar-Integrated V2G Charging Station with MPPT and Energy ManagementSimulink, HOMER Pro⭐⭐ Intermediate
16V2G Participation in Demand Response with Fleet ClusteringPython scikit-learn, Pyomo⭐⭐ Intermediate
17Multi-Agent V2G Coordination Using ADMMPython CVXPY, OpenDSS⭐⭐⭐ Advanced
18Digital Twin of a V2G Charging StationSimulink, Python, Node-RED, MQTT⭐⭐⭐ Advanced
19Low-Cost Hardware Prototype: 500 W Bidirectional DC-DC with Arduino/STM32LTspice, KiCad, STM32⭐ Basic
20Techno-Economic Analysis of V2G for an Indian Apartment CommunityHOMER Pro, Python, Excel⭐ Basic

Metrics, Results and Report Structure

Report numbers that others can reproduce.

01

Efficiency

Round-trip efficiency in charge and discharge across load, plus a loss breakdown.

02

Power Quality

Grid current THD, power factor and DC-link ripple compared with limits.

03

Control Performance

Rise time, overshoot and steady-state error for power or current commands, including a reversal.

04

Economic Result

Cost or revenue versus uncontrolled charging, with battery wear included.

05

Driver Constraint

Fraction of vehicles that meet their required departure SOC.

06

Report Chapters

Introduction, literature review, system model, method, results, comparison, conclusion, future work.

Common Mistakes to Avoid

A

Scope Too Wide

Trying converter, scheduling and blockchain in one semester. Pick one primary contribution.

B

No Baseline

Reporting savings without comparing against uncontrolled charging.

C

Ignoring Battery Wear

Showing profit from heavy cycling without any degradation penalty.

D

Ideal Components

Assuming 100% efficient converters; include losses and dead time.

E

Unsafe Hardware Testing

Connecting an unprotected prototype to the mains or a real vehicle.

F

Unreproducible Results

Not sharing parameters, seeds and data, so results cannot be repeated.

Frequently Asked Questions

Simulation is faster and fits a single semester; hardware earns extra marks but needs lab access, safety review and more time. A strong middle path is a validated Simulink model plus a low-voltage bench prototype of one stage, such as the bidirectional DC-DC converter.
No. A battery pack with a BMS, a programmable DC supply or a battery emulator is enough to represent the vehicle. Never connect an experimental converter to a real EV or to the mains without proper isolation and supervision.
Compare against uncontrolled charging (plug in and charge immediately) and, if relevant, against one-way smart charging. Show the difference in cost, peak demand and battery wear.
No. It is an illustrative Monte Carlo with assumed arrival, departure and SOC distributions. It demonstrates the method for estimating aggregate capacity; real projects should use measured charging data.
Typical venues include IEEE Transactions on Smart Grid, IEEE Transactions on Transportation Electrification, Applied Energy, Journal of Energy Storage and IEEE PES / ITEC conferences. Check each venue's current scope and deadlines.

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