Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer

Wei, Zhongbao; Meng, Shujuan; Xiong, Binyu; Ji, Dongxu; Tseng, King Jet

doi:10.1016/j.apenergy.2016.08.103

Cited by 160 publications

(71 citation statements)

References 40 publications

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“…[21] Metallic lithium was initially used as an anode for LIBs, owing to its very high theoreticalc apacity of about 3840 mA hg À1 and lowr eductionp otential. [21] Metallic lithium was initially used as an anode for LIBs, owing to its very high theoreticalc apacity of about 3840 mA hg À1 and lowr eductionp otential.…”

Section: Mof-derived Nanostructures For Libsmentioning

confidence: 99%

Recent Progress in Metal–Organic Frameworks and Their Derived Nanostructures for Energy and Environmental Applications

et al. 2017

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Metal-organic frameworks (MOFs), as a very promising category of porous materials, have attracted increasing interest from research communities due to their extremely high surface areas, diverse nanostructures, and unique properties. In recent years, there is a growing body of evidence to indicate that MOFs can function as ideal templates to prepare various nanostructured materials for energy and environmental cleaning applications. Recent progress in the design and synthesis of MOFs and MOF-derived nanomaterials for particular applications in lithium-ion batteries, sodium-ion batteries, supercapacitors, dye-sensitized solar cells, and heavy-metal-ion detection and removal is reviewed herein. In addition, the remaining major challenges in the above fields are discussed and some perspectives for future research efforts in the development of MOFs are also provided.

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Section: Mof-derived Nanostructures For Libsmentioning

confidence: 99%

Recent Progress in Metal–Organic Frameworks and Their Derived Nanostructures for Energy and Environmental Applications

et al. 2017

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“…However, the SOC and capacity determination is still a challenging task because it is difficult to measure it with sensors. Recently, Wei and coauthors [23][24][25] presented recursive online models to estimate the SOC of a lithium-ion battery. Wei et al [23] indicated the problem of biased results for the estimation of the SOC using conventional identification methods in the model-based charging technique.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Wei and coauthors [23][24][25] presented recursive online models to estimate the SOC of a lithium-ion battery. Wei et al [23] indicated the problem of biased results for the estimation of the SOC using conventional identification methods in the model-based charging technique. They proposed a recursive least square-based Frisch scheme to enhance the model identification and estimation of the SOC in the presence of noise.…”

Section: Introductionmentioning

confidence: 99%

“…For the charging of lithium-ion batteries, different charging techniques, such as constant current and constant voltage (CC/CV), pulse charging (PC) algorithm, model-based charging, and multistage current charging algorithm (MSCC), have been reported [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The CC/CV techniques is computationally efficient and can be implemented easily.…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

et al. 2018

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Abstract:The lithium-ion battery has high energy and power density, long life cycle, low toxicity, low discharge rate, more reliability, and better efficiency compared to other batteries. On the other hand, the issue of a reduction in charging time of the lithium-ion battery is still a bottleneck for the commercialization of electric vehicles (EVs). Therefore, an approach to charge lithium-ion batteries at a faster rate is needed. This paper proposes an efficient, real-time, fast-charging methodology of lithium-ion batteries. Fuzzy logic was adopted to drