Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-ion Batteries

by   Kailong Liu, et al.

This paper presents the development of machine learning-enabled data-driven models for effective capacity predictions for lithium-ion batteries under different cyclic conditions. To achieve this, a model structure is first proposed with the considerations of battery ageing tendency and the corresponding operational temperature and depth-of-discharge. Then based on a systematic understanding of covariance functions within the Gaussian process regression, two related data-driven models are developed. Specifically, by modifying the isotropic squared exponential kernel with an automatic relevance determination structure, 'Model A' could extract the highly relevant input features for capacity predictions. Through coupling the Arrhenius law and a polynomial equation into a compositional kernel, 'Model B' is capable of considering the electrochemical and empirical knowledge of battery degradation. The developed models are validated and compared on the Nickel Manganese Cobalt Oxide (NMC) lithium-ion batteries with various cycling patterns. Experimental results demonstrate that the modified Gaussian process regression model considering the battery electrochemical and empirical ageing signature outperforms other counterparts and is able to achieve satisfactory results for both one-step and multi-step predictions. The proposed technique is promising for battery capacity predictions under various cycling cases.



There are no comments yet.


page 1


Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach

A latent function decomposition method is proposed for forecasting the c...

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

Accurate on-board capacity estimation is of critical importance in lithi...

Battery health prediction under generalized conditions using a Gaussian process transition model

Accurately predicting the future health of batteries is necessary to ens...

Gaussian Process Regression for Arctic Coastal Erosion Forecasting

Arctic coastal morphology is governed by multiple factors, many of which...

Data Driven Prediction of Battery Cycle Life Before Capacity Degradation

Ubiquitous use of lithium-ion batteries across multiple industries prese...

GPHQP: Hierarchical Quadratic Programming for Controlling a Gaussian Process Regression Model of Redundant Robot

Accurate kinematic models are essential for effective control of surgica...

Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectors

To date, a large number of experiments are performed to develop a bioche...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.