High-Fidelity State-of-Charge Estimation of Li-Ion Batteries Using Machine Learning

TL;DR

This paper introduces a pulse-injection method combined with neural networks to improve the accuracy of state-of-charge estimation in Li-ion batteries, achieving less than 2% error.

Contribution

It presents a novel pulse-based augmentation technique and neural network model for high-fidelity SoC estimation, integrating seamlessly with existing hardware.

Findings

Less than 2% SoC estimation error achieved

Pulse amplitude sensitivity analysis conducted via simulation

Neural network trained with up to 1 million epochs

Abstract

This paper proposes a way to augment the existing machine learning algorithm applied to state-of-charge estimation by introducing a form of pulse injection to the running battery cells. It is believed that the information contained in the pulse responses can be interpreted by a machine learning algorithm whereas other techniques are difficult to decode due to the nonlinearity. The sensitivity analysis of the amplitude of the current pulse is given through simulation, allowing the researchers to select the appropriate current level with respect to the desired accuracy improvement. A multi-layer feedforward neural networks is trained to acquire the nonlinear relationship between the pulse train and the ground-truth SoC. The experimental data is trained and the results are shown to be promising with less than 2\% SoC estimation error using layer sizes in the range of 10 - 10,000 trained in 0 - 1 million epochs. The testing procedure specifically designed for the proposed technique is explained and provided. The implementation of the proposed strategy is also discussed. The detailed system layout to perform the augmented SoC estimation integrated in the existing active balancing hardware has also been given.