A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries

Ren, Zhong; Du, Changqing

doi:10.1016/j.egyr.2023.01.108

Cited by 73 publications

(20 citation statements)

References 124 publications

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“…This model had approximately 100% detection accuracy when it came to identifying cyberattacks in the monitoring system. En and Du [80] compared LTSM, SVM, and GPR to predict SOH for lithium-ion batteries. The assessment was carried out using various performance indicators, such as datasets, input features, hyperparameter adjustments, benefits, and drawbacks.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Lipu,

Miah,

Jamal

et al. 2023

Vehicles

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In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Lipu,

Miah,

Jamal

et al. 2023

Vehicles

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“…This allows differences in the voltage of each cell, which accumulate when the battery cell voltage is measured. Therefore, it is challenging to design a suitable circuit to minimize the voltage difference across each battery cell in the BMS [60], [61].…”

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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“…Therefore, the data-driven approach relies heavily on high-quality datasets and algorithms, which are otherwise prone to over-and underfitting problems. [21] All the above-proposed methods are based on the estimation of SOH of Li-ion batteries under a single operating condition. However, in practical applications, the battery discharge is constantly changing according to the user's operation.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the data‐driven approach relies heavily on high‐quality datasets and algorithms, which are otherwise prone to over‐ and underfitting problems. [ 21 ]…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network‐Long Short‐Term Memory‐Based State of Health Estimation for Li‐Ion Batteries under Multiple Working Conditions

Feng,

Song,

Lin

et al. 2023

Energy Tech

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The state of health (SOH) for lithium‐ion batteries is an important indicator to ensure the safety and reliability of battery energy storage systems. Aiming at the difficulty of accurately estimating the SOH of lithium‐ion batteries under different working conditions, this article proposes a method based on a hybrid convolutional neural network‐long short‐term memory (CNN‐LSTM) model. First, the battery health indicators and capacity data under different operating conditions are extracted from the public dataset to form a new dataset. Second, the CNN has multiple one‐dimensional convolutional layers to improve the efficiency of feature extraction from new datasets, and the resulting features are used as inputs to the LSTM to predict SOH. Finally, the CNN‐LSTM model integrates a fully connected layer that outputs the estimation of SOH for different operating conditions. The results show that the mean absolute error of the SOH estimation results is within 2.33% and 3.01% for the same and different working conditions, respectively.

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A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries

Cited by 73 publications

References 124 publications

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

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

Convolutional Neural Network‐Long Short‐Term Memory‐Based State of Health Estimation for Li‐Ion Batteries under Multiple Working Conditions

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