Machine learning bridging battery field data and laboratory data

TL;DR

This paper introduces a machine learning method that uses minimal field data to accurately predict laboratory battery data, enabling laboratory-based diagnostic techniques to be applied directly to field batteries, thus improving accuracy and reducing costs.

Contribution

The paper presents a novel machine learning approach that bridges field and laboratory battery data using only two impedance measurements, expanding the applicability of lab data-driven methods to real-world batteries.

Findings

Achieved mean absolute percentage errors of 0.85%, 4.72%, and 2.69% for impedance, charge, and discharge curves.

Method requires only two field impedance measurements, reducing data collection costs.

Demonstrated effectiveness on two open-source datasets with 249 NMC cells.

Abstract

Aiming at the dilemma that most laboratory data-driven diagnostic and prognostic methods cannot be applied to field batteries in passenger cars and energy storage systems, this paper proposes a method to bridge field data and laboratory data using machine learning. Only two field real impedances corresponding to a medium frequency and a high frequency are needed to predict laboratory real impedance curve, laboratory charge/discharge curve, and laboratory relaxation curve. Based on the predicted laboratory data, laboratory data-driven methods can be used for field battery diagnosis and prognosis. Compared with the field data-driven methods based on massive historical field data, the proposed method has the advantages of higher accuracy, lower cost, faster speed, readily available, and no use of private data. The proposed method is tested using two open-source datasets containing 249 NMC cells. For a test set containing 76 cells, the mean absolute percentage errors of laboratory real impedance curve, charge curve, and discharge curve prediction results are 0.85%, 4.72%, and 2.69%, respectively. This work fills the gap between laboratory data-driven diagnostic and prognostic methods and field battery applications, making all laboratory data-driven methods applicable to field battery diagnosis and prognosis. Furthermore, this work overturns the fixed path of developing field battery diagnostic and prognostic methods based on massive field historical data, opening up new research and breakthrough directions for field battery diagnosis and prognosis.