Estimation of Battery State of Health Using Probabilistic Neural Network

By Ho-Ta Lin, Tsorng-Juu Peter Liang, Shih-Ming Chen. IEEE Transactions on Industrial Informatics, 2013

302 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e4e4a52f8b0dd195cb2aa1843b223165e9916614

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In Automotive Engineering

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Citations in the corpus

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications (2023, background): “6 [112], Using the gating signal that is received from the control circuit as a starting point, the primary goal is to measure current, voltage, and temperature.”
Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling (2016, methodology): “models, such as artificial neural networks [33], [34], fuzzy logic”
Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries (2020, methodology): “[11] covered that probabilistic neural networks (PNN) were adaptive to SOH estimation of LIBs when enough training samples were available.”
Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model (2014, background): “Aside from fuzzy logic, ANNs have gained much popularity for estimation and prediction, specifically for direct SoC estimation [18]–[21].”
Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “Another similar work using a probabilistic distribution based on a Bayesian network (BN) was presented in [71].”
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks (2017, methodology): “In [21], a PNN is proposed to estimate SOH by using battery’s OCV,”
Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis (2018, methodology): “For the black box model-based techniques, artificial neural networks [18,19], support vector machine [20,21], sample entropy [22,23] and sparse bayesian modeling [24] are generally employed to estimate the capacity fade in batteries.”
A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life (2021, methodology): “The data-driven methods analyze LIB life characteristic parameters [16], [17] such as capacity, current, voltage, and impedance by intelligent algorithm models such as neural network [18], support vector machine (SVM) [19], and Bayesian regression [20].”