Improved joint prediction strategy for state of charge and peak power of lithium-ion batteries by considering hysteresis characteristics-current measurement deviation correction

Qi, Chuangshi; Wang, Shunli; Cao, Wen; Wang, Yangtao; Liu, Donglei; Fernandez, Carlos

doi:10.1016/j.est.2024.110726

Journal of Energy Storage

2024

DOI: 10.1016/j.est.2024.110726

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Improved joint prediction strategy for state of charge and peak power of lithium-ion batteries by considering hysteresis characteristics-current measurement deviation correction

Chuangshi Qi,

Shunli Wang,

Wen Cao

et al.

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A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering

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Wang,

et al. 2024

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Improved joint prediction strategy for state of charge and peak power of lithium-ion batteries by considering hysteresis characteristics-current measurement deviation correction

Cited by 3 publications

References 37 publications

A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering

A hybrid algorithm for the state of energy estimation of lithium-ion batteries based on improved adaptive-forgotten-factor recursive least squares and particle swarm optimized unscented particle filtering

State of power prediction joint fisher optimal segmentation and PO-BP neural network for a parallel battery pack considering cell inconsistency

Decoding degradation: The synergy of partial differential equations and advanced predictive models for lithium-ion battery

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