🔋Lithium-Ion Battery Remaining Useful Life (RUL) Predictor

Fill in the battery details below to check how many cycles it has left.

📌 About This Project: Imagine trying to figure out how healthy a rechargeable battery is without tearing it open. This project uses machine learning to do exactly that! By tracking simple time and voltage details from just one routine charge and discharge cycle, our system builds a digital "fingerprint" of the battery to predict exactly how many more life cycles it has left before wearing out.

🔍 What the Inputs Mean:

  • Discharge Time: Total time (in seconds) the battery lasted under use before running empty.
  • Decrement Range: Time taken for the battery voltage to drop through its busiest working range (3.6V down to 3.4V). Weak cells drop much faster.
  • Max. Voltage Discharge: The peak voltage measured right at the beginning of the discharge cycle.
  • Min. Voltage Charge: The lowest voltage measured right before the battery was plugged into a charger.
  • Time at 4.15V: Seconds the battery spent resting at a high, near-full voltage state.
  • Time Constant Current: Duration of the high-speed charging mode before the charger slowed down to top it off safely.
  • Total Charging Time: The total time (in seconds) required to charge the cell back to 100%.
🚀 Let's predict how long your battery stays alive!
Must be greater than 0 (Expected range: 1000.0 to 4000.0)
Must be 0 or greater (Expected range: 200.0 to 2500.0)
Accepts values strictly between 2.0V and 5.0V
Accepts values strictly between 1.0V and 5.0V
Must be 0 or greater (Expected range: 0.0 to 500.0)
Must be 0 or greater (Expected range: 100.0 to 2000.0)
Must be greater than 0 (Expected range: 2000.0 to 10000.0)
Predicted Life: -- cycles
95% Confidence Range: --
Margin of Error: --
Tree Deviation Error: --
Disclaimer: This application relies on statistical machine learning patterns for estimation purposes. Due to the intrinsic nature of inductive modeling and variations in real-world environmental factors (such as ambient temperature and cell internal resistance), predictions may deviate from exact physical degradation metrics. Outputs should be treated as probabilistic indicators rather than absolute hardware guarantees.