Varun Cumbamangalam
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Varun Cumbamangalam.

Senior IoT and Edge AI Engineer, AI Product Manager at OraLens Healthcare

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function autoload % Drive cycle data loader - reads simulation output columns % from the large-cycle dataset into named MATLAB workspace variables Time = largecycle(:, 1); % [s] - elapsed time Speed = largecycle(:, 3); % [rpm] - motor speed Torque = largecycle(:, 3); % [Nm] - motor torque Power = largecycle(:, 4); % [W] - shaft power demand TurbinePower = largecycle(:, 6); % [W] - microturbine output BatteryPower = largecycle(:, 7); % [W] - battery charge / discharge VehicleSpeed = largecycle(:, 8); % [km/h] - vehicle speed InstDist = largecycle(:, 9); % [m] - instantaneous distance TotalDist = largecycle(:,10); % [m] - cumulative distance InstEnergy = largecycle(:,11); % [Wh] - instantaneous energy TotalEnergy = largecycle(:,12); % [Wh] - cumulative energy consumed SOC = largecycle(:,13); % [0-1] - battery state of charge end
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AeroStrovillos - Microturbine Series Hybrid EV

MATLAB simulation of a microturbine and battery series hybrid for heavy vehicles.

AeroStrovillos - Microturbine Series Hybrid EV main screen
35T

Vehicle Class

LTO vs NMC

Battery Chemistries Compared

2 Drive Cycles

Validated Against

Tech Stack

MATLABSimulinkExcelEnergy ModellingDrive Cycle AnalysisBattery Sizing

The problem

Heavy trucks and buses burn enormous amounts of diesel, but a large part of that energy is wasted during steady highway cruising where the engine operates far from its most efficient point. Going fully electric is not viable at this scale because the battery would be too heavy and expensive. Could a small turbine acting as a range extender solve it?

The challenge

Heavy-duty commercial vehicles face a fundamental conflict between range, refueling infrastructure, and battery weight. A pure battery-electric 35-tonne bus requires a pack large enough to sustain steady highway power - but most of that capacity sits unused during low-demand urban segments and incurs significant weight penalty. The goal was to evaluate a series hybrid architecture where a microturbine runs at its optimal operating point as a continuous range extender, with the battery handling only transient peaks and regenerative recovery. The core challenge: prove out the sizing math - how much turbine power, how much battery capacity, and which chemistry (LTO for high power density or NMC for higher energy density) actually satisfies the drive cycle without over-engineering either component.

Simulation Methodology

Series hybrid powertrain for heavy-duty vehicle: continuous micro-range-extender provides base power at peak efficiency while battery handles transient demand peaks and regenerative recovery. System modeled against real urban and highway duty cycles to optimize component sizing and battery chemistry selection.

The simulation is built in MATLAB around drive-cycle data loaded from Excel time-series sheets. Two cycles are modelled: a short urban cycle and a large mixed-route cycle, each with vehicle speed, motor torque, power demand, and distance at every timestep. The microturbine provides steady base power at its best-efficiency operating point, while the battery absorbs or supplies the difference between turbine output and demand. It charges during regenerative braking and discharges during acceleration. State of Charge is tracked to confirm that the battery stays above its cutoff across both cycles. Battery sizing was evaluated for LTO, which has high cycle life and power density but lower energy density, and NMC, which has higher energy density but requires more thermal management. Required capacity came from the peak-to-average power ratio in the simulation.

Results

The simulation validated the series hybrid architecture for 35T heavy-vehicle operation. Microturbine steady-state dispatch at its peak efficiency operating point, combined with battery buffering of acceleration transients, produced a flatter turbine load profile and reduced total energy consumption compared to direct-drive baselines. The LTO chemistry satisfied the peak discharge rate requirement with less thermal risk; NMC offered a lighter pack for range-dominated routes. Battery sizing outputs from the simulation fed directly into the AeroStrovillos prototype specification, which was presented to TEV stakeholders as the design basis for a physical prototype.

Interested in this work?

I can walk through the architecture and code during an interview.

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