How Do You Catch Battery Degradation Before It Becomes a Failure?
Nominal
A battery test deviates at hour 5. Nobody catches it until days later. By then a week of lab time is gone for a test that was never going to pass. That's the shape of the problem for engineers running cycle-life and characterization campaigns on packs.
At hour 5 of a 100-cycle life test, a resistance reading drifts half a percent off its expected curve. Nobody catches it in real time. The test runs another three days before the trend is obvious, by which point the pack has failed, the report is due, and a week of engineering time is gone on a result that was effectively decided on day one.
That's the shape of the problem for engineers running cycle-life and characterization campaigns on packs headed into an electric vehicle, battery energy storage system, aircraft, or drone. The chemistry changes with the application. The job underneath it doesn't: run long campaigns, catch the failure early enough for it to matter, and get from raw data to a decision fast enough to keep the program on schedule.
The job, underneath the chemistry
Battery testing runs on long, repetitive campaigns. Cycle life testing alone can mean 100+ charge-discharge cycles stretched over days or weeks. Layer in performance characterization, temperature sweeps, aging characterization, different BMS operating modes, and the data layer multiplies fast. When something does go wrong mid-campaign, forensic and root cause analysis has to move quickly. Every day spent chasing an anomaly holds up whatever comes after it.
What actually eats the time is where the data lives once the test starts running. Cycler output, BMS communication logs, CAN, External DAQ readings for temperature and current, environmental chamber data often live in separate areas. Reconstructing a coherent picture of what a pack did over the last 50 cycles usually means stitching together exports from different data sources before post-test analysis can start.
Where the time actually goes
Let’s use performance characterization as an example case study. A hybrid pulse power characterization (HPPC) test is supposed to answer a fairly specific question: how does DC internal resistance change with state of charge and temperature. Getting there means aligning cycler commands with BMS response and external truth data on one timeline first, then deriving the resistance curves from that alignment. When the alignment step is manual, the actual engineering work, tuning BMS configuration off the resulting curve, doesn't start until the data wrangling is done.
Cycle life testing runs into a related problem. A single cycle rarely tells you much about degradation on its own. The truth only shows up when cycle 1 sits next to cycle 50 on the same plot. Aligning runs to a common start time turns what would otherwise be a multi-dataset scrubbing exercise into a single overlay, and running incremental capacity analysis on top of that gives a clearer read on where and how a cell is degrading, without reconstructing the comparison by hand every time.
Forensic and root cause analysis is where disconnected data costs the most. An anomaly in one cell has to get checked against readings from multiple sensors to tell whether it's a real physical fault across every cell or just noise. That analysis can’t happen until logs from every run are pulled from separate systems. The longer that takes, the more a program waits to find out whether the anomaly is a reason to pause, or keep the test campaign going.
How Nominal helps
An eVTOL developer cut battery anomaly triage from days to minutes and saved 60% of battery-test engineering time. A defense battery manufacturer ramped production 3 to 10 units per week with Nominal as its test data source of truth.
Nominal allows engineers to accelerate their battery test cadence across the entire campaign lifecycle.
Centralize your battery data: Ingest and organize cycler output, BMS communication logs (CAN or proprietary), external DAQ readings, and environmental chamber data into one repository, all time-aligned and searchable by cell, pack, or test campaign. Query and filter across every test and asset to find exactly the run, cycle, or anomaly you're looking for, without digging through separate exports.
Store your data on Nominal, AWS or on premise. We can support your data, regardless of CUI or classification level.
Monitor and analyze battery lifecycles: Create a unified profile for every cell, module, or pack, capturing chemistry, BMS version, and test history in one place.
Look back across the full history of a pack's performance, cycle 1 next to cycle 200, to track degradation over time instead of comparing one test in isolation. Replace static spreadsheets with a dynamic, events-driven view of how a pack has aged.
Turn results into reports: Produce detailed reports for internal review or qualification sign-off in a few clicks, with multi-user editing and version control built in.
Embed live charts and tables straight from your analysis, annotate findings, and export to PDF without rebuilding the report from scratch each time. Reports can be shared to the responsible engineer or anyone in the organization by tagging them or sending the link.
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