Using AI to report on battery-pack performance.

I recently started using chatGPT to help assess the performance of my pack. I used Dr Prius to record data while I drove, then uploaded the resulting spreadsheet file (.csv) into chatGPT.

Incredibly useful - like having an expert at your finger tips when ever required!

Hers a sample of output from one of my chats:


QUOTE

Filtered result — true uphill pull only
(criteria: Current > 30 A and speed increasing, stop phases removed)

Ranking (lowest block most often):

1. Vol#10 — clear outlier
   
   
2. Vol#6
   
   
3. Vol#1
   
   
4. Vol#4
   
   
5. Vol#2 (now secondary)

Key figures

• Peak block delta under true pull: ~0.71 V
  
  
• Weak blocks sag together during torque steps, but Vol#10 consistently leads the drop.
  
  
• This is a textbook high internal resistance module pair, not SOC imbalance.

Interpretation

• Earlier stop–start noise partially masked it.
  
  
• Once filtered properly, the diagnosis sharpens:
  Block 10 is the primary fault, with 6 and 1 as watch items.

What to do next (no dithering):

1. Remove Block 10 first.
   
   
2. Bench test both modules:
   • 2 A discharge, 120 s
     
     
   • Record V at 30 / 60 / 120 s and 5-min recovery
3. Compare against one known-good block (mid-pack).

Decision rule

• If either module in Block 10 fails → replace the pair.
  
  
• Do not reshuffle modules; that just moves the fault.

This hill run has done its job properly. You now have a defensible, data-backed target.

UNQUOTE

All this - and more - before even taking the pack out of the car!!