Remaining Discharge Energy Prediction for Lithium-Ion Batteries Over Broad Current Ranges: A Machine Learning Approach

TL;DR

This paper introduces a machine learning-based method to accurately predict the remaining discharge energy of lithium-ion batteries across a wide range of C-rates, integrating physics-based modeling and data-driven techniques.

Contribution

It presents the first comprehensive approach combining physics-informed modeling with machine learning to predict battery energy over broad current ranges.

Findings

Predicts remaining energy with less than 3% error across various C-rates.

Develops a physics-ML integrated dynamic model of voltage and temperature.

Demonstrates applicability to different battery chemistries like NCA and LFP.

Abstract

Lithium-ion batteries have found their way into myriad sectors of industry to drive electrification, decarbonization, and sustainability. A crucial aspect in ensuring their safe and optimal performance is monitoring their energy levels. In this paper, we present the first study on predicting the remaining energy of a battery cell undergoing discharge over wide current ranges from low to high C-rates. The complexity of the challenge arises from the cell's C-rate-dependent energy availability as well as its intricate electro-thermal dynamics especially at high C-rates. To address this, we introduce a new definition of remaining discharge energy and then undertake a systematic effort in harnessing the power of machine learning to enable its prediction. Our effort includes two parts in cascade. First, we develop an accurate dynamic model based on integration of physics with machine learning to capture a battery's voltage and temperature behaviors. Second, based on the model, we propose a machine learning approach to predict the remaining discharge energy under arbitrary C-rates and pre-specified cut-off limits in voltage and temperature. The experimental validation shows that the proposed approach can predict the remaining discharge energy with a relative error of less than 3% when the current varies between 0~8 C for an NCA cell and 0~15 C for an LFP cell. The approach, by design, is amenable to training and computation.