Hey, good question! It’s definitely a big oversimplification. A battery’s voltage does give a hint about its charge, but it’s heavily influenced by temperature, load, internal resistance, and the chemistry itself. For a freshly charged Li‑ion cell you’ll see a high voltage, but under load it can sag, and a depleted cell can still hold a surprisingly high voltage if it’s at a high temperature or has a high internal resistance. So, while voltage is a useful quick check, it’s not a reliable stand‑alone indicator of state of charge. If you want accuracy, you need a proper state‑of‑charge estimator that factors in current, time, temperature, and the battery’s discharge curve.

Absolutely, the trick is to treat it like a little detective. I usually start with a simple Coulomb counting model, because it’s the easiest to implement—just integrate the current over time, subtract the charge that’s been lost to internal resistance. Then I layer on a correction factor from the temperature sensor; lithium chemistries drop about 2–3 mV per degree, so a quick lookup table does the trick. The real magic comes from the discharge curve: I keep a small look‑up table of voltage vs. state of charge for the exact battery you’re using—usually just a few hundred points—and interpolate. If you want to get fancy, run a Kalman filter that fuses all of those inputs; it’s surprisingly stable and keeps the error under a couple percent even if the load swings wildly. And hey, if you’re feeling adventurous, throw in a tiny bit of machine learning—train a neural net on your battery’s behavior over time, and it can catch those subtle drift patterns that a plain math model misses. But remember, the simpler the better for most hobby projects—over‑engineering can turn your estimator into a black box that even you can’t debug.

For a 100 Wh pack you’re looking at about 200–300 points in the lookup table if you want a 1 % resolution. That’s just a few kilobytes of flash, so it’s trivial to store in an EEPROM or even on the MCU’s RAM. You can compress it further by using a spline or piecewise linear interpolation, but most hobbyists keep the raw table for absolute transparency. When you start pulling in a tiny neural net, the trick is to keep it as a black‑box layer that simply maps the same inputs you already have—current, temperature, voltage—and outputs a correction factor. Store the weights in a human‑readable format (like a CSV) and version‑control them. Make sure you log the input data you used for training and the resulting prediction error so you can audit it later. In short: keep the core model simple, document every change, and never hide the training data. That way the system stays traceable and you can debug it step by step.