Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

Wei, Xiong; Yi-min, MO; Yan, Cong

doi:10.1109/access.2020.3026552

Cited by 56 publications

(22 citation statements)

References 40 publications

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“…The data driven based methods extract and model the ageing patterns or characteristics from the historical data without needing to know the underlying ageing mechanisms of the battery [4][5][34][35][36][37][38][39][40][41][42][43][44][45][46]. Because of the recent advances in deep learning algorithms and computation capability of computers or microcomputers, the data-driven based methods are widely utilized to tackle and solve complex and challenging tasks.…”

Section: B Data Driven Based Methodsmentioning

confidence: 99%

“…Ref. [34] utilizes a weighted least squares support vector machine (SVM) to estimate SOH of second-use Lithium-ion batteries. Ref.…”

Section: B Data Driven Based Methodsmentioning

confidence: 99%

“…For data driven based methods, the quality of data utilized to train the models affects the estimation performance [4]. In order to estimate SOH of battery cells under different operation conditions, the data utilized to train the models needs to cover the corresponding battery usage scenarios that the models plan to make the predictions under [34][35][36][37][38][39][40][41][42][43][44][45][46].…”

Section: B Data Driven Based Methodsmentioning

confidence: 99%

“…This is in order to investigate and explore the characteristics and ageing behavior patterns of battery when it is highly deteriorated and evaluate the SOH estimation performance under low SOH values. There are applications where battery is used even after SOH is below 70% ~ 80%, such as second-use battery which is an emerging topic [34,[49][50].…”

Section: Battery Ageing Protocol and Testing Proceduresmentioning

confidence: 99%

See 3 more Smart Citations

Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

Xia

Qahouq

2021

IEEE Access

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This paper presents a study on Lithium-ion battery aging behaviors/patterns and related Stateof-Health (SOH) indicators before presenting the development of data-driven based SOH estimators. Battery charge/discharge cycling experiments are conducted in order to obtain needed data for this work. The battery ageing behavior patterns until the battery cell reaches highly deteriorated health conditions are investigated and characterized in this paper by analyzing the aggregated battery ageing data. The observed battery ageing behavior patterns include: (1) the rate at which the battery voltage decreases during discharging increases as the battery ages, (2) the speed at which the battery terminal voltage increases during Constant Current (CC) charging increases as the battery's health deteriorates, (3) the time period for CC charging operation decreases as the battery ages, (4) the rate at which the battery current decreases during Constant Voltage (CV) charging increases as the battery ages, and (5) the speed at which the battery temperature drops during CV charging increases as the battery ages. Corresponding SOH indicators are developed to quantify these battery ageing behavior patterns for the development of SOH estimators. Deep Neural Network (DNN) is utilized to extract and model the nonlinear and complex correlation between the defined SOH indicators and SOH values of the Lithium-ion battery. Multiple DNN-based SOH estimators are developed in this paper. The SOH estimation results from different DNN-based SOH estimators indicate that the diversity of SOH indicators used for the development of SOH estimator can substantially improve the estimation performance.

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Section: B Data Driven Based Methodsmentioning

confidence: 99%

“…Ref. [34] utilizes a weighted least squares support vector machine (SVM) to estimate SOH of second-use Lithium-ion batteries. Ref.…”

Section: B Data Driven Based Methodsmentioning

confidence: 99%

Section: B Data Driven Based Methodsmentioning

confidence: 99%

Section: Battery Ageing Protocol and Testing Proceduresmentioning

confidence: 99%

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Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

Xia

Qahouq

2021

IEEE Access

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“…For analysis of the results, we used an intelligent system methods (Cuevas, Díaz, Camarena, 2021), namely multiple regression (Srinivasan, Murugasan, 2021) and support vector machine (Xiong, Mo, Yan, 2021).…”

Section: Materials Preparation and Methodsmentioning

confidence: 99%

Modeling surface roughness of point robot laser hardening, with emphasis on the surface

Babič

2021

Politehnika (Online)

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The topic of Machine Learning is so popular that it is not only the future trend, but also the money tide. Machine learning technique and intelligent system methods are very popular in mechanical engineering. Robot laser surface hardening is one of the most promising techniques for surface modification of the microstructure of a material to improve wear and corrosion resistance. For predicting the surface roughness of the hardened specimens, the support vector machine and multiple regression is used. The aim of this paper is to present modeling roughness of point robot laser hardened specimens with different parameters of robot laser cell.

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Multiscale modeling for enhanced battery health analysis: Pathways to longevity

Yang,

Zhang,

Wang

et al. 2024

Carbon Neutralization

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The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.

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Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

Cited by 56 publications

References 40 publications

Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

Modeling surface roughness of point robot laser hardening, with emphasis on the surface

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

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