A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction
According to the United States environmental protection agency (EPA), every burned gallon of gasoline generates 8.87 Kg of CO2. The pollution created by vehicles’ fuel consumption has been one of the primary sources of environmental contamination that can lead to more climate changes and...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9944663/ |
| _version_ | 1798017636744822784 |
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| author | Mohamed Elmahallawy Tarek Elfouly Ali Alouani Ahmed M. Massoud |
| author_facet | Mohamed Elmahallawy Tarek Elfouly Ali Alouani Ahmed M. Massoud |
| author_sort | Mohamed Elmahallawy |
| collection | DOAJ |
| description | According to the United States environmental protection agency (EPA), every burned gallon of gasoline generates 8.87 Kg of CO2. The pollution created by vehicles’ fuel consumption has been one of the primary sources of environmental contamination that can lead to more climate changes and global warming. Thus, science and technology have converged on the idea that reducing fuel consumption benefits the environment and human health. One of the ideas for reducing fuel usage is deploying hybrid electric vehicles (HEVs) and electric vehicles (EVs) using renewable energy as alternatives to gasoline. One of the main issues with EV batteries is that over operational time the battery health degrades and ultimately becomes unsafe to use. It is crucial that safety issues be addressed by researchers and battery manufacturers. Assessing and predicting battery health has been a high-priority research topic to attempt to mitigate the danger introduced by EV batteries. Although various techniques have been developed to estimate and predict the battery’s state of health (SOH), they do not cover all degradation scenarios that may affect the battery’s lifetime. In addition, the models used in estimating and predicting the battery’s lifetime need to be improved to provide a more accurate battery health state and guarantee battery safety while in use by an EV. Even though all types of EV batteries face similar issues, this paper focuses on Li-ion EV batteries. The main objectives of this paper are 1) to present various Li-ion battery models that are used to mimic battery dynamic behaviors, 2) to discuss the degradation factors that cause the battery lifespan to be degraded, and to become unsafe, 3) to provide a review of the estimation and prediction techniques used for Li-ion battery SOH and remaining useful life (RUL) estimation along with a discussion of their advantages and limitations, and 4) to provide recommendations for improving Li-ion battery lifetime estimation. This paper represents a concise source of information for battery community researchers to help expedite beneficial and practical outcomes to improve EV battery safety. |
| first_indexed | 2024-04-11T16:10:30Z |
| format | Article |
| id | doaj.art-2c85396dac16445eaf65ebb03d587a7b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T16:10:30Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2c85396dac16445eaf65ebb03d587a7b2022-12-22T04:14:42ZengIEEEIEEE Access2169-35362022-01-011011904011907010.1109/ACCESS.2022.32211379944663A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime PredictionMohamed Elmahallawy0https://orcid.org/0000-0002-5731-9253Tarek Elfouly1https://orcid.org/0000-0002-1688-6163Ali Alouani2Ahmed M. Massoud3https://orcid.org/0000-0001-9343-469XDepartment of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN, USADepartment of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN, USADepartment of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN, USADepartment of Electrical Engineering, Qatar University, Doha, QatarAccording to the United States environmental protection agency (EPA), every burned gallon of gasoline generates 8.87 Kg of CO2. The pollution created by vehicles’ fuel consumption has been one of the primary sources of environmental contamination that can lead to more climate changes and global warming. Thus, science and technology have converged on the idea that reducing fuel consumption benefits the environment and human health. One of the ideas for reducing fuel usage is deploying hybrid electric vehicles (HEVs) and electric vehicles (EVs) using renewable energy as alternatives to gasoline. One of the main issues with EV batteries is that over operational time the battery health degrades and ultimately becomes unsafe to use. It is crucial that safety issues be addressed by researchers and battery manufacturers. Assessing and predicting battery health has been a high-priority research topic to attempt to mitigate the danger introduced by EV batteries. Although various techniques have been developed to estimate and predict the battery’s state of health (SOH), they do not cover all degradation scenarios that may affect the battery’s lifetime. In addition, the models used in estimating and predicting the battery’s lifetime need to be improved to provide a more accurate battery health state and guarantee battery safety while in use by an EV. Even though all types of EV batteries face similar issues, this paper focuses on Li-ion EV batteries. The main objectives of this paper are 1) to present various Li-ion battery models that are used to mimic battery dynamic behaviors, 2) to discuss the degradation factors that cause the battery lifespan to be degraded, and to become unsafe, 3) to provide a review of the estimation and prediction techniques used for Li-ion battery SOH and remaining useful life (RUL) estimation along with a discussion of their advantages and limitations, and 4) to provide recommendations for improving Li-ion battery lifetime estimation. This paper represents a concise source of information for battery community researchers to help expedite beneficial and practical outcomes to improve EV battery safety.https://ieeexplore.ieee.org/document/9944663/Electric vehicles (EVs)Lithium-ion (Li-ion) batteriesstate of health (SOH)remaining useful life (RUL)battery modelsbattery aging |
| spellingShingle | Mohamed Elmahallawy Tarek Elfouly Ali Alouani Ahmed M. Massoud A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction IEEE Access Electric vehicles (EVs) Lithium-ion (Li-ion) batteries state of health (SOH) remaining useful life (RUL) battery models battery aging |
| title | A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction |
| title_full | A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction |
| title_fullStr | A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction |
| title_full_unstemmed | A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction |
| title_short | A Comprehensive Review of Lithium-Ion Batteries Modeling, and State of Health and Remaining Useful Lifetime Prediction |
| title_sort | comprehensive review of lithium ion batteries modeling and state of health and remaining useful lifetime prediction |
| topic | Electric vehicles (EVs) Lithium-ion (Li-ion) batteries state of health (SOH) remaining useful life (RUL) battery models battery aging |
| url | https://ieeexplore.ieee.org/document/9944663/ |
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