MDPI - Publisher of Open Access Journals

15 pages, 4641 KB

Open AccessEditor’s ChoiceArticle

Investigation of the Suitability of the DTV Method for the Online SoH Estimation of NMC Lithium-Ion Cells in Battery Management Systems

by Jan Neunzling, Philipp Hainke, Hanno Winter, David Henriques, Matthias Fleckenstein and Torsten Markus

Batteries 2025, 11(1), 25; https://doi.org/10.3390/batteries11010025 - 13 Jan 2025

Cited by 3 | Viewed by 1736

Abstract

Investigating the temperature behavior of lithium-ion battery cells has become an important part of today’s research and development. The main reason for this is that the temperature profile of a battery cell changes during aging. By using Differential Thermal Voltammetry (DTV), new possibilities are opened up, especially since this diagnostic method is designed to work in operando by only requiring voltage and temperature readings. In this study, a batch of NMC-21700 cells were aged in calendar and cyclic manners. After a specified aging cycle was complete, a check-up measurement was performed. During this time, the cycler collected the electrical measuring values, while a negative temperature coefficient thermistor, which was located on the cell, was used to record the temperature fluctuations. The data were then evaluated by using the DTV analysis technique. By comparing the characteristic points of DTV, correlations between the changing curve characteristics and the capacity loss, and therefore the aging of the respective cell, were established. Based on these results, a simple model suitable for online State of Health (SoH) is derived and validated, showing an estimation accuracy of

1.1

%. Full article

(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)

► Show Figures

Figure 1

18 pages, 8304 KB

Open AccessArticle

A Deep Learning Approach for State-of-Health Estimation of Lithium-Ion Batteries Based on a Multi-Feature and Attention Mechanism Collaboration

by Bosong Zou, Mengyu Xiong, Huijie Wang, Wenlong Ding, Pengchang Jiang, Wei Hua, Yong Zhang, Lisheng Zhang, Wentao Wang and Rui Tan

Batteries 2023, 9(6), 329; https://doi.org/10.3390/batteries9060329 - 19 Jun 2023

Cited by 24 | Viewed by 4917

Abstract

Safety issues are one of the main limitations for further application of lithium-ion batteries, and battery degradation is an important causative factor. However, current state-of-health (SOH) estimation methods are mostly developed for a single feature and a single operating condition as well as a single battery material system, which consequently makes it difficult to guarantee robustness and generalization. This paper proposes a data-driven and multi-feature collaborative SOH estimation method based on equal voltage interval discharge time, incremental capacity (IC) and differential thermal voltammetry (DTV) analysis for feature extraction. The deep learning model is constructed based on bi-directional long short-term memory (Bi-LSTM) with the addition of attention mechanism (AM) to focus on the important parts of the features. The proposed method is validated based on a NASA dataset and Oxford University dataset, and the results show that the proposed method has high accuracy and strong robustness. The estimated root mean squared error (RMSE) are below 0.7% and 0.3%, respectively. Compared to single features, the collaboration between multiple features and AM resulted in a 25% error improvement, and the capacity rebound is well captured. The proposed method has the potential to be applied online in an end-cloud collaboration system. Full article

► Show Figures

Graphical abstract

21 pages, 5145 KB

Open AccessArticle

Lithium-Ion Battery State of Health Estimation with Multi-Feature Collaborative Analysis and Deep Learning Method

by Xianbin Yang, Bin Ma, Haicheng Xie, Wentao Wang, Bosong Zou, Fengwei Liang, Xiao Hua, Xinhua Liu and Siyan Chen

