Lithium-Ion Battery Pack Design Simulation for Drones and Aerospace

Introduction

In a car, a battery pack that is 10% heavier costs a little range. In a drone, it can cost a fifth of the flight time. In an electric aircraft, it can decide whether the mission is possible at all.

Aerospace battery design is a fight over every gram, and the fight is unusually unforgiving. Packs are asked for very high discharge rates in short bursts, such as takeoff, hover and climb. They must run hot without overheating, they cannot be allowed to fail in flight, and they must meet strict safety expectations. A battery failure in the air has no safe shoulder to pull over onto.

Simulation lets teams work through these trade-offs before building hardware. The IST Li-ion Battery Simulator is a browser-based workbench that takes a pack from application targets through sizing, cell modelling, thermal analysis, BMS logic, ageing and a design report, with nothing to install.

Where These Packs Are Used

• Multirotor drones: Survey, inspection, photography, delivery and agricultural spraying.
• Fixed-wing and VTOL UAVs: Mapping, surveillance and long-endurance missions where energy density matters most.
• Heavy-lift and cargo drones: Larger packs with high hover currents.
• eVTOL and electric aircraft: Very high power at takeoff and landing, with strict redundancy and safety expectations.
• Defence and specialised UAS: Wide temperature ranges, rugged use and demanding reliability.

What Makes Aerospace Packs Different

Four pressures dominate:

• Specific energy: Every gram of enclosure, busbar and insulation reduces flight time.
• High discharge rates: Hover and climb draw large currents, which means heat, voltage sag and limited cell choices.
• Thermal limits with little cooling: Packs often rely on airflow or conduction to the airframe.
• Safety in flight: A single-cell failure must not become a pack-wide event.

Step 1: Start from the Mission

The simulator begins with Define Application: voltage window, energy capacity, continuous and peak discharge power, peak duration, charge power, ambient range, system mass, weight budget and envelope, IP target, cooling method and standards.

For a drone, translate the flight profile into these fields. Continuous power approximates cruise or hover, while peak power and peak duration represent takeoff or climb. The guide’s advice applies strongly here: fill in the targets carefully, because the most useful output is the list of targets your chosen cell and BMS cannot meet.

Step 2: Choose the Cell and BMS

The database holds cylindrical, prismatic and pouch cells in several chemistries. For aerospace work, the selection criteria that matter most are:

• Weight and specific energy: Small cylindrical and pouch formats are common because of their energy-to-weight ratio.
• Continuous and pulse current ratings: The simulator’s peak-power check will fail if your cell and parallel count cannot supply the current needed at minimum voltage.
• DCIR: Lower resistance means less heat and less voltage sag.
• Temperature limits: Check both discharge limits at high load and charge limits for cold-weather operation.
• Data quality: Prefer datasheet values, and note estimated fields, which the simulator flags.

On the BMS side, the simulator checks that the series range covers your Ns, that OVP and UVP sit inside the cell window, that continuous and peak over-current ratings match your hover and climb currents, and that over-temperature trips sit within the cell’s limits. Also check balancing current, number of NTC inputs and the communication protocol your flight controller expects. If you need a BMS or cell not in the library, you can import it as a .dat file.

Step 3: Size the Pack

In Pack Configuration you enter series cells, parallel cells and module count, or ask the simulator to suggest them from your targets. It returns the cell model, a cell-to-BMS check, pack sizing, a pass/warn/fail check against your targets and a standards checklist.

An illustrative example. The numbers below use a hypothetical 5 Ah, 3.6 V, 15 mΩ, 70 g cylindrical cell and an assumed mission. They are my own back-of-envelope calculations using the guide’s formulas, not simulator output, so substitute your own cell and mission data.

