Active equalization for lithium-ion battery pack via data-driven residual charging capacity estimation

Considering the limitations in existing voltage-based and state-of-charge (SOC)-based active equalization strategies, including the difficulty in threshold value determination for equalization system on/off controlling, repeated estimation for equalization variable and the corresponding tremendous c...

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Veröffentlicht in:Journal of cleaner production 2023-10, Vol.422, p.138583, Article 138583
Hauptverfasser: Zhang, Shuzhi, Wu, Shaojie, Cao, Ganglin, Zhang, Xiongwen
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creator Zhang, Shuzhi
Wu, Shaojie
Cao, Ganglin
Zhang, Xiongwen
description Considering the limitations in existing voltage-based and state-of-charge (SOC)-based active equalization strategies, including the difficulty in threshold value determination for equalization system on/off controlling, repeated estimation for equalization variable and the corresponding tremendous complexity, this paper designs a novel residual capacity-based active equalization strategy via data-driven residual charging capacity (RCC) estimation to fully charge all in-pack cells during constant current (CC) charging stage. Firstly, taking charge accumulation corresponding to specific voltage window as input, we build a data-driven RCC estimation model to online monitor each cell's RCC at specific voltage. Afterwards, considering the initial cell inconsistences, each cell's current RCC before equalization is further computed based on the estimated RCC and the subsequently measured charge accumulation. Finally, a parallel global search algorithm named particle swarm optimization is adopted to search the optimal combination of in-pack cells' equalization current considering equalization time and energy loss simultaneously. The verification results based on the Oxford battery degradation dataset demonstrate that the established data-driven model can realize accurate RCC estimation and all in-pack cells' totally charged capacity can roughly approach their maximum capacity at the end of CC charging stage using the proposed equalization strategy. Meanwhile, the maximum cell voltage difference and maximum cell SOC difference can be limited below 0.01V and 0.02, respectively. Moreover, by extending the charging time by only about 4min, the developed equalization strategy can further increase battery pack capacity by about 10%. [Display omitted] •A novel residual capacity-based active equalization strategy is designed.•A data-driven residual charging capacity estimation method is developed.•The optimal combination of in-pack cells' equalization current is searched by PSO.•The maximum voltage difference among all in-pack cells can be limited below 0.01V.•Cells' RE between maximum capacity and totally charged capacity are between ±2.5%.
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Firstly, taking charge accumulation corresponding to specific voltage window as input, we build a data-driven RCC estimation model to online monitor each cell's RCC at specific voltage. Afterwards, considering the initial cell inconsistences, each cell's current RCC before equalization is further computed based on the estimated RCC and the subsequently measured charge accumulation. Finally, a parallel global search algorithm named particle swarm optimization is adopted to search the optimal combination of in-pack cells' equalization current considering equalization time and energy loss simultaneously. The verification results based on the Oxford battery degradation dataset demonstrate that the established data-driven model can realize accurate RCC estimation and all in-pack cells' totally charged capacity can roughly approach their maximum capacity at the end of CC charging stage using the proposed equalization strategy. Meanwhile, the maximum cell voltage difference and maximum cell SOC difference can be limited below 0.01V and 0.02, respectively. Moreover, by extending the charging time by only about 4min, the developed equalization strategy can further increase battery pack capacity by about 10%. [Display omitted] •A novel residual capacity-based active equalization strategy is designed.•A data-driven residual charging capacity estimation method is developed.•The optimal combination of in-pack cells' equalization current is searched by PSO.•The maximum voltage difference among all in-pack cells can be limited below 0.01V.•Cells' RE between maximum capacity and totally charged capacity are between ±2.5%.