Batteries 2023, 9(2), 120; https://doi.org/10.3390/batteries9020120 - 8 Feb 2023

Cited by 27 | Viewed by 7433

Abstract

The accurate estimation of the battery state of health (SOH) is crucial for the dependability and safety of battery management systems (BMS). The generality of existing SOH estimation methods is limited as they tend to primarily consider information from single-source features. Therefore, a novel method for integrating multi-feature collaborative analysis with deep learning-based approaches is proposed in this research. First, several battery degradation features are obtained through differential thermal voltammetry (DTV) analysis, singular value decomposition (SVD), incremental capacity analysis (ICA), and terminal voltage characteristic (TVC) analysis. The features highly related to SOH are selected as inputs for the deep learning model based on the results of a Pearson correlation analysis. The SOH estimation is achieved by developing a deep learning framework cored by long short-term memory (LSTM) neural network (NN), which integrates multi-source features as an input. A suggested method is validated using NASA and Oxford Battery Degradation datasets. The results demonstrate that the presented model provides great SOH estimation accuracy and generality, where the maximum root mean square error (RMSE) is less than 1%. Based on a cloud computing platform, the proposed method can be applied to provide a real-time prediction of battery health, with the potential to enhance battery full lifespan management. Full article

(This article belongs to the Special Issue Feature Papers to Celebrate the First Impact Factor of Batteries)

► Show Figures

Figure 1

20 pages, 4319 KB

Open AccessArticle

Early Prediction of the Health Conditions for Battery Cathodes Assisted by the Fusion of Feature Signal Analysis and Deep-Learning Techniques

by Wentao Wang, Lisheng Zhang, Hanqing Yu, Xianbin Yang, Teng Zhang, Siyan Chen, Fengwei Liang, Huizhi Wang, Xuekun Lu, Shichun Yang and Xinhua Liu

Batteries 2022, 8(10), 151; https://doi.org/10.3390/batteries8100151 - 1 Oct 2022

Cited by 12 | Viewed by 3948

Abstract

With rapid development of clean energy vehicles, the health diagnosis and prognosis of lithium batteries remain challenging for practical applications. Accurate state-of-health (SOH) and remaining useful life (RUL) estimation provides crucial information for improving the safety, reliability and longevity of batteries. In this paper, a fusion of deep-learning model and feature signal analysis methods are proposed to realize accurate and fast estimation of the health conditions for battery cathodes. Specifically, the long short-term memory (LSTM) network and differential thermal voltammetry (DTV) are utilized to verify our fusion method. Firstly, the DTV feature signal analysis is executed based on battery charging and discharging data, based on which useful feature variables are extracted with Pearson correlation analysis. Next, the deep-learning model is constructed and trained with the LSTM as the core based on timeseries datasets constructed with features. Finally, the validation and error analysis of proposed model are provided, showing a max mean absolute error of 0.6%. The proposed method enables highly accurate models for SOH and RUL estimation that can be potentially deployed on cloud-end for offline battery degradation tracking. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

► Show Figures

Graphical abstract

15 pages, 602 KB

Open AccessArticle

Study on Adaptive Cycle Life Extension Method of Li-Ion Battery Based on Differential Thermal Voltammetry Parameter Decoupling

by Zhixuan Wu, Guorong Zhu, Qian Wang, Shengjie Yang, Jing V. Wang and Jianqiang Kang

Energies 2021, 14(19), 6239; https://doi.org/10.3390/en14196239 - 30 Sep 2021

Cited by 2 | Viewed by 2872

Abstract

Battery aging leads to reduction in a battery’s cycle life, which restricts the development of energy storage technology. At present, the state of health (SOH) assessment technology, which is used to indicate the battery cycle life, has been widely studied. This paper tries to find a way to adjust the battery management system adaptively in order to prolong the battery cycle life with the change of SOH. In this paper, an improved Galvanostatic Intermittent Titration Technique (GITT) method is proposed to decouple the terminal voltage into overpotential (induced by total internal resistance) and stoichiometric drift (caused by battery aging, indicated by OCV). Based on improved GITT, the open circuit voltage-temperature change (OCV-dT/dV) characteristics of SOH are described more accurately. With such an accurate description of SOH change, the adaptive method to change the discharge and charge cut-off voltage is obtained, whose application can prolong battery cycle life. Experiments verify that, in the middle of a battery’s life-cycle, the adaptive method to change the discharge and charge cut-off voltage can effectively improve the cycle life of the battery. This method can be applied during the period of preventive maintenance in battery storage systems. Full article

► Show Figures

Figure 1

Search Results (5)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Saved Queries

Search Filter Reset All

Years

Feature Papers

Subjects

Journals

Article Types

Countries / Regions

Search Results (5)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI