Optimal battery charging, Part I: Minimizing time-to-charge, energy loss, and temperature rise for OCV-resistance battery model

Abdollahi, Ali; Han, Xu; Balasingam, Balakumar; Raghunathan, Nithin; Pattipati, Krishna R.; Bar‐Shalom, Yaakov

doi:10.1016/j.jpowsour.2015.02.075

Cited by 100 publications

(42 citation statements)

References 24 publications

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“…Abdollahi et al 25,26 have utilized an equivalent circuit model of LiB and analytically derived the optimal charging profile that minimizes a weighted sum of time to charge, energy loss, and temperature rise index. However, such analytical derivations are possible only for simple battery models that ignore various physical phenomena such as diffusion voltage and double layer capacitance and are limited in scope.…”

Section: Discussionmentioning

confidence: 99%

Fast computational framework for optimal life management of lithium ion batteries

et al. 2018

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Summary The determination of optimal charging profiles over cycle life of a lithium ion battery is a challenging problem that is extremely important for commercial applications. It is a difficult problem to solve owing to the complex degradation processes occurring inside the battery. Further, modeling of a realistic battery operation, let alone the degradation mechanisms, results in computationally expensive mathematical models. In the present study, a framework is developed towards addressing this problem by (1) developing a method to formulate extremely fast and accurate algebraic models that capture essential features such as charging time and aging characteristics described by battery models and (2) utilizing these algebraic models in an optimization framework involving genetic algorithms for determining the optimal charging profiles over the cycle life of the battery. The utility of the present framework in determining the optimal charging solutions is illustrated with various real‐life usage scenarios such as fast charging and extension of cycle life. The proposed solution can be utilized onboard for generating the optimal charging profiles over cycle life of the battery.

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Section: Discussionmentioning

confidence: 99%

Fast computational framework for optimal life management of lithium ion batteries

et al. 2018

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“…In Ref. [117], a closedform approach to search the optimal charging strategy for a Li-ion battery is proposed. A cost function which considers the charging time, energy loss and temperature rise is used to acquire the optimal CC-CV charging profile.…”

Section: Optimization Of Battery Charging Approachmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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“…Abdollahi et al [22] propose a closed-form optimal control solution to solve the optimal charging of a Li-ion battery. An objective function which considers the time-to-charge, energy losses and a temperature rise index is used to acquire the optimal CCCV solution.…”

Section: Introductionmentioning

confidence: 99%

An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model

Liu

Yang

et al. 2017

Electrochimica Acta

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. (2017) An advanced Lithiumion battery optimal charging strategy based on a coupled thermoelectric model. Electrochimica Acta, 225. pp. 330-344. Permanent WRAP URL:http://wrap.warwick.ac.uk/86302 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription. Kingdom (Email:{kliu02,k.li,zyang07,czhang07,j.deng}@qub.ac.uk).Abstract: Lithium-ion batteries are widely adopted as the power supplies for electric vehicles. A key but challenging issue is to achieve optimal battery charging, while taking into account of various constraints for safe, efficient and reliable operation. In this paper, a triple-objective function is first formulated for battery charging based on a coupled thermoelectric model. An advanced optimal charging strategy is then proposed to develop the optimal constant-current-constant-voltage (CCCV) charge current profile, which gives the best trade-off among three conflicting but important objectives for battery management. To be specific, a coupled thermoelectric battery model is first presented. Then, a specific triple-objective function consisting of three objectives, namely charging time, energy loss, and temperature rise (both the interior and surface), is proposed.Heuristic methods such as Teaching-learning-based-optimization (TLBO) and particle swarm optimization (PSO) are applied to optimize the triple-objective function, and their optimization performances are compared.The impacts of the weights for different terms in the objective function are then assessed. Experimental results show that the proposed optimal charging strategy is capable of offering desirable effective optimal charging current profiles and a proper trade-off among the conflicting objectives. Further, the proposed optimal charging strategy can be easily extended to other battery types.

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Optimal battery charging, Part I: Minimizing time-to-charge, energy loss, and temperature rise for OCV-resistance battery model

Cited by 100 publications

References 24 publications

Fast computational framework for optimal life management of lithium ion batteries

Fast computational framework for optimal life management of lithium ion batteries

A brief review on key technologies in the battery management system of electric vehicles

An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model

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