</description><identifier>ISSN: 0959-6526</identifier><identifier>EISSN: 1879-1786</identifier><identifier>DOI: 10.1016/j.jclepro.2023.138583</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Active equalization strategy ; algorithms ; data collection ; Data-driven residual charging capacity estimation ; electric potential difference ; Equalization current calculation ; lithium batteries ; Lithium-ion battery pack ; Particle swarm optimization</subject><ispartof>Journal of cleaner production, 2023-10, Vol.422, p.138583, Article 138583</ispartof><rights>2023 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c342t-a3e3cf68060c7045de9dfbe07da929ba04b80085fe4144e0871662bf4fdae3573</citedby><cites>FETCH-LOGICAL-c342t-a3e3cf68060c7045de9dfbe07da929ba04b80085fe4144e0871662bf4fdae3573</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jclepro.2023.138583$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids></links><search><creatorcontrib>Zhang, Shuzhi</creatorcontrib><creatorcontrib>Wu, Shaojie</creatorcontrib><creatorcontrib>Cao, Ganglin</creatorcontrib><creatorcontrib>Zhang, Xiongwen</creatorcontrib><title>Active equalization for lithium-ion battery pack via data-driven residual charging capacity estimation</title><title>Journal of cleaner production</title><description>Considering the limitations in existing voltage-based and state-of-charge (SOC)-based active equalization strategies, including the difficulty in threshold value determination for equalization system on/off controlling, repeated estimation for equalization variable and the corresponding tremendous complexity, this paper designs a novel residual capacity-based active equalization strategy via data-driven residual charging capacity (RCC) estimation to fully charge all in-pack cells during constant current (CC) charging stage. Firstly, taking charge accumulation corresponding to specific voltage window as input, we build a data-driven RCC estimation model to online monitor each cell's RCC at specific voltage. Afterwards, considering the initial cell inconsistences, each cell's current RCC before equalization is further computed based on the estimated RCC and the subsequently measured charge accumulation. Finally, a parallel global search algorithm named particle swarm optimization is adopted to search the optimal combination of in-pack cells' equalization current considering equalization time and energy loss simultaneously. The verification results based on the Oxford battery degradation dataset demonstrate that the established data-driven model can realize accurate RCC estimation and all in-pack cells' totally charged capacity can roughly approach their maximum capacity at the end of CC charging stage using the proposed equalization strategy. Meanwhile, the maximum cell voltage difference and maximum cell SOC difference can be limited below 0.01V and 0.02, respectively. Moreover, by extending the charging time by only about 4min, the developed equalization strategy can further increase battery pack capacity by about 10%. 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Firstly, taking charge accumulation corresponding to specific voltage window as input, we build a data-driven RCC estimation model to online monitor each cell's RCC at specific voltage. Afterwards, considering the initial cell inconsistences, each cell's current RCC before equalization is further computed based on the estimated RCC and the subsequently measured charge accumulation. Finally, a parallel global search algorithm named particle swarm optimization is adopted to search the optimal combination of in-pack cells' equalization current considering equalization time and energy loss simultaneously. The verification results based on the Oxford battery degradation dataset demonstrate that the established data-driven model can realize accurate RCC estimation and all in-pack cells' totally charged capacity can roughly approach their maximum capacity at the end of CC charging stage using the proposed equalization strategy. Meanwhile, the maximum cell voltage difference and maximum cell SOC difference can be limited below 0.01V and 0.02, respectively. Moreover, by extending the charging time by only about 4min, the developed equalization strategy can further increase battery pack capacity by about 10%. [Display omitted] •A novel residual capacity-based active equalization strategy is designed.•A data-driven residual charging capacity estimation method is developed.•The optimal combination of in-pack cells' equalization current is searched by PSO.•The maximum voltage difference among all in-pack cells can be limited below 0.01V.•Cells' RE between maximum capacity and totally charged capacity are between ±2.5%.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.jclepro.2023.138583</doi></addata></record>
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subjects Active equalization strategy
algorithms
data collection
Data-driven residual charging capacity estimation
electric potential difference
Equalization current calculation
lithium batteries
Lithium-ion battery pack
Particle swarm optimization
title Active equalization for lithium-ion battery pack via data-driven residual charging capacity estimation
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