Automatic equivalent-circuit estimation system for lithium-ion battery

Hirai, Tsuneo; Ohnishi, Atsushi; Nagaoka, Naoto; Mori, N.; Ametani, Akihiro; Umeda, Shigeki

doi:10.1109/upec.2008.4651678

Cited by 24 publications

(16 citation statements)

References 1 publication

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“…Hirai et al [47] and Moo et al [48] calculate OCV t directly given the current I t and the terminal voltage U t based on the Randle equivalent model.…”

Section: Open Circuit Voltage Estimationmentioning

confidence: 99%

“…Given the OCV, the SoC is then estimated using a predefined relationship that is considered as linear [46,48] for Lead-acid batteries and exponential [47] for Li-ion ones.…”

Section: Open Circuit Voltage Estimationmentioning

confidence: 99%

“…The time domain analysis, which uses experimental battery voltage and current data, under pulse tests [40,63,47]. 2.…”

Section: Parameters Identificationmentioning

confidence: 99%

“…This method can use a lot of BMS resources despite the fact that these parameters change slowly during the cycling conditions and do not need to be frequently estimated. Hirai et al [47] identify the model parameters for a specific temperature and SoC. This model cannot be efficiently generalized to situations not seen previously.…”

Section: Parameters Identificationmentioning

confidence: 99%

“…Moreover the model parameters change during charge/discharge of the battery. The proposed improvements [46,43,51,47] are not enough as they do not take into account the limited hardware resources and the improved model cannot be efficiently generalized to cases not previously seen.…”

Section: Parameters Identificationmentioning

confidence: 99%

See 4 more Smart Citations

From a novel classification of the battery state of charge estimators toward a conception of an ideal one

Kalawoun

Biletska

Suard

et al. 2015

Journal of Power Sources

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. From a novel classification of the battery state of charge estimators toward a conception of an ideal one. Journal of Power Sources, Elsevier, 2015, 279, pp.694 -706. 10 b s t r a c tAn efficient estimation of the State of Charge (SoC) of an electrical battery in a real-time context is essential for the development of an intelligent management of the battery energy. The main performance limitations of a SoC estimator originate in limited Battery Management System hardware resources as well as in the battery behavior cross-dependence on the battery chemistry and its cycling conditions. This paper presents a review of methods and models used for SoC estimation and discusses their concept, adaptability and performances in real-time applications. It introduces a novel classification of SoC estimation methods to facilitate the identification of aspects to be improved to create an ideal SoC model. An ideal model is defined as the model that provides a reliable SoC for any battery type and cycling condition, online. The benefits of the machine learning methods in providing an online adaptive SoC estimator are thoroughly detailed. Remaining challenges are specified, through which the characteristics of an ideal model can emerge.

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“…Hirai et al [47] and Moo et al [48] calculate OCV t directly given the current I t and the terminal voltage U t based on the Randle equivalent model.…”

Section: Open Circuit Voltage Estimationmentioning

confidence: 99%

“…Given the OCV, the SoC is then estimated using a predefined relationship that is considered as linear [46,48] for Lead-acid batteries and exponential [47] for Li-ion ones.…”

Section: Open Circuit Voltage Estimationmentioning

confidence: 99%

“…The time domain analysis, which uses experimental battery voltage and current data, under pulse tests [40,63,47]. 2.…”

Section: Parameters Identificationmentioning

confidence: 99%

Section: Parameters Identificationmentioning

confidence: 99%

Section: Parameters Identificationmentioning

confidence: 99%

See 3 more Smart Citations

From a novel classification of the battery state of charge estimators toward a conception of an ideal one

Kalawoun

Biletska

Suard

et al. 2015

Journal of Power Sources

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show abstract

Adaptive online state-of-charge determination based on neuro-controller and neural network

Shen

2010

Energy Conversion and Management

117

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Analysis of DC–DC power supply systems for pulsed loads from green electronics perspective

Gárate¹,

Ibarra²,

Alegría³

et al. 2015

IET Power Electronics

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Discontinuous loads frequently compromise the performance of their power source and electronics. They cause the voltage and current ripple at the source and load, and introduce electromagnetic interferences. Also, they affect the efficiency of the power source. The aforementioned issues are particularly relevant in battery powered electronics. In order to minimise these unwanted effects, it is necessary to introduce a power supply architecture between the load and the source that should filter and/or regulate the currents and voltages. This architecture could be made solely of passive components or could use DC-DC regulators. The present work classifies and characterises the most relevant architectures available. A novel switched power supply architecture for pulsed loads with adaptive input current is also introduced. A mathematical analysis of the conditions and characteristics that the regulated architectures should fulfil to obtain the maximum performance in terms of efficiency and green electronics is provided. The simulation and experimental results shown in this study demonstrate the theoretical analysis.

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Automatic equivalent-circuit estimation system for lithium-ion battery

Cited by 24 publications

References 1 publication

From a novel classification of the battery state of charge estimators toward a conception of an ideal one

From a novel classification of the battery state of charge estimators toward a conception of an ideal one

Adaptive online state-of-charge determination based on neuro-controller and neural network

Analysis of DC–DC power supply systems for pulsed loads from green electronics perspective

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