Empirical Wavelet Transform and Deep-Learning Neural Network

Empirical Wavelet Transform and Deep-Learning Neural Network for State of Health Estimation of Lithium-ion Batteries

Authors: Ma’d El-Dalahmeh, Maher Al-Greer, School of Computing Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
Centre of Smart Energy and Smart Grid

Abstract

This work presents a state of health (SOH) estimation of lithium-ion batteries based on advanced signal processing technique called empirical wavelet transform (EWT) and deep-learning neural network. The proposed approach first performs features extraction using WT for health degradation, and then the extracted features are used to train a deep neural network to estimate the state of health in the battery. The proposed approach is validated using NASA and Stanford University experimental datasets.

Introduction and Motivation

  • LIBs are recognised as a key future form of technology for renewable energy and electric vehicles (EVs) due to their high power and energy densities, low maintenance cost, long lifetime, and low-self discharge rate.
  • The aim of this project is to develop a novel accurate SOH estimation algorithm of lithium ion batteries based on WT and deep-learning neural network.

Empirical Wavelet Transform and Neural Network

Validation Test and Results

Conclusion

This work proposed a new generalized approach for the capacity estimation of lithium-ion batteries. The proposed method focuses on the lithium-ion battery capacity degradation assessment using an adaptive empirical wavelet transform (EWT) technique and deep learning neural network.

Empirical Wavelet Transform and Deep-Learning Neural Network for State of Health Estimation of Lithium-ion Batteries

Authors: Ma’d El-Dalahmeh, Maher Al-Greer, School of Computing Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
Centre of Smart Energy and Smart Grid


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