Model predictive control of MEMS LCR

Singh, Amardeep; Izadian, Afshin; Anwar, Sohel

doi:10.1109/iecon.2013.6699707

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…A similar approach was conducted by Singh et al where overcharge and overdischarge cycles were examined and a model was constructed for normal operation and any other operation that exceeded normal limits. 148 Impedance data along with an ECM was used to construct a battery model. Multiple model adaptive estimation was adopted for comparing the measured data against each separate model derived for battery overcharge and overdischarge.…”

Section: = [ ]mentioning

confidence: 99%

Review—Use of Impedance Spectroscopy for the Estimation of Li-ion Battery State of Charge, State of Health and Internal Temperature

Carthy¹,

Gullapalli²,

Ryan³

et al. 2021

J. Electrochem. Soc.

101

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The rapid adoption of electric vehicles (EVs) and the evolving needs of portable electronic devices has intensified the need for enhanced state diagnosis of Li-ion batteries (LIBs). As the applications for LIBs continue to grow, so too does their operational requirements; ranging from faster charging and improved safety to optimized energy control and extended lifespan. In order to keep pace with the growing requirements of LIBs, improvements in the monitoring of battery states must be achieved. Although electrochemical impedance spectroscopy (EIS) has existed since the 1960's, its potential as a diagnosis tool has only received widespread attention in recent years. In this paper, a detailed review on the applicability of impedance measurements for the estimation of the vital battery parameters of state of charge (SOC), state of health (SOH) and internal temperature (IT) has been performed.

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Section: = [ ]mentioning

confidence: 99%

Review—Use of Impedance Spectroscopy for the Estimation of Li-ion Battery State of Charge, State of Health and Internal Temperature

Carthy¹,

Gullapalli²,

Ryan³

et al. 2021

J. Electrochem. Soc.

101

View full text Add to dashboard Cite

show abstract

“…The basic components of an electronic cell (e.g. cell lead-acid) is a positive electrode, a negative electrode, and electrolyte (solid and liquid) [2,3]. The battery is one of the main supporting factors for being successful in electric vehicle [4].…”

Section: Introductionmentioning

confidence: 99%

Fault detection design and simulation based on battery modelling

Amifia¹,

Widayat²,

Cahyadi³

et al. 2016

AIP Conference Proceedings

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Abstract. In electric vehicles, a lithium polymer battery is usually adopted as the main energy storage battery for charging and discharging processes. To manage the battery, Battery Management System (BMS) is needed in the effective and efficient way in order to assure the operation of an EV successful. Failure in the battery means failure in the whole system. In fact, most of the battery damage are found due to the failure of error detection on the battery's sensors or actuators. Therefore, it requires fault detection on the battery which can work accurately. In this research, the implementation of equivalent circuit models of Lithium Polymer batteries has been done in the Matlab Simulink model to determine the working limit of the voltage and current so that any fault can be detected and isolated, such as overcurrent and overvoltage. The simulation results show that the error detection can be done accurately so that the battery can work optimally.

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Model predictive control of MEMS LCR

Cited by 2 publications

References 14 publications

Review—Use of Impedance Spectroscopy for the Estimation of Li-ion Battery State of Charge, State of Health and Internal Temperature

Review—Use of Impedance Spectroscopy for the Estimation of Li-ion Battery State of Charge, State of Health and Internal Temperature

Fault detection design and simulation based on battery modelling

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