Honorable Mention Winnerʼs Article: High-Fidelity State-of-Charge Estimation of Li-Ion Batteries using Machine Learning

Erstellt von Matthias PREINDL (Winner)* et.al. | |   Technical Reports

Matthias PREINDL (Winner)*, Weizhong WANG, Nicholas W. BRADY, Chenyao LIAO, Youssef A. FAHMY, Ephrem CHEMALI, Alan C. WEST

*Columbia University in the City of New York

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.

Key words: Aging Test, Battery Management Systems, Machine Learning, Neural Network, State-of-Charge Estimation

Machine learning algorithm implemented in real battery system
Machine learning algorithm implemented in real battery system