Boosting battery state of health estimation based on self-supervised learning

Che, Yunhong; Zheng, Yusheng; Sui, Xin; Teodorescu, Remus

doi:10.1016/j.jechem.2023.05.034

Cited by 27 publications

(5 citation statements)

References 28 publications

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“…However, the charging rate is much larger than the discharging rate in most cases, which can induce a quicker degradation of a battery's cycling life in the charging process than in the discharging process 45–48 . An overall consideration of both charging and discharging information is supposed to achieve a high‐accuracy prediction of battery SOH and unveil the underlying degradation mechanisms 49–52 . Finally, the temperature control also impacts the electrochemical polarization and then leads to the degradation trajectory fluctuation in batteries 53 .…”

Section: Resultsmentioning

confidence: 99%

“…[45][46][47][48] An overall consideration of both charging and discharging information is supposed to achieve a high-accuracy prediction of battery SOH and unveil the underlying degradation mechanisms. [49][50][51][52] Finally, the temperature control also impacts the electrochemical polarization and then leads to the degradation trajectory fluctuation in batteries. 53 As such temperature-related descriptors identically serve as an important indicator to identify the degradation mode and then perform prediction tasks against diverse modes.…”

Section: Resultsmentioning

confidence: 99%

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A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries

Han,

Gao,

Chen

et al. 2024

InfoMat

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Accurately forecasting the nonlinear degradation of lithium‐ion batteries (LIBs) using early‐cycle data can obviously shorten the battery test time, which accelerates battery optimization and production. In this work, a self‐adaptive long short‐term memory (SA‐LSTM) method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data. Specifically, two features were extracted from discharge voltage curves by a time‐series‐based approach and forecasted to further cycles using SA‐LSTM model. The as‐obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model. The proposed method achieved an average online prediction error of 6.00% and 6.74% for discharge capacity and end of life, respectively, when using the early‐cycle discharge information until 90% capacity retention. Furthermore, the importance of temperature control was highlighted by correlating the features with the average temperature in each cycle. This work develops a self‐adaptive data‐driven method to accurately predict the cycling life of LIBs, and unveils the underlying degradation mechanism and the importance of controlling environmental temperature.image

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries

Han,

Gao,

Chen

et al. 2024

InfoMat

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“…The studies by Hannan et al [32], Che et al [33], and Hannan et al [34] focus on improving the SOC and SOH estimation for LIBs, particularly in EV applications. Hannan et al [32] introduced a novel deep learning-based transformer model trained using self-supervised learning (SSL) techniques for SOC estimation.…”

Section: Self-supervised Learningmentioning

confidence: 99%

“…Additionally, the learning weights from the SSL training process exhibit transferability, enabling the model to perform well on new LIBs with different chemistries. Che et al [33] proposed a self-supervised learning framework for SOH estimation. Their approach uses filter-based data preprocessing and an auto-encoder-decoder network to learn aging characteristics from unlabeled data.…”

Section: Self-supervised Learningmentioning

confidence: 99%

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

Madani,

Ziebert,

Vahdatkhah

et al. 2024

Batteries

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In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring.

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“…2) To make the battery work at appropriate voltage and temperature intervals, ensuring safety and extending battery life [47], [48].…”

Section: A Battery Elementmentioning

confidence: 99%

Evaluating the Battery Management System's Performance Under Levels of State of Health (SOH) Parameters

Amifia,

Kamali

2023

Journal of Robotics and Control

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Batteries in electric vehicles are the primary focus battery health care. The Battery Management System (BMS) maintains optimal battery conditions by evaluating the system's Htate of health (SOH). SOH identification can recommend the right time to replace the battery to keep the electric vehicle system working optimally. With suitable title and accuracy, the battery will avoid failure and have a long service life. This research aims to produce estimates and identify SOH parameters so that the performance of the battery management system increases. The central parameter values obtained are R0, Rp, and Cp based on Thevenin battery modeling. Then, to get good initialization and accurate results, the parameter identification is completed using an adaptive algorithm, namely Coulomb Counting and Open Circuit Voltage (OCV). The two algorithms compare the identification results of error, MAE, RSME, and final SOH. The focus of this research is to obtain data on estimation error values along with information regarding reliable BMS performance. The performance of the current estimation algorithm is known by calculating the error, which is presented in the form of root mean square error (RMSE) and mean absolute error (MAE). The SOH estimation results using Coulomb Counting have a better error than OCV, namely 1.770%, with a final SOH value of 17.33%. The Thevenin battery model can model the battery accurately with an error of 0.0451%.

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Boosting battery state of health estimation based on self-supervised learning

Cited by 27 publications

References 28 publications

A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries

A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

Evaluating the Battery Management System's Performance Under Levels of State of Health (SOH) Parameters

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