Cycle Life Worksheet

What You're Actually Trying to Do

A Cycle Life Worksheet is a spreadsheet-based tracking tool that models how many charge-discharge cycles a battery cell or pack will endure before reaching a defined capacity degradation threshold. Most people find it when they're trying to size an energy storage system or validate a supplier's cycle life claims. The tool takes raw test data and projects it forward, which sounds simple enough until you actually populate one. I build these from scratch most of the time rather than relying on vendor templates. A properly structured worksheet has five columns that matter: cycle number, capacity retention percentage, cumulative throughput in amp-hours, temperature at end-of-discharge, and the DOD applied that cycle. Everything else is noise if you don't have those four things locked down first. You can grab a clean template from the shared drive folder that I maintain for the team. The first step is inputting your test data or projected usage profile. If you're working with accelerated cycling data at elevated temperatures, you'll want to normalise it back to your target operating condition using an Arrhenius correction factor. Most people skip this and then wonder why their projected cycle count is double what the field data shows. I learned that the hard way on a 200kWh residential storage project last year.

Once your baseline data is in, you apply a degradation model. The two most common are the linear-per-cycle model and the exponential model. Linear works fine for the first 500 to 800 cycles on LFP chemistry. After that, degradation accelerates and the linear fit will overestimate your remaining life by 15 to 20 percent. I use an exponential decay curve fitted to the last third of the available test data for anything past that point. Here's the workaround I ended up using on that residential project: the vendor provided cycle life data at 25C ambient temperature and 80 percent DOD, but our installation would regularly hit 35C and run closer to 90 percent DOD in summer. I ran a sensitivity analysis by adjusting both variables in the worksheet simultaneously. The combined effect cut the projected cycles at 80 percent remaining capacity from 6,000 down to roughly 3,200. That number change completely flipped the economics of the project. Without running those adjustments in the Cycle Life Worksheet, we would have proposed a system that needed replacement three years earlier than budgeted.

Where These Worksheets Break Down

They fail when you feed them garbage. Not sloppy garbage. Accurate-looking garbage. A common issue I see is people entering cycle count without specifying the DOD. A cell cycled at 10 percent DOD will last orders of magnitude longer than the same cell at 80 percent DOD. If your worksheet doesn't cross-reference DOD against cycle life, every output number is unreliable. I started adding a mandatory DOD column with validation that flags any cycle entry missing a corresponding depth value. It takes three extra minutes to set up and saves hours of rework later. Another limitation is that these models assume steady-state cycling. Real-world use involves partial cycles, rest periods, varying charge rates, and temperature swings. None of that sits cleanly in a standard worksheet. When I need to account for real-world complexity, I layer a Monte Carlo simulation on top of the base Cycle Life Worksheet results. It adds about 20 percent more time to build but gives you a confidence interval instead of a single point estimate. For customer-facing reports, that difference between a point estimate and a range is usually the difference between a signed contract and a skeptical email. There's also the issue of calendar aging versus cycle aging. A worksheet focused purely on cycle life will understate degradation for systems that sit idle between cycles. In one storage application, calendar aging contributed roughly 40 percent of total capacity loss over a ten-year period while cycle aging made up the other 60. If your use case involves long idle periods, you need a separate calendar degradation curve in the same file. Merging the two models into one sheet is tedious but not difficult. The key is keeping the inputs clearly separated so you can swap one out without breaking the other.

Practical Tips That Actually Matter

Always include a row for the 80 percent remaining capacity threshold. That's the industry standard for end-of-life, but it's surprising how many worksheets I've seen missing it entirely. People default to 70 percent or 90 percent without a reason. If you're comparing against manufacturer specs, make sure both sides are using the same threshold or your comparison means nothing. Build in a column for charge C-rate. Fast charging at 2C versus 0.5C can reduce cycle life by 30 to 50 percent depending on chemistry, and that factor rarely shows up in basic worksheets. I add it as a multiplier that adjusts the degradation rate per cycle. It makes the model slightly more complex but only by one additional parameter. Temperature deserves its own sensitivity section. I usually create a small lookup table inside the worksheet that maps ambient temperature ranges to cycle life correction factors based on the specific cell chemistry. LFP is relatively tolerant up to about 40C. NMC starts degrading noticeably past 30C. Keeping that mapping internal to the file means you don't have to remember the specifics every time you build a new project estimate.

If you're presenting these numbers to anyone who signs checks, include a simple chart showing projected capacity retention over time with shaded bands for best case and worst case. Even a rough band based on your sensitivity analysis carries more weight than a single line. It communicates that you understand uncertainty without needing a paragraph of explanation.