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.
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.
Read recent papers and the relevant standards (ISO 15118-20, IEEE 1547, OCPP). Identify the research gap that your project fills.
Build battery, converter and grid models, tune controllers and test grid-connected and islanded cases in MATLAB/Simulink, PLECS or Python.
Size the converter magnetics and filter, select devices, or implement the scheduling or learning algorithm with realistic data.
Assemble a low-power bench prototype (for example 100 W to 1 kW) or a hardware-in-the-loop setup, add sensing and communications.
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.
(48 V + BMS)
(DAB / CLLC)
+ Inverter / PFC
+ Grid / Simulator
(PWM, PLL, PI)
(V, I, T)
/ OCPP Link
+ Data Logger
| Block | Typical choice | Purpose |
|---|---|---|
| Bidirectional DC-DC (DAB / CLLC) | Silicon-carbide or GaN MOSFET half-bridges, 20–100 kHz | Isolated power transfer between battery and DC link |
| Grid-tied inverter / PFC stage | Single-phase full bridge or three-phase 2-level, LCL filter | Converts DC link to AC and back, controls current and power factor |
| Battery or emulator | 48 V Li-ion pack with BMS, or programmable DC source | Stores energy; BMS enforces voltage, current and temperature limits |
| Controller | TI C2000 (F28379D), STM32G4 or dSPACE / OPAL-RT target | Runs PWM, PLL, current loops and the scheduling logic |
| Sensors | Hall current sensors, isolated voltage sensors, energy meter | Feedback for control and for measuring efficiency and power quality |
| Protection | Contactor, fuses, pre-charge circuit, anti-islanding logic | Safe connection and fault handling |
| Communication | CAN, Modbus, MQTT, OCPP / ISO 15118 simulator on a PC or Raspberry Pi | Charger-to-vehicle and charger-to-backend signalling |
| Test equipment | Oscilloscope, power analyser, grid simulator or isolation transformer | Efficiency, 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.
Design for rated power at d ≈ 0.25: L = V₁·V₂ · d(1 − d) / (2·n·f·P)
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.
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"{'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")
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.
| # | Project Topic | Simulation / Tools | Level |
|---|---|---|---|
| 01 | Bidirectional DAB Converter for V2G Charger with Phase-Shift Control | MATLAB Simulink, PLECS, TI C2000 | ⭐⭐ Intermediate |
| 02 | Single-Phase Grid-Tied Bidirectional Charger with PR Current Control | Simulink, Typhoon HIL | ⭐⭐ Intermediate |
| 03 | Three-Phase V2G Inverter with SRF-PLL and dq Current Control | Simulink Simscape, PLECS | ⭐⭐ Intermediate |
| 04 | Vehicle-to-Home Backup System with Seamless Islanding | Simulink, Arduino/STM32 prototype | ⭐⭐ Intermediate |
| 05 | V2G Frequency Regulation Using Droop-Based Power Control | MATLAB Simulink, OpenDSS | ⭐⭐ Intermediate |
| 06 | Reactive Power Compensation by EV Chargers in Distribution Feeder | OpenDSS, pandapower, Python | ⭐⭐ Intermediate |
| 07 | Optimal V2G Scheduling with Time-of-Use Tariff Using LP / MILP | Python Pyomo, Gurobi, HiGHS | ⭐ Basic |
| 08 | Fleet Aggregator Model for V2G Capacity Estimation (Monte Carlo) | Python NumPy, Pandas | ⭐ Basic |
| 09 | Reinforcement Learning V2G Scheduling under Price and Arrival Uncertainty | Python, Stable-Baselines3, Gymnasium | ⭐⭐⭐ Advanced |
| 10 | Battery Degradation-Aware V2G Dispatch with Rainflow Cycle Counting | MATLAB, Python, PyBaMM | ⭐⭐⭐ Advanced |
| 11 | V2G Impact Study on Transformer Ageing and Feeder Voltage | OpenDSS, DIgSILENT, MATLAB | ⭐⭐ Intermediate |
| 12 | Blockchain-Based V2G Energy Trading and Smart Contracts | Ethereum / Hyperledger, Solidity, Python | ⭐⭐⭐ Advanced |
| 13 | Secure ISO 15118 / OCPP Communication Testbed for V2G | Python, Wireshark, Mosquitto, OCPP simulators | ⭐⭐⭐ Advanced |
| 14 | Wireless Bidirectional Power Transfer for Vehicle-to-Grid | ANSYS Maxwell, Simulink, PLECS | ⭐⭐⭐ Advanced |
| 15 | Solar-Integrated V2G Charging Station with MPPT and Energy Management | Simulink, HOMER Pro | ⭐⭐ Intermediate |
| 16 | V2G Participation in Demand Response with Fleet Clustering | Python scikit-learn, Pyomo | ⭐⭐ Intermediate |
| 17 | Multi-Agent V2G Coordination Using ADMM | Python CVXPY, OpenDSS | ⭐⭐⭐ Advanced |
| 18 | Digital Twin of a V2G Charging Station | Simulink, Python, Node-RED, MQTT | ⭐⭐⭐ Advanced |
| 19 | Low-Cost Hardware Prototype: 500 W Bidirectional DC-DC with Arduino/STM32 | LTspice, KiCad, STM32 | ⭐ Basic |
| 20 | Techno-Economic Analysis of V2G for an Indian Apartment Community | HOMER Pro, Python, Excel | ⭐ Basic |
Metrics, Results and Report Structure
Report numbers that others can reproduce.
Efficiency
Round-trip efficiency in charge and discharge across load, plus a loss breakdown.
Power Quality
Grid current THD, power factor and DC-link ripple compared with limits.
Control Performance
Rise time, overshoot and steady-state error for power or current commands, including a reversal.
Economic Result
Cost or revenue versus uncontrolled charging, with battery wear included.
Driver Constraint
Fraction of vehicles that meet their required departure SOC.
Report Chapters
Introduction, literature review, system model, method, results, comparison, conclusion, future work.
Common Mistakes to Avoid
Scope Too Wide
Trying converter, scheduling and blockchain in one semester. Pick one primary contribution.
No Baseline
Reporting savings without comparing against uncontrolled charging.
Ignoring Battery Wear
Showing profit from heavy cycling without any degradation penalty.
Ideal Components
Assuming 100% efficient converters; include losses and dead time.
Unsafe Hardware Testing
Connecting an unprotected prototype to the mains or a real vehicle.
Unreproducible Results
Not sharing parameters, seeds and data, so results cannot be repeated.
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