Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Li, Yang; Liu, Kailong; Foley, Aoife; Zulke, Alana Aragon; Berecibar, Maitane; Nanini-Maury, Elise; Mierlo, Joeri Van; Hoster, Harry E.

doi:10.1016/j.rser.2019.109254

Cited by 784 publications

(363 citation statements)

References 138 publications

(167 reference statements)

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“…where C curr is the capacity of the battery in its current state, C full is the capacity of the battery in its fully charged state, C nom is the nominal capacity of the brand-new battery 2 .…”

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“…By convention, SOC is 100% when the battery is fully charged and 0% when it is empty, while SOH is 100% at the time of manufacture and reaches 80% at end of life (EOL). In the battery manufacturing industry, EOL is often defined as the point at which the actual capacity at full charge drops to 80% of its nominal value 2 . The remaining number of charge/discharge cycles until the battery reaches EOL is the RUL of the battery.…”

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Predicting the state of charge and health of batteries using data-driven machine learning

et al. 2020

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W ith rising concerns about global warming, electrification of transport has recently emerged as an important vision in many countries. The successful development of electric vehicles (EVs) depends highly on the cycling performance, cost and safety of the batteries. Rechargeable lithium-ion (Li-ion) batteries are currently the best choice for EVs due to their reasonable energy density and cycle life 1 . Further research and development on Li-ion batteries will lead to even higher energy density and more complicated battery dynamics, where the efficiency and safety of such batteries will become a concern. An advanced battery management system (BMS) that can monitor and optimize battery behaviour and safety is thus essential for the entire electrification system 2 .Today, one of the major barriers to widespread adoption of EVs is range anxiety. The ability of a BMS to accurately determine the state of charge (SOC) and state of health (SOH) of batteries, and hence the estimated driving range, will alleviate this problem. In addition, reliable prediction of remaining useful life (RUL) will allow batteries to be used to their fullest potential and maximum life expectancy before replacement or disposal. Knowledge of the RUL of spent batteries will also enable their redeployment in less demanding, second-life applications such as stationary grid storage. If we are able to sort manufactured cells based on their expected lifetime using early-cycle data, we can further accelerate the testing, validation and development process of new batteries. In summary, accurate prediction of the current and future state of batteries will open up vast opportunities in battery manufacturing, usage and optimization 3,4 . SOC and SOH are the two most important parameters in battery management and are generally defined as:where C curr is the capacity of the battery in its current state, C full is the capacity of the battery in its fully charged state, C nom is the nominal capacity of the brand-new battery 2 . In essence, SOC denotes the capacity of the battery in its current state compared to the capacity in its fully charged state (equivalent of a fuel gauge), while SOH describes the capacity of the battery in its fully charged state compared to the nominal capacity when brand new. By convention, SOC is 100% when the battery is fully charged and 0% when it is empty, while SOH is 100% at the time of manufacture and reaches 80% at end of life (EOL). In the battery manufacturing industry, EOL is often defined as the point at which the actual capacity at full charge drops to 80% of its nominal value 2 . The remaining number of charge/discharge cycles until the battery reaches EOL is the RUL of the battery. Current BMSs can determine the SOC of Li-ion batteries within 0.6% to 6.5% 5 , but are unable to predict the SOH and RUL of batteries accurately 6 .The traditional methods for SOC estimation include ampere hour counting estimation, open-circuit voltage-based estimation, impedance-based estimation, model-based estimation, fuzzy logic, and Kal...

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Predicting the state of charge and health of batteries using data-driven machine learning

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“…Advances in lithium‐ion battery technology make it ubiquitous in battery powered devices, ranging from electric terrestrial and aerial vehicles to consumer electronics. Therefore, battery health assessment becomes a crucial problem in providing safety and reliability of the system 1,2 …”

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Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

2020

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Summary Online state of health (SOH) prediction of lithium‐ion batteries remains a very important problem in assessing the safety and reliability of battery‐powered systems. Deep learning techniques based on recurrent neural networks with memory, such as the long short‐term memory (LSTM) and gated recurrent unit (GRU), have very promising advantages, when compared to other SOH estimation algorithms. This work addresses the battery SOH prediction based on GRU. A complete BMS is presented along with the internal structure and configuration parameters. The neural network was highly optimized by adaptive moment estimation (Adam) algorithm. Experimental data show very good estimation results for different temperature values, not only at room value. Comparisons performed against other relevant estimation methods highlight the performance of the recursive neural network algorithms such as GRU and LSTM, with the exception of the battery regeneration points. Compared to LSTM, the GRU algorithm gives slightly higher estimation errors, but within similar prediction error range, while needing significantly fewer parameters (about 25% fewer), thus making it a very suitable candidate for embedded implementations.

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“…To be more than incremental, such improvement could take the form of disruptive battery technologies [2,3] and, equally as important, the form of improved battery management systems with innovative control strategies to enable more efficient and safer battery packs. The latter topic is attracting enormous amount a research and a wide variety of algorithms have been proposed in recent years [4][5][6][7][8] for state-of-charge (SOC) and state-of-health (SOH) tracking. For all these studies, there is a dire need for experimental validation so that the effectiveness of the proposed methodology can be demonstrated.…”

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Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols

Dubarry

Baure

2020

Electronics

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Validation is an integral part of any study dealing with modeling or development of new control algorithms for lithium ion batteries. Without proper validation, the impact of a study could be drastically reduced. In a perfect world, validation should involve testing in deployed systems, but it is often unpractical and costly. As a result, validation is more often conducted on single cells under control laboratory conditions. Laboratory testing is a complex task, and improper implementation could lead to fallacious results. Although common practice in open literature, the protocols used are usually too quickly detailed and important details are left out. This work intends to fully describe, explain, and exemplify a simple step-by-step single apparatus methodology for commercial battery testing in order to facilitate and standardize validation studies.

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Cited by 784 publications

References 138 publications

Predicting the state of charge and health of batteries using data-driven machine learning

Predicting the state of charge and health of batteries using data-driven machine learning

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols

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