SoH-Aware Reconfiguration in Battery Packs

He, Liang; Yang, Zhihan; Gü, Yü; Liu, Cong; He, Tian; Shin, Kang G.

doi:10.1109/tsg.2016.2639445

“…Given the same resting period, the extension demonstrates a decreasing trend in response to an increased load power, e.g., the four orange cones with a resting period of 15 min. The load power level's influence derives from the battery ratecapacity effect; i.e., the higher the discharging rate, the lower the charge/ energy delivery [2], [10], [38], [43], [44]. Thus, when choosing the group of batteries to be scheduled to rest, a tradeoff has to be made between the resting batteries' recovery effect and the operating batteries' rate-capacity effect.…”

Section: Scheduling Principle For Enhanced Energy Conversionmentioning

confidence: 99%

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Han

¹

,

Wik

²

,

Kersten

³

et al. 2020

EEE Ind. Electron. Mag.

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“…Given the same resting period, the extension demonstrates a decreasing trend in response to increased load power, e.g., the four orange cones with a resting period of 15 minutes. Such influence of load power level arises from the battery rate-capacity effect, i.e., the higher the discharging rate, the lower the charge/energy delivery [2,10,38,43,44]. Thus, when choosing the group of batteries scheduled to rest, a trade-off has to be made between the resting batteries' recovery effect and the operating batteries' rate-capacity effect.…”

Section: Management Principles Of Battery System Reconfigurationmentioning

confidence: 99%

“…• Constraints on the sequential or synchronized switch operations to avoid any circuit faults. Among the problems in Table II, those formulated in [11,25,33,43] focus on only one period, either the present control period or a full discharging process. To solve such optimization problems, graph theory has been deployed based on the graph representation of system configurations.…”

Section: B Optimization Algorithms Based On Reconfigurationmentioning

confidence: 99%

“…To solve such optimization problems, graph theory has been deployed based on the graph representation of system configurations. For example, minimizing the total additional resistance was transformed to a minimum path cover problem in [25], and maximizing the battery pack's charge delivery was achieved by constructing and solving a maximum-weight independent set problem [43]. In [11], the power loss curves and terminal voltages of different system configurations were evaluated offline in advance and subsequently used for determining the optimal configuration online in response to the voltage and current demands.…”

Section: B Optimization Algorithms Based On Reconfigurationmentioning

confidence: 99%

See 1 more Smart Citation

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Han¹,

Wik²,

Kersten³

et al. 2020

Preprint

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<div>Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, etc. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications, multiple battery cells have to be connected in series and/or parallel. While battery technology has advanced significantly in the past decade, existing battery management systems (BMSs) mainly focus on state monitoring and control of battery systems packed in fixed configurations. In fixed configurations, though, the battery system performance is in principle limited by the weakest cells, which can leave large parts severely underutilized. Allowing dynamic reconfiguration of battery cells, on the other hand, allows individual and flexible manipulation of the battery system at cell, module, and pack levels, which may open up a new paradigm for battery management. Following this trend, this paper provides an overview of next-generation BMSs featuring dynamic reconfiguration. Motivated by numerous potential benefits of reconfigurable battery systems (RBSs), the hardware designs, management principles, and optimization algorithms for RBSs are sequentially and systematically discussed. Theoretical and practical challenges during the design and implementation of RBSs are highlighted in the end to stimulate future research and development.</div>

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“…In addition, the RBS enables a flexible and wide-range system terminal voltage, and the load/charger voltage can also change over time. To buffer the voltage mismatch between the RBS and the load/charger, voltage regulators or power converters are needed, which introduces additional power conversion losses [11,31,33,43]. Such losses of voltage regulators can vary dramatically.…”

Section: Management Principles Of Battery System Reconfigurationmentioning

confidence: 99%

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Han¹,

Wik²,

Kersten³

et al. 2020

Preprint

1

0

View full text Add to dashboard Cite

<div>Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, etc. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications, multiple battery cells have to be connected in series and/or parallel. While battery technology has advanced significantly in the past decade, existing battery management systems (BMSs) mainly focus on state monitoring and control of battery systems packed in fixed configurations. In fixed configurations, though, the battery system performance is in principle limited by the weakest cells, which can leave large parts severely underutilized. Allowing dynamic reconfiguration of battery cells, on the other hand, allows individual and flexible manipulation of the battery system at cell, module, and pack levels, which may open up a new paradigm for battery management. Following this trend, this paper provides an overview of next-generation BMSs featuring dynamic reconfiguration. Motivated by numerous potential benefits of reconfigurable battery systems (RBSs), the hardware designs, management principles, and optimization algorithms for RBSs are sequentially and systematically discussed. Theoretical and practical challenges during the design and implementation of RBSs are highlighted in the end to stimulate future research and development.</div>

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SoH-Aware Reconfiguration in Battery Packs

Cited by 29 publications

References 16 publications

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Next-Generation Battery Management Systems: Dynamic Reconfiguration

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