EV vs ICE Comparison with ADVISOR: Energy, Emissions and Cost

The problem

Comparisons between EVs and conventional cars are often made with headline figures that ignore drive cycle, grid electricity mix and component efficiency. A simulation-based comparison with stated assumptions shows how the answer depends on driving pattern and electricity source, which is useful for policy, fleet and design discussions.

Objectives

• Set up ADVISOR and run its conventional and electric vehicle configurations.
• Configure comparable example vehicles of similar size and performance.
• Run both vehicles on urban and mixed drive cycles and compare energy per kilometre.
• Convert results into CO₂ and running cost using stated, sourced factors.
• Analyse sensitivity to grid emission factor, fuel price and electricity tariff.

What you need

Key concepts

Tank-to-wheel and well-to-wheel

Tank-to-wheel counts only energy and emissions at the vehicle. Well-to-wheel adds fuel production and, for EVs, electricity generation and grid losses.

Grid emission factor

Kilograms of CO₂ per kWh of electricity delivered, which depends on the generation mix. For India it should be taken from the latest Central Electricity Authority baseline database.

Engine efficiency map

Fuel consumption of the engine at each speed and torque. Urban cycles keep a petrol engine at low load where efficiency is poor.

Comparable vehicles

To be fair, compared vehicles should have similar mass class, frontal area and acceleration performance, otherwise differences reflect size rather than powertrain.

Step-by-step guide

1. 1

   Install and run ADVISOR

   Add ADVISOR to the MATLAB path and launch its GUI. Run one supplied conventional and one supplied electric vehicle on a standard cycle to learn the workflow: vehicle input, simulation setup and results screens. Older ADVISOR code can raise warnings on new MATLAB releases; record any changes you make to get it running and keep them in a notes file.

   Checkpoint: Both example vehicles complete a cycle and the results screen shows fuel or electrical consumption.

2. 2

   Define comparable example vehicles

   Edit the vehicle input files to describe two example cars of the same body: same Cd, frontal area, Crr and wheel radius. Give the petrol car an engine and gearbox and the EV a motor, single-speed reduction and battery sized for a stated range. Adjust masses to reflect the different powertrains, and record every value with its source or a note that it is an assumption.

   Checkpoint: A side-by-side parameter table of both vehicles with sources and assumptions marked.

3. 3

   Match performance

   Run ADVISOR's acceleration and gradeability tests for both vehicles and adjust engine or motor size until 0–100 km/h times are within about 10 per cent. Without this step, the EV may look better or worse just because it is more or less powerful. Record the final acceleration and gradeability results. Also compare top speed and gradeability, and note any remaining differences that could favour one vehicle in the energy results.

   Checkpoint: Acceleration times of the two vehicles within the stated tolerance.

4. 4

   Add Indian drive cycles

   Create ADVISOR cycle files for IDC and MIDC from CSV speed traces, following the format of the supplied cycle files (time and speed vectors in the expected units). Also use WLTP or another supplied mixed cycle. Run a quick check that the cycle distance ADVISOR reports matches your own calculation. Plot ADVISOR's achieved speed against the requested speed to confirm both vehicles can follow the new cycles without large tracking errors.

   Checkpoint: ADVISOR reports distance and duration for the new cycles matching the CSV data.

5. 5

   Run the energy simulations

   Run both vehicles on each cycle. For the petrol car, record fuel consumption in litres per 100 km and engine operating points; for the EV, record battery energy in Wh/km and SOC change, with SOC correction if needed. Export results to the workspace and save them in a MAT file with the cycle and vehicle names so the comparison can be scripted.

   Checkpoint: A results table of energy per kilometre for both vehicles on every cycle.

6. 6

   Convert energy to CO₂

   For tank-to-wheel, convert fuel to CO₂ using a stated carbon content per litre. For well-to-wheel, add an upstream factor for fuel and use the grid emission factor (from the latest CEA database) plus charging and grid losses for the EV. Put all factors in one table with sources so readers can change them. Keep the calculation in a script, not only in a spreadsheet, so it can be rerun when factors are updated.

   Checkpoint: Tank-to-wheel and well-to-wheel g CO₂/km for both vehicles on each cycle, with factor sources listed.

7. 7

   Running-cost comparison

   Compute energy cost per kilometre using a local fuel price and an electricity tariff entered as inputs, not fixed numbers. Include charging losses for the EV. Present cost per kilometre for each cycle, and keep the calculation in a MATLAB function so the sensitivity study can call it. Keep energy cost separate from maintenance and purchase cost, which are outside this simulation, and say so clearly in the results table.

   Checkpoint: Cost per kilometre for both vehicles on each cycle, driven entirely by stated inputs.

8. 8

   Sensitivity study

   Sweep the grid emission factor (from a coal-heavy to a renewable-heavy mix), fuel price and electricity tariff, and find break-even points where the EV and petrol car have equal CO₂ or equal cost. Plot well-to-wheel CO₂ against grid factor for each cycle. Explain why the EV advantage is usually larger on urban cycles. Mark the present value of each input on the plots so readers can see where today's conditions sit.

   Checkpoint: Break-even plots for CO₂ and cost with the break-even values marked.

9. 9

   Report

   Present the vehicle definitions, performance matching, cycle results, CO₂ and cost conversion, and the sensitivity study. Be clear that results depend on stated assumptions and that manufacturing emissions (especially battery production) are not included unless you add them. Avoid general claims beyond what your inputs support. Include the vehicle input files, cycle files and post-processing scripts as an appendix so every result in the report can be regenerated by another student.

   Checkpoint: A report where every number in the conclusions can be traced to a stated input and a simulation run.

Testing & validation

• Cycle distances match the input data.
• Acceleration of the two vehicles is matched within tolerance.
• EV SOC balance and petrol fuel use are consistent with power flows.
• CO₂ and cost calculations reproduce by hand for one cycle.

Expected results

A transparent EV versus petrol comparison on Indian and international cycles, with energy, CO₂ and cost per kilometre and break-even plots that show how the answer depends on grid mix and prices.

Take it further

• Add a parallel hybrid configuration as a third vehicle.
• Include battery and vehicle manufacturing emissions for a simple life-cycle comparison.
• Add air-conditioning auxiliary loads.
• Repeat with a two-wheeler pair (petrol scooter against electric scooter).

Viva and review questions

Learn it at DIYguru

These DIYguru courses and programmes teach what this project needs.