• Configuration: 12S4P, giving 48 cells
• Nominal voltage: 12 × 3.6 = 43.2 V
• Capacity and energy: 4 × 5 Ah = 20 Ah, so about 864 Wh, or roughly 778 Wh usable at 90%
• Pack resistance: 12 × 15 mΩ ÷ 4 × 1.10 ≈ 49.5 mΩ
• Mass: 48 × 70 g = 3.36 kg of cells; dividing by the guide’s 0.65 cylindrical packing ratio gives roughly 5.2 kg, or about 165 Wh/kg at pack level
• Hover at an assumed 3 kW: about 70 to 75 A, or roughly 18 A per cell (nearly 4C), with about 250 W of heat generated in the pack
• Endurance at that load: roughly 15 minutes

Now the peak check. A 6 kW climb at an assumed 36 V minimum operating voltage needs about 167 A, which is roughly 42 A per cell, or above 8C. That is beyond what typical energy-oriented 21700 cells can deliver, and the targets check would flag it. The fixes are the ones the guide lists: raise Np, choose a power-oriented cell, or reduce the peak demand. That is exactly the kind of problem you want to catch at the screen rather than in a test flight.

Step 4: Model the Cell

The BDS Cell Modeling module builds the models behind later analysis:

• HPPC-based equivalent-circuit extraction gives R0 and two RC pairs, which determine voltage sag under load.
• EIS-based fitting and a reduced-order P2D (SPMe-type) electrochemical solver, benchmarked against PyBaMM’s DFN model within about 2 mV RMS up to roughly 6C, which is relevant for high-rate drone discharge.
• Joint parameter identification from a measured voltage trace.

When test data is not available, the simulator auto-generates inputs from the selected cell, labelled as synthetic. For high-rate flight packs, replacing these with measured data from your own cell is one of the most valuable validation steps.

Step 5: Thermal Analysis

At several C of discharge, even a modest resistance makes real heat. In the example above, 250 W of heat in a pack with about 3.4 kg of cells could, with no heat escape at all, raise cell temperature by well over 50 K during a 15-minute flight (a rough estimate that assumes a typical cell heat capacity, and real packs lose some heat). That is why thermal design cannot wait.

The thermal tools include:

• Conjugate heat transfer along a cooling path and coolant flow and pressure drop calculations.
• 3-D cooling simulation for natural convection, forced air through cell gaps or a liquid cold plate. For drones, forced-air and natural-convection options are the most relevant. It reports maximum temperature, cell-to-cell ΔT, energy balance, and for forced cooling the pressure drop and fan power. The guide suggests targets of Tmax at or below 45 °C and ΔT at or below 5 K.
• Thermal imaging live view for a staged cooling controller across modules.

Two honest limits apply. First, the 3-D cooling tool is not a Navier-Stokes solution, so airflow design around real airframe ducts needs full CFD or a test. Second, nothing in the guide describes modelling altitude, low air density or cold-soak at altitude, so those effects need to be handled separately.

Step 6: Thermal Runaway and Propagation

For aircraft, this is the analysis that matters most. The propagation module models a module as a network of cells with per-cell abuse kinetics, internal short after separator melt, venting, and heat transfer through gaps, barriers, busbars and radiation.

You choose the trigger, test SOC, cooling state and barrier (none, 2 mm air, 1 mm mica, 2 mm aerogel or 3 mm ceramic fibre). Results show how many cells enter runaway, the time between the first and second event and an automatic barrier comparison. Barrier choice is a direct weight trade-off, so this lets you find the lightest barrier that stops propagation or buys the required time. The guide’s 5-minute proxy comes from road-vehicle practice, so for aviation use treat it as a screening reference rather than a requirement.

An ARC-style runaway analysis, auto-generated from the selected cell, also shows self-heating onset and the margin to the BMS over-temperature trip.

Step 7: BMS Design and Fault Testing

Because a BMS fault in flight is serious, the verification tools carry extra weight:

• Protection and balancing simulation covers OV, UV, over-current, short circuit, over-temperature and sensor faults.
• SOC and SOH estimation (EKF SOC with resistance tracking) matters for reliable remaining-flight-time estimates, since voltage sag at high C-rates makes naive voltage-based estimates misleading.
• Fault-injection matrix tests over-charge, over-discharge, over-current, external short, cooling failure, cold charging and open sensors, with detection latency against requirement and a diagnostic code for each.
• Code generation with SIL and HIL produces C code, a CAN message set and test vectors. A SIL pass means the generated C reproduces the simulator’s logic. The guide is clear that this is template-based, MISRA-oriented, not certified and has no ISO 26262 tool qualification, and that short-circuit protection must also exist in hardware. Aviation software assurance is a separate matter that this tool does not claim to cover.

