Novel method for the parameterization of a reliable equivalent circuit model for the precise simulation of a battery cell's electric behavior

Bruch, Maximilian; Millet, Lluís; Kowal, Julia; Vetter, Matthias

doi:10.1016/j.jpowsour.2021.229513

Cited by 46 publications

(16 citation statements)

References 40 publications

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“…Therefore, the second-order Thevenin model is proposed to cope with this problem to predict the LiC’s transient behavior [ 59 ]. This model is shown in Figure 8 h. For almost all the mentioned models, the parameters of the models significantly depend on temperature, current rate, and SoC [ 60 ]. Moreover, the model’s number of RC branches defines its complexity [ 61 ].…”

Section: 1d Electrical Thermal and Lifetime Modelingmentioning

confidence: 99%

A Comprehensive Review of Lithium-Ion Capacitor Technology: Theory, Development, Modeling, Thermal Management Systems, and Applications

et al. 2022

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This review paper aims to provide the background and literature review of a hybrid energy storage system (ESS) called a lithium-ion capacitor (LiC). Since the LiC structure is formed based on the anode of lithium-ion batteries (LiB) and cathode of electric double-layer capacitors (EDLCs), a short overview of LiBs and EDLCs is presented following the motivation of hybrid ESSs. Then, the used materials in LiC technology are elaborated. Later, a discussion regarding the current knowledge and recent development related to electro-thermal and lifetime modeling for the LiCs is given. As the performance and lifetime of LiCs highly depends on the operating temperature, heat transfer modeling and heat generation mechanisms of the LiC technology have been introduced, and the published papers considering the thermal management of LiCs have been listed and discussed. In the last section, the applications of LiCs have been elaborated.

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Section: 1d Electrical Thermal and Lifetime Modelingmentioning

confidence: 99%

A Comprehensive Review of Lithium-Ion Capacitor Technology: Theory, Development, Modeling, Thermal Management Systems, and Applications

et al. 2022

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“…22 The ECM method trains the battery's model to simulate the internal characteristics of the lithium-ion battery. [23][24][25] The traditional KF and its advanced methods used for SOC estimation are applied to all types of batteries, provided that accurate input features such as load current, voltage, and temperature values are used. 26,27 Considering the complex and dynamic operational conditions of lithium-ion batteries, the SOC is estimated accurately by the optimization of the traditional model-based methods like the KF, 28 adaptive extended Kalman filter (AEKF), 29 extended Kalman filter (EKF), 5 particle filter (PF), 30 unscented Kalman filter (UKF), 8 H-infinity, 15 etc.…”

Section: Introductionmentioning

confidence: 99%

A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

Takyi‐Aninakwa

Wang

Zhang

et al. 2022

Intl J of Energy Research

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“…The high‐speed temperature sensor is also introduced to monitor the working state of batteries with low supply sensitivity, which benefits the present application and non‐functional requirements 43 . Markov‐based state estimation is conducted to characterize the charging process, and modular‐fault tolerant processing is investigated for the mathematical state‐space Equation 44 . Various operating strategies are analyzed for the battery's redox performance with structural optimization.…”

Section: Introductionmentioning

confidence: 99%

“…43 Markov-based state estimation is conducted to characterize the charging process, and modular-fault tolerant processing is investigated for the mathematical state-space Equation. 44 Various operating strategies are analyzed for the battery's redox performance with structural optimization.…”

Section: Introductionmentioning

confidence: 99%

A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries

Wang

Fan

et al. 2022

Intl J of Energy Research

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Summary Accurate state of charge (SOC) estimation is essential for the whole‐life‐cycle safety guarantee and protection of lithium‐ion batteries, which is quite difficult to realize. In this study, a novel weighting factor‐adaptive Kalman filtering (WF‐AKF) method is proposed for the accurate estimation of SOC with a collaborative model for parameter identification. An improved bipartite electrical equivalent circuit (BEEC) model is constructed to describe the dynamic characteristics combined with the mathematical correction of the time‐varying factors. The model parameters are identified online, corresponding to various SOC levels and temperature conditions. Considering the internal resistances, ambient temperature, and complex current rate variations, an adaptive multi‐time scale iterative calculation model is constructed and combined with the real‐time estimation and correction strategies. The maximum closed‐circuit voltage (CCV) traction error is 0.36% and 0.24% for the main pulse‐current charging and discharging processes, respectively. The proposed WF‐AKF algorithm stabilizes the large initial SOC estimation error by tracking the actual value with a maximum error of 0.46% under the complex working condition. The SOC estimation is accurate and robust to the time‐varying characteristics and working conditions even when the initial error is large, providing a safety protection theory for lithium‐ion batteries.

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Novel method for the parameterization of a reliable equivalent circuit model for the precise simulation of a battery cell's electric behavior

Cited by 46 publications

References 40 publications

A Comprehensive Review of Lithium-Ion Capacitor Technology: Theory, Development, Modeling, Thermal Management Systems, and Applications

A Comprehensive Review of Lithium-Ion Capacitor Technology: Theory, Development, Modeling, Thermal Management Systems, and Applications

A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries

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