Hybrid data-based modeling for the prediction and diagnostics of Li-ion battery thermal behaviors

Legala, Adithya; Li, Xianguo

doi:10.1016/j.egyai.2022.100194

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…108 Researchers have still devoted themselves to using ML to forecast the battery's internal thermal behavior. Up till now, multi-features such as cell external temperature, depth of discharge, nominal capacity, ambient temperature, and discharge rate have been utilized to train the ML, realizing a prediction of thermal effects (generally expressed as heat generation rate 109,110 /internal temperature 111 /external temperature 112,113 ).…”

Section: Thermal-based Tasksmentioning

confidence: 99%

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Huang

Sugiarto

2023

EcoMat

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Lithium‐ion batteries (LIBs) have been dominating the markets of electric vehicles and grid energy storage. Accurate monitoring of battery health status has been one of the most critical challenges of the battery industry. Machine learning (ML) has been widely applied to battery health estimation as well as prediction. Here, by investigating the specific features and targets, we comprehensively discuss task‐oriented ML implementation in various application scenarios in the field of battery health. This review explores the tasks assisted by ML based on multi‐level cell degradation. We highlight opportunities and significance of considering the potential feature–target pair during the ML model training to identify more health information about LIBs as well as shed light into designing tasks for new application scenarios.

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Section: Thermal-based Tasksmentioning

confidence: 99%

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Huang

Sugiarto

2023

EcoMat

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“…The adaptive algorithm used to identify these parameters is Coulomb Counting to get maximum results to obtain the SOH initialization value. So, this can be used as an essential reference in knowing BMS performance [81], [82], [83]. Fig.…”

Section: Identify State Of Health Parametersmentioning

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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Green Energy

Adhikari,

Patel,

Sindhwani

et al. 2024

Carbon‐based Nanomaterials for Green Applications

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Hybrid data-based modeling for the prediction and diagnostics of Li-ion battery thermal behaviors

Cited by 8 publications

References 29 publications

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

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

Green Energy

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