One more limitation to state plainly: the built-in drive cycles (MIDC, WLTC, WMTC and others) are road-vehicle profiles. There is no flight-profile cycle in the guide, so for drones you represent the mission through continuous and peak power targets, peak duration and your own duty assumptions.

Step 8: Electrical Design

The Circuit Design tab derives design current, a temperature-derated fuse, busbar cross-section (about 3 A/mm² for copper), pre-charge sizing and minimum isolation, and checks fuse breaking capacity against short-circuit current. The DFM card builds module geometry from the real cell size and provides a tolerance stack-up, which matters in tight airframe envelopes. Module wiring diagrams show series-parallel connections, balancing taps and NTC positions, with SVG and STL export. Since busbars and nickel strip carry large currents in a high-rate pack, checking their resistance and heating is worthwhile, because every milliohm costs both heat and range.

Step 9: Life and Duty

Drone packs are cycled often and pushed hard, so lifetime is a real cost driver.

• Calendar and cycle ageing models SEI growth, particle cracking and lithium plating, with plating risk rising in cold or fast-charge conditions. That is relevant for winter field operations and quick turnarounds between flights. The model is calibrated to the cell’s datasheet cycle-life rating.
• Warranty and life prediction estimates how many flights or cycles remain before the SOH threshold, depending on depth of discharge, charge rate and temperature. Note that its vehicle usage inputs are written for road distance, so for drones use the cycles-per-day style inputs.

The guide cautions that ageing results are a screening estimate until fitted to your own storage and cycling tests.

Step 10: Safety Checks and the Design Report

The Safety & Compliance tab covers capacity, resistance, isolation, overcharge, short circuit, drop, shock, vibration and protection response time, then rolls them into a release decision where any failed or un-run gate blocks release. The standards checklist can include UN 38.3 for transport and IEC 62619 for industrial lithium batteries. Aviation-specific airworthiness requirements are not built into the checklist, so confirm those separately with your regulator or certifying body.

Reports then runs the full chain and produces an A4 design report with the schematic, simulation results, standards checklist and bill of quantities.

Questions Simulation Helps Answer

• Can this cell and parallel count deliver the climb current without voltage collapse or overheating?
• How much flight time do I gain from a larger pack, and how much does the extra mass take back?
• How hot does the pack get at the end of a flight, and do I need ducted airflow?
• What is the lightest barrier that stops one failing cell from spreading?
• Does the BMS detect each fault quickly enough, and what does the flight controller see?
• How many flights will the pack deliver before it falls below the SOH threshold?

What the Simulator Is, and Isn’t

It is a design and screening tool. Pack arithmetic follows standard formulas, while thermal, abuse, ageing and system models are reduced-order. It does not replace full CFD for airflow design, or the physical tests needed for approval, and it does not model altitude effects or flight profiles directly. Ageing results are a screening estimate until fitted to test data.

A sensible validation path:

1. Replace synthetic HPPC, EIS and ARC data with measured data for your chosen cell.
2. Test a prototype module at the real hover and climb currents, comparing temperature and voltage sag with the simulation.
3. Run SIL, then HIL, then hardware protection tests on the BMS.
4. Complete cell and pack safety tests with an accredited lab, and meet any applicable aviation requirements.
5. Start cycling tests early, then refit the ageing model before relying on flight-count claims.

Conclusion

Drone and aerospace packs are designed at the edge: lightest possible, highest power, least cooling, highest consequence. Simulation lets teams explore that edge safely, finding the cell, parallel count, barrier and BMS settings that meet the mission with margin, before anything leaves the ground.

Explore pack design at Academy of EV Technology and follow the step-by-step guide from mission targets to a full design report.