Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries

TL;DR

This paper introduces GP-ICE, a data-driven method using Gaussian Process regression to estimate lithium-ion battery capacity in real-time from voltage data, avoiding complex data interpretation and noise amplification.

Contribution

The novel GP-ICE technique estimates battery capacity directly from voltage measurements without relying on IC/DV curve interpretation, improving robustness and simplicity.

Findings

Achieves 2-3% RMSE in capacity estimation within 10 seconds of data.

Applicable to multiple datasets with different cell counts.

Does not require voltage peaks in IC/DV curves.

Abstract

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells respectively. In each case, within certain voltage ranges, as little as 10 seconds of galvanostatic operation enables capacity estimates with approximately 2-3% RMSE.