Proceedings ArticleDOI
Online estimation of state-of-health for lithium ion batteries based on charge curves
Nina Yang,Jing Feng,Quan Sun,Tianyu Liu,Dao Zhong +4 more
- pp 1-8
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TLDR
A novel approach is proposed in this paper to estimate the usable capacity and SOH of lithium ion batteries based on the charge curve, where the time intervals between two voltages and currents during charging are used as the health factors to predict the usable Capacity and the SOH estimation.Abstract:
Usable capacity refers to the maximum capacity in theory that a fully charged battery can release, and is often used as an indicator in state of health (SOH) estimation for lithium ion batteries. The traditional method for measuring usable capacity is mainly based on voltage data in the discharge process with a constant current. However, the discharge current of a lithium ion battery in operation always fluctuates due to load changes, which makes the traditional method difficult for realizing online capacity measurement. To overcome the above problems, a novel approach is proposed in this paper to estimate the usable capacity and SOH of lithium ion batteries based on the charge curve. The time intervals between two voltages and currents during charging are used as the health factors to predict the usable capacity, which is then used to perform the SOH estimation. Experiments are implemented based on data provided by the NASA Ames Prognostics Center of Excellence. Results confirm that the proposed method performs well in online estimation of SOH.read more
Citations
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Journal ArticleDOI
Online capacity estimation of lithium-ion batteries with deep long short-term memory networks
TL;DR: The scope of this work is the development of a data-driven capacity estimation model for cells under real-world working conditions with recurrent neural networks having long short-term memory capability, which achieves a best-case mean absolute percentage error and is extremely robust while handling input noise.
Journal ArticleDOI
The challenge and opportunity of battery lifetime prediction from field data
Valentin Sulzer,Peyman Mohtat,Antti Aitio,Suhak Lee,Yen T. Yeh,Frank Steinbacher,Muhammad Umer Khan,Jang-Woo Lee,Jason B. Siegel,Anna G. Stefanopoulou,David A. Howey +10 more
TL;DR: A range of techniques for estimating lifetime from lab and field data are explored and it is suggested that combining machine learning approaches with physical models is a promising method, enabling inference of battery life from noisy data, assessment of second-life condition, and extrapolation to future usage conditions.
Journal ArticleDOI
Active Cell Equalization Topologies Analysis for Battery Packs: A Systematic Review
TL;DR: A review of the state-of-the-art active battery cell equalization methods is conducted, where it is classified as adjacent- based, nonadjacent-based, direct cell-cell, and mixed topologies to provide a comprehensive way to analyze and compare the existing active cell balancing methods’ performance.
Journal ArticleDOI
An online state of health estimation technique for lithium-ion battery using artificial neural network and linear interpolation
Yi-Feng Luo,Ken-Yueh Lu +1 more
TL;DR: In this paper , Li et al. proposed a SOH estimation method that can predict the battery current SOH when the battery is fully charged, and the result has been validated by another aging battery and it shows that with the best performance neural network, the maximum relative error of the estimated SOH is only 1.31 %.
Patent
Method for predicting health state of power battery
Li Dinggen,Wu Kuan +1 more
TL;DR: In this article, the authors proposed a method for predicting the state of health of a power battery. But the method is limited to the case of a single battery, and it is not suitable for a large number of batteries.
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