A semi-empirical, electrochemistry-based model for Li-ion battery performance prediction over lifetime

Varini, Maria; Campana, Pietro Elia; Lindbergh, Göran

doi:10.1016/j.est.2019.100819

Cited by 50 publications

(27 citation statements)

References 43 publications

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“…This is important for thermal management of Lithium-ion battery stacks [29] to protect against overheating [30,31] and fires [32] because exceeding the upper limit risks thermal runaway whereas operating under the lower limit, reduces cell life. It could also be useful for indirect battery pack cooling [33], where pre-cooled air enables operation at an optimum temperature which improves power output, charging rate, and battery life [34,35].…”

mentioning

confidence: 99%

An experimentally-verified temperature control simulation model for spray evaporative cooling of vibrating powertrain parts

Jose

Sarmadian

Dunne

et al. 2021

International Journal of Heat and Mass Transfer

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mentioning

confidence: 99%

An experimentally-verified temperature control simulation model for spray evaporative cooling of vibrating powertrain parts

Jose

Sarmadian

Dunne

et al. 2021

International Journal of Heat and Mass Transfer

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“…Most rechargeable cells include a safety valve that releases excess pressure when incorrectly charged. The release of pressure through the closing valve does not cause any damage, however, some electrolyte may escape during ventilation [26][27][28].…”

Section: Nickel Cadmium Batterymentioning

confidence: 99%

Degradation of Lithium-Ion Batteries in an Electric Transport Complex

et al. 2021

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The article provides an overview and comparative analysis of various types of batteries, including the most modern type—lithium-ion batteries. Currently, lithium-ion batteries (LIB) are widely used in electrical complexes and systems, including as a traction battery for electric vehicles. Increasing the service life of the storage devices used today is an important scientific and technical problem due to their rapid wear and tear and high cost. This article discusses the main approaches and methods for researching the LIB resource. First of all, a detailed analysis of the causes of degradation was carried out and the processes occurring in lithium-ion batteries during charging, discharging, resting and difficult operating conditions were established. Then, the main factors influencing the service life are determined: charging and discharging currents, self-discharge current, temperature, number of cycles, discharge depth, operating range of charge level, etc. when simulating a real motion process. The work considers the battery management systems (BMS) that take into account and compensate for the influence of the factors considered. In the conclusion, the positive and negative characteristics of the presented methods of scientific research of the residual life of LIB are given and recommendations are given for the choice of practical solutions to engineers and designers of batteries. The work also analyzed various operating cycles of electric transport, including heavy forced modes, extreme operating modes (when the amount of discharge and discharge of batteries is greater than the nominal value) and their effect on the degradation of lithium-ion batteries.

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“…2,4 Previous studies tended to analyze battery service life by fitting semiempirical models. [3][4][5] Consequently, many researchers have proposed diverse physical methods for simulating the electrochemical process in batteries. 6,7 Rakhmatov et al used an analytical model based on diffusion theory to predict the life of lithium ion battery.…”

Section: Introductionmentioning

confidence: 99%

“…How to use early data to predict cell life is a key issue in battery application and management . Previous studies tended to analyze battery service life by fitting semi‐empirical models . Consequently, many researchers have proposed diverse physical methods for simulating the electrochemical process in batteries .…”

Section: Introductionmentioning

confidence: 99%

Predicting battery life with early cyclic data by machine learning

2019

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This work applies machine learning tools to achieve the early prediction of commercial battery life. We compared the prediction accuracy of different machine learning algorithms to the battery database. Among various algorithms, the decision tree (DT) method exhibits the highest accuracy of 95.2% to predict whether the battery can maintain above 80% initial capacity after 550 cycles. Using the initial two cycles of data, DT proposes that the change of discharge capacity is the main feature to estimate the lifetime type of batteries. Given the first 100 cycles, the factor with the maximum weight turns to the internal resistance for estimating the battery lifetime. K E Y W O R D Slifetime prediction, lithium-ion battery, machine learning

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A semi-empirical, electrochemistry-based model for Li-ion battery performance prediction over lifetime

Cited by 50 publications

References 43 publications

An experimentally-verified temperature control simulation model for spray evaporative cooling of vibrating powertrain parts

An experimentally-verified temperature control simulation model for spray evaporative cooling of vibrating powertrain parts

Degradation of Lithium-Ion Batteries in an Electric Transport Complex

Predicting battery life with early cyclic data by machine learning

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