Search for an Optimal Rapid Charging Pattern for Lithium–Ion Batteries Using Ant Colony System Algorithm

Liu, Yi-Hwa; Teng, Jen‐Hao; Lin, Ying‐Chih

doi:10.1109/tie.2005.855670

Cited by 175 publications

(73 citation statements)

References 17 publications

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“…The control algorithm changes the charging behavior only at the tuning points, and the period outside the tuning points is unaffected. The ant colony algorithm was used to optimize the MSCC profile [28]. This optimization algorithm does not use the real-time measurements of the state of the battery to change the charging current, but rather finds the best charging profile for the entire process.…”

Section: Resultsmentioning

confidence: 99%

“…For the charging of lithium-ion batteries, different charging techniques, such as constant current and constant voltage (CC/CV), pulse charging (PC) algorithm, model-based charging, and multistage current charging algorithm (MSCC), have been reported [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The CC/CV techniques is computationally efficient and can be implemented easily.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [27] proposed that a dynamic optimization algorithm can be utilized to determine the optimal charging time. Liu et al [28] implemented the ant colony algorithm to find the optimal current to charge a lithium-ion battery. In addition to these methodologies, a fuzzy logic controller (FLC) could be beneficial [29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

et al. 2018

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Abstract:The lithium-ion battery has high energy and power density, long life cycle, low toxicity, low discharge rate, more reliability, and better efficiency compared to other batteries. On the other hand, the issue of a reduction in charging time of the lithium-ion battery is still a bottleneck for the commercialization of electric vehicles (EVs). Therefore, an approach to charge lithium-ion batteries at a faster rate is needed. This paper proposes an efficient, real-time, fast-charging methodology of lithium-ion batteries. Fuzzy logic was adopted to drive the charging current trajectory. A temperature control unit was also implemented to evade the effects of fast charging on the aging mechanism. The proposed method of charging also protects the battery from overvoltage and overheating. Extensive testing and comprehensive analysis were conducted to examine the proposed charging technique. The results show that the proposed charging strategy favors a full battery recharging in 9.76% less time than the conventional constant-current-constant-voltage (CC/CV) method. The strategy charges the battery at a 99.26% state of charge (SOC) without significant degradation. The entire scheme was implemented in real time, using Arduino interfaced with MATLAB TM Simulink. This decrease in charging time assists in the fast charging of cell phones and notebooks and in the large-scale deployment of EVs.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

et al. 2018

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“…Methods involving computational intelligence techniques such as neural networks [16], gray prediction [17], fuzzy control [14,18], and ant-colony algorithm [19] have been proposed to optimize the charging current profile.…”

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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“…In [12], a Taguchi-based algorithm was used to obtain rapid charge. In [13], an Ant Colony System algorithm was used to select the optimum charging current among five charging stages, and the charging time was decreased and battery cycle life was extended by 25%. Fig.…”

Section: Introductionmentioning

confidence: 99%

An ECM-PSO based optimal charging current pattern searching for Li-ion batteries

Min¹,

Sun²,

Liu³

et al. 2017

Proceedings of the 2017 International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2017)

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Abstract. EV battery charging is growing in importance as it has a direct influence on the EV performance. Multistage Constant Current (MCC) charging strategy is an effective rapid charging method. This paper proposes an ECM-PSO algorithm to search for the optimal current pattern of the MCC charging strategy. The MCC charging model is built according to the Li-ion battery equivalent circuit model, and the object function for the ECM-PSO optimization was formulated and the optimal current pattern of the MCC charging method was obtained through the ECM-PSO algorithm. The experimental result shows that the obtained current pattern is capable of charging the batteries to 87.7% of the rated capacity within 36.5 min. Compared with the conventional CC-CV charging method, the proposed charging strategy has a performance improvement of 60.3% in charging time reduction, and the average temperature rise during the proposed MCC charging process is 0.5 ℃ lower than the CC-CV charging method.

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Search for an Optimal Rapid Charging Pattern for Lithium–Ion Batteries Using Ant Colony System Algorithm

Cited by 175 publications

References 17 publications

A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

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

An ECM-PSO based optimal charging current pattern searching for Li-ion batteries

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