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Exercise 800.1 — Hybrid-electric mission test case (PhlyGreen)

Reference documentation

This notebook uses the PhlyGreen library. The official documentation is here:

You are expected to consult the PhlyGreen documentation for:

  • class definitions and available models,
  • parameter meanings and units,
  • plotting utilities and conventions.

This exercise page focuses on what to explore and how to report results.

Notebook

🧪 Jupyter Notebook
hybrid.ipynb

This is a simple test case with:

  • a prescribed hybridization strategy (power split),
  • a battery model (Class II),
  • a mission-level simulation loop.

Learning objectives

By completing this exercise, you will learn to:

  • Run a hybrid-electric design loop using an existing engineering codebase
  • Identify key drivers of feasibility:
  • peak power,
  • battery energy capacity,
  • efficiency losses,
  • battery constraints (C-rate, usable SoC window, etc.)
  • Explore sensitivity to assumptions in a structured way
  • Produce a short engineering narrative from simulation results

How to run (minimum path)

Provided that the PhlyGreen code has been successfully installed (see Installation Setup →), launch jupyter:

jupyter lab

and open the notebook hybrid.ipynb.

What to focus on

PhlyGreen exposes many parameters and modeling options.
For this exercise, you are not expected to explore everything.

Focus on a small set of high-impact levers:

  • Power split ratio along the mission
    (how much power is provided electrically vs thermally, and when). This is controlled by the phi values in the MissionStages dictionary.

  • Battery cell and pack assumptions
    (energy density, power capability, C-rate,usable state-of-charge window). These are controlled by the Battery class parameters in the CellInput dictionary.

  • Powertrain efficiencies
    (electrical path vs thermal path losses). These are controlled by the Efficiencies in the EnergyInput dictionary.

  • Mission constraints
    (mission profile, range, payload, etc.). These are controlled by the MissionInput and MissionStages dictionaries.

Your goal is to understand which assumptions dominate the results,
not to fine-tune a single “optimal” configuration.

Guided questions

1) Baseline behavior: where does mass come from?

In the baseline configuration:

  • What are the main contributors to aircraft mass?
  • How is total mass partitioned between:
    • propulsion system,
    • fuel,
    • battery system,
    • remaining aircraft structure,
    • payload

Identify which subsystem dominates mass before any modification is applied.

2) Snowball effects of hybridization level

Increase the global hybridization level (power split) gradually.

  • How does required battery mass evolve?
  • How does increased battery mass feed back into:
    • required power,
    • required energy,
    • overall aircraft mass?

Describe the snowball mechanism linking hybridization → mass → power → mass.

3) Power vs energy driven redesign

Modify the power split strategy while keeping all components feasible.

  • When hybridization is increased mainly during high-power phases (e.g. climb):
    • which mass component grows most rapidly?

  • When hybridization is increased during lower-power phases (e.g. cruise):

    • does mass growth behave differently?

  • What happens to the battery pack architecture (number of cells in series/parallel)?

Explain whether the redesign is primarily power-driven or energy-driven.

4) Efficiency improvements and indirect mass effects

Improve drivetrain efficiency by a small amount.

  • Does total aircraft mass decrease?
  • Is the mass reduction proportional to the efficiency improvement?
  • Which mass components benefit most indirectly from improved efficiency?

Explain how small efficiency gains can propagate nonlinearly through the design loop.

5) Battery technology assumptions and feasibility illusion

Modify battery technology parameters (e.g. energy density).

  • Does improved battery performance always lead to a lighter aircraft?
  • Are there regimes where mass continues to increase despite “better” batteries?

Discuss why improving a component does not necessarily improve the system.

6) Design stability and diminishing returns

As hybridization is increased:

  • Does aircraft mass grow smoothly or accelerate?
  • Can you identify a region of diminishing returns, where added hybridization yields little benefit but large mass penalties?

Explain how this behavior emerges from repeated aircraft re-design rather than explicit constraints.

Student tasks

Task 1 — Baseline run and constraint diagnosis (core)

Run the notebook as provided and deliver:

  • one figure showing the mission power split (thermal vs electric),
  • one figure showing battery state evolution (SoC and/or relevant limits),
  • a short diagnosis (6–10 lines) explaining:
  • what limits the system in the baseline case,
  • during which mission phase the limit is reached.

Task 2 — One structured sensitivity sweep

Choose one parameter and sweep 5–8 values, for example:

  • constant hybridization level,
  • single-phase hybridization (e.g. climb only),
  • battery energy density,
  • drivetrain efficiency.

Deliver: - one plot of a key outcome versus the swept parameter
(e.g. battery mass, final SoC margin, constraint violations), - 6–10 lines interpreting the trend physically.

Task 3 — Strategy comparison (engineering narrative)

Define two hybridization strategies (A and B), for example:

  • A: high hybridization during climb, low during cruise
  • B: moderate hybridization throughout the mission

Compare both strategies using the same battery model.

Write 10–12 lines discussing: - which strategy is more feasible and why, - what trade-off it represents (power peaks vs energy consumption), - which strategy you would recommend if battery mass were constrained.

Task 4 — Critical reflection (mandatory)

In 8–12 lines, answer:

  • which results are robust (architecture-level insights),
  • which results are fragile (strongly assumption-dependent),
  • what additional data or modeling would be required before design decisions.

Limitations (important)

  • This is a simplified test case intended for learning and sensitivity exploration.
  • Results depend strongly on:
  • mission definition,
  • battery model fidelity and parameter assumptions,
  • drivetrain efficiency assumptions,
  • chosen power split strategy.
  • Structural, thermal, and safety aspects of battery integration are not modeled.
  • Aerodynamic and structural effects of battery integration are not modeled (e.g. increase drag). Aircraft structural mass simply scales with take-off mass.
  • Results should not be interpreted as optimized or certified designs.

Treat all outputs as conditional engineering evidence, not final truth.

Key takeaway

Hybrid-electric feasibility is governed by mission power profiles and constraints.

Maximizing hybridization is rarely optimal. The key engineering question is where and when electrical power is most valuable, given realistic limits on battery power, energy, and mass.