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CURATOR
A pinboard by
Arghya Chakravarty

PhD Student, Department of Electronics & Electrical Engg., Indian Institute of Technology Guwahati

PINBOARD SUMMARY

Battery Model Identification and its State of Charge Estimation

Lithium-ion (Li-ion) batteries are popularity due to high power rating, high energy density, longer life cycle, better electrochemical strength, rechargeability, low weight and low maintenance. The dynamics of Li-ion battery are non-linear, the exact modeling is a challenging task. Of late, different equivalent circuit models (ECMs) have been used to represent the electrochemical model of Li-ion batteries. Such models result in computational efficiency and structural simplicity leading to an easy real-time implementation. Some of the ECMs include Rint model, Thevenin model and RC model etc. In order to ensure a reliable performance of hybrid electric vehicle (HEV), a precise estimation of state of charge (SOC) in Li-ion battery is a crucial step, wherein the exact knowledge of battery model parameters is essential. Some of the commonly employed methods for SOC estimation include ampere-hour (Ah) counting approach, open circuit voltage (OCV) method and artificial neural networks.

We have proposed a novel online structure for an integrated parameter extraction and state of charge estimation for Li-ion batteries. A state space based relay feedback approach for parameter extraction of Li-ion battery is utilized for exact model identification. The identification method is capable of combating the ill effects of sensor noise yielding an accurate estimation of battery parameters. The identification procedure has the merits over a step/pulse testing as follows; 1) it identifies process information around the important frequency, the ultimate frequency (the frequency where the phase angle is −π), 2) it is a closed-loop test therefore, the process will not drift away from the nominal operating point, 3) for processes with a long time constant, it is a more time-efficient method than conventional step or pulse testing. Further, a smooth and robust estimation of SOC is achieved using a finite time extended state observer with no chattering. Key contributions are as follows: 1) a smart online structure is proposed, yielding accurate system parameters in addition to precise SOC estimation; 2) the proposed integrated scheme is robust towards large measurement noise leading to a bounded estimation error, without the requirement of the signal averaging technique; 3) this scheme is devoid of any current sensor usage, merely the voltage available at the output terminals of the battery is utilized in entire SOC estimation process. 4) no persistence of excitation required.

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