Estimation of state of charge for lithium-ion batteries - A Review

A.M.S.M.H.S.Attanayaka,; Karunadasa, J.P.; Hemapala, K.T.M.U.

doi:10.3934/energy.2019.2.186

Cited by 46 publications

(9 citation statements)

References 54 publications

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“…This allows the ability to avoid being dependent on the algorithm of SOC estimation implemented in the system by the manufacturer, which is different for each technology provider and often proprietary (i.e., unknown). Furthermore, it is recognized that a dynamic (online) estimation of SOC hardly reaches the accuracy of estimation via OCV achieved after a convenient relaxation time [43][44][45], and the SOC-OCV curve is used as a reliable reference in most methods [46,47]. Therefore, in the framework of this study, the SOC-OCV curve built as follows is used as an estimator of real SOC to be compared with SOC estimated by the model.…”

Section: The Experimental Proceduresmentioning

confidence: 99%

Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis

et al. 2019

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The interest in modeling the operation of large-scale battery energy storage systems (BESS) for analyzing power grid applications is rising. This is due to the increasing storage capacity installed in power systems for providing ancillary services and supporting nonprogrammable renewable energy sources (RES). BESS numerical models suitable for grid-connected applications must offer a trade-off, keeping a high accuracy even with limited computational effort. Moreover, they are asked to be viable in modeling for real-life equipment, and not just accurate in the simulation of the electrochemical section. The aim of this study is to develop a numerical model for the analysis of the grid-connected BESS operation; the main goal of the proposal is to have a test protocol based on standard equipment and just based on charge/discharge tests, i.e., a procedure viable for a BESS owner without theoretical skills in electrochemistry or lab procedures, and not requiring the ability to disassemble the BESS in order to test each individual component. The BESS model developed is characterized by an experimental campaign. The test procedure itself is framed in the context of this study and adopted for the experimental campaign on a commercial large-scale BESS. Once the model is characterized by the experimental parameters, it undergoes the verification and validation process by testing its accuracy in simulating the provision of frequency regulation. A case study is presented for the sake of presenting a potential application of the model. The procedure developed and validated is replicable in any other facility, due to the low complexity of the proposed experimental set. This could help stakeholders to accurately simulate several layouts of network services.

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Section: The Experimental Proceduresmentioning

confidence: 99%

Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis

et al. 2019

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“…On the other hand, the regression type of approaches which are mostly based on least squares (LS) is more widely used because of their lower computing effort. The researches [21], [22], the LS moving window was used to update the first-order RC model parameters and estimate the SOC with high accuracy. In contrast to the batch calculation features of LS, the recursive least squares (RLS) method [23] has been most widely used to identify LIB model parameters due to its reasonable computational requirements and recursive calculation framework.…”

Section: Introductionmentioning

confidence: 99%

Online parameter identification for equivalent circuit model of lithium-ion battery

Trung

Diệp

2023

IJEECS

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Parameter identification is the most fundamental task for the model-based battery management system. However, there are some difficulties in completing this task since most of the existing methods require at least one known parameter or a time-consuming offline procedure to extract parameters from measured data. Based on the well-known thevenin equivalent circuit for battery, this paper determines the unique purpose is introducing the bounded varying forgetting factor recursive least square approach which identifies online all the parameters of the battery model at the same time. The precision of the proposed method is verified by simulation with the error converged to zero and the maximum error less than 1% of the nominal value.

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“…KF is a set of recursive equations that consists of two steps: (a) a prediction step that forecasts the system output, state, and error; and (b) a correction step that modifies the present state estimate value in light of the system output value. The system's accuracy is heavily dependent on the properties of the LIB equivalent circuit, which are computed both offline and online 18 and then supplied into the ECM. The indirect technique for SOC estimation includes the support vector machine method (SVMM), the neural network method (NNM), and the Kalman filter method (KF).…”

Section: Introductionmentioning

confidence: 99%

“…In general, there are several different kinds of SOC estimating techniques described in the literature, including direct measurement methods, bookkeeping methods, indirect measurement methods or adaptive approaches, and hybrid methods. 9,10 The terminal voltage method, impedance spectroscopy methods, and open-circuit voltage (OCV) methods are all examples of direct measuring techniques. 11 The OCV method is a well-known approach for estimating SOC.…”

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confidence: 99%

State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

2022

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Lithium‐ion batteries (LIBs) are currently the most widely used energy storage technologies in electric vehicles (EVs) due to their higher power density, greater energy density, longer life, fast charging capabilities, and low discharge rate. The brain of rechargeable batteries is referred to be the battery management system (BMS). The operation of the battery is monitored & regulated by an electronic system called BMS. Most significantly, it prevents the battery from exceeding its safety limits. One of the important parameters of BMS is the state of charge (SOC); it determines the battery's remaining capacity of accessible stored energy, battery lifespan, and cell balancing. As a result, an accurate SOC calculation is essential for BMS, and it is used for safer operations that avoid overcharging and discharging for the optimum battery life. The primary ambition of this article is to find an accurate and robust SOC estimation based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques. RMS error, average error, minimum error, and maximum error are used to compare the EKF method and UKF method. Two resistor‐capacitor (2RC) equivalent circuit model has been considered for the dynamic performance of the battery. The waveforms are displayed, and errors are simulated for SOC estimate using the EKF and UKF algorithms, taking the same initial SOC into consideration at a time. The UKF algorithm outperforms the EKF approach in terms of dynamic results when comparing the two methods.

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Estimation of state of charge for lithium-ion batteries - A Review

Cited by 46 publications

References 54 publications

Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis

Modeling a Large-Scale Battery Energy Storage System for Power Grid Application Analysis

Online parameter identification for equivalent circuit model of lithium-ion battery

State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

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