Support Vector Machines Used to Estimate the Battery State of Charge

By J. C. Álvarez Antón, P. J. García Nieto, C. Blanco Viejo, J. A. Vilán Vilán. IEEE transactions on power electronics, 2013

434 citations recorded by the source corpus.

The aim of this study is to estimate the state of charge (SOC) of a high-capacity lithium iron manganese phosphate (LiFeMnPO4) battery cell from an experimental dataset using a support vector machine (SVM) approach. SVM is a type of learning machine based on statistical learning theory. Many applications require accurate measurement of battery SOC in order to give users an indication of available runtime. It is particularly important for electric vehicles or portable devices. In this paper, the proposed SOC estimator extracts model parameters from battery charging/discharging testing cycles, using cell current, cell voltage, and cell temperature as independent variables. Tests are carried out on a 60 Ah lithium-ion cell with the dynamic stress test cycle to set up the SVM model. The SVM SOC estimator maintains a high level of accuracy, better than 6% over all ranges of operation, whether the battery is charged/discharged at constant current or it is operating in a variable current profile.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/00773cefe8d439b100dafe20727ccaa755ae1de1

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Citations in the corpus

Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine (2016, background): “The radial basis function (RBF) is a popular kernel function in SVM and can accomplish nonlinear mapping [35], [36].”
Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries (2019, methodology): “The commonly used machine learning methods include artificial neural networks [13], fuzzy logic [14], support vector machine [15], and so on.”
Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “In [33], an SVM was applied to estimate the SOC of a 60Ah LFP.”
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries (2018, methodology): “Strategies involving data analytics have also been used in the past; these have relied on conventional machine-learning techniques, such as support vector machines (SVM) [16]–[18] and neural networks [19], [20].”
State of Charge Estimation of Vanadium Redox Flow Battery Based on Sliding Mode Observer and Dynamic Model Including Capacity Fading Factor (2017, positive): “Another category of SOC estimation methods is reliant on computational and intelligence-based strategies, which consist of artificial neural network, fuzzy neural network, and support vector machines [3]–[7].”