Lithium-ion batteries soh estimation, based on support-vector regression and a feature-based approach

Abstract

Lithium-Ion batteries, have become enormously used in many systems and applications, and are the most widespread energy storage system. Optimizing the usage of batterie is therefore very important to increase the safety of systems like electric vehicles or portable devices, to reduce economic loss in industrial environments, and to increase their availability. An accurate State of Health (SoH) estimation is important since it allows us to know battery conditions and make an appropriate use of it, and it improves the accuracy of other diagnostic measures, like State of Charge (SoC). In this paper, an approach for SoH estimation is proposed, based on Support Vector Regression machine learning algorithm and a smart feature extraction process, finding a good trade-off between applicability, light computation effort, and accuracy of results. Features selection and parameters tuning are discussed, and performances are measured on a dataset from the Prognostics Center of Excellence at NASA, considering 3 batteries of the dataset.

Comparing Machine Learning Strategies for SoH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach

“…In [34], the authors proposed an approach for SoH estimation based on SVR and a feature extraction procedure. In this paper, SVR is compared to other ML approaches, including multiple linear and polynomial regression and random forest.…”

State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression

“…An SVR strategy is presented in [19] based on curves of battery voltage as a function of charging capacity (V-Q). In [20,21], an SVR strategy based on partial voltage charging curves is proposed, while in [22], a comparison between linear regression, SVR, and random forest methods is provided.…”