When we talk about battery testing for eVTOL applications, we're operating in a completely different risk landscape than automotive. In a car, if your battery fails, you pull over, stop the engine, and get out. You might call roadside assistance or wait for help, but the consequences are manageable—inconvenience, perhaps some cost, but rarely life-threatening.
In flight, the equation changes dramatically. If an eVTOL battery fails mid-air, pilots need several minutes to execute a controlled descent and locate a suitable emergency landing site.
They require sustained power to maintain flight control systems, navigation, and communication during this critical period. Complete power loss at altitude? The consequences become catastrophic.
This fundamental difference in failure consequences demands that we rethink our entire approach to battery testing. Standard automotive protocols, designed for ground-based applications where failure equals inconvenience, simply cannot capture the reliability requirements for applications where failure equals disaster.
We need testing methodologies that match the stakes, and that means replicating real-world operational stresses with unprecedented fidelity.
Comparison of key battery performance demands between eVTOL and EV applications. eVTOL batteries face significantly higher stress in terms of discharge rates, peak power duration, charge frequency, and safety requirements.
Source: Yang et al., “Challenges and key requirements of batteries for electric vertical takeoff and landing aircraft”, Joule, 2023.
eVTOL aircraft create power demands that exceed the capabilities of batteries in standard testing protocols. During vertical takeoff, batteries must deliver 15C discharge pulses for 45+ seconds—a sustained high-power output that most automotive test cycles never approach.
We're not talking about brief acceleration bursts; these are extended periods of maximum power output when failure isn't an option.
eVTOL missions demand rapid high-C discharges for takeoff and landing, unlike the smoother, lower-power profile of EVs, highlighting the need for mission-specific testing protocols.
The mission profile complexity makes this even more challenging. Aircraft transition rapidly from takeoff's 15C demands to cruise phase 2-3C requirements, then back to 8C for landing—all whilst the battery state of charge steadily depletes. The landing phase presents the highest risk scenario: pilots need significant power from batteries that have already been drained during flight operations.
Emergency scenarios compound these challenges further. Aircraft must maintain sufficient power for controlled descent procedures, emergency communications, and navigation systems. Standard test cycles, designed around predictable automotive drive patterns, completely miss these safety-critical operational requirements.
When batteries sustain 15C discharge rates, lithium ions can begin plating on the anode surface rather than intercalating properly into the electrode structure. This creates metallic lithium deposits that form dendrite structures—needle-like growths that can pierce separator membranes and create internal short circuits.
Standard automotive cycling tests, typically running at 1-3C rates, never stress the electrochemical system enough to trigger this failure mode. Yet for eVTOL applications, anode plating represents a pathway to sudden, catastrophic battery failure. These dendrites can cause thermal runaway events or complete power loss with minimal warning, exactly the failure modes that aviation applications cannot tolerate.
The progression from initial plating to thermal runaway can occur over just a few cycles under high-stress conditions. Static testing protocols miss this entirely because they never recreate the sustained high-power conditions that trigger the failure mechanism.
During the landing phase, pilots need reliable power delivery from batteries that have been depleted during flight operations. This end-of-discharge performance becomes critical for flight safety, yet it's precisely where many batteries exhibit their worst performance characteristics.
As cells approach depletion, internal resistance increases and voltage drops under load become more pronounced. In automotive applications, this gradual performance degradation provides ample warning and allows drivers to adapt. In aviation, pilots need predictable power delivery throughout the entire battery capacity range.
Standard static testing typically focuses on mid-range state-of-charge performance or uses constant-current discharge profiles that don't capture real-world loading conditions. They miss the voltage instability and power fade characteristics that occur when aircraft systems demand high power from nearly depleted batteries—the exact scenario pilots face during emergency landings.
Heat generation during rapid power transitions creates thermal stress patterns that static testing cannot replicate. During takeoff, batteries generate significant heat due to high current flow, which they must dissipate during lower-power cruise phases, only to generate heat again during landing operations.
This cyclical thermal stress, combined with the cumulative heat buildup over mission profiles, creates conditions that lead to progressive thermal events. Unlike automotive thermal runaway scenarios, where drivers have time to stop and evacuate, aviation thermal events can progress during flight operations when pilots have limited response options.
Static testing typically evaluates thermal performance under steady-state conditions or single-point thermal abuse tests. These approaches overlook the dynamic thermal loading that occurs during actual flight operations, where thermal management systems must handle rapid heat generation cycles while maintaining safe operating temperatures throughout the mission profile.
eVTOL mission profiles subject batteries to constantly varying discharge rates that accelerate degradation through mechanisms not captured in standard cycling tests. The rapid transitions between high-power takeoff, moderate cruise, and high-power landing create electrochemical stresses that differ significantly from steady-rate cycling.
Single C-rate life testing can provide false confidence in battery longevity because it doesn't account for the accelerated degradation that occurs when cells experience mixed-rate cycling patterns.
Research shows that batteries cycled under varying C-rates degrade faster than those cycled at constant rates, even when the average power delivery remains similar.
For eVTOL operators, this translates directly to operational economics. If cycle life predictions based on standard testing prove optimistic, aircraft may require battery replacements far more frequently than projected, creating unsustainable operational costs and safety risks from unexpected degradation.
The fundamental challenge facing eVTOL battery testing lies in replicating actual flight conditions with sufficient fidelity to capture real failure modes. Small deviations between test protocols and operational reality can result in the complete omission of critical failure mechanisms.
Power profile timing matters enormously. A 30-second 15C pulse followed by immediate rest doesn't stress the battery system as much as a 45-second pulse followed by moderate cruise loading.
Temperature variations during flight, vibration effects, and even the specific current rise rates during power transitions all influence how batteries behave and degrade.
Traditional test protocol development relies on simplified approximations of operational conditions. For applications where the consequences of failure are severe, these approximations introduce unacceptable uncertainty regarding real-world performance and reliability.
Machine learning offers powerful tools for optimising test protocols to match real-world operational conditions more precisely. By analysing actual flight data and correlating it with battery performance patterns, AI systems can identify which testing parameters matter most for capturing critical failure modes.
Rather than relying on standardised test cycles designed for different applications, AI-guided approaches can develop mission-specific protocols that target the exact stress conditions batteries will face in service.
These systems can continuously refine testing strategies based on operational feedback, ensuring experiments focus on the failure modes and operating conditions most relevant to flight safety.
Data-driven insights help identify subtle patterns in battery behaviour that traditional analysis might miss. AI can correlate complex multi-variable datasets to understand how different stress factors interact, providing deeper insights into failure mechanisms and more accurate predictions of real-world performance.
eVTOL battery testing cannot simply adapt automotive methodologies for aviation applications. The consequences of failure are fundamentally different and far more severe. When a car battery fails, the vehicle comes to a stop. When an eVTOL battery fails, the aircraft may fall.
That difference demands testing approaches that reflect the true operational environment and risk profile of flight.
Meeting that standard requires more than just higher discharge rates or thermal stress thresholds. It means moving beyond standardised, static test cycles toward dynamic, mission-specific protocols that replicate real-world flight demands across varying C-rates, temperatures, and state-of-charge conditions.
Conventional lab tests miss critical failure modes like power fade at end-of-discharge, anode plating during high-rate pulses, and cascading thermal events under mixed loads. These are precisely the scenarios where lives are at stake.
AI-guided testing offers a path forward—one that brings the fidelity of real flight into the lab. By learning from actual flight data and identifying the patterns that precede failure, engineers can design tests that expose weaknesses before they show up in the air.
In eVTOL, safety doesn’t come from more testing—it comes from smarter testing, designed to match the stakes of electric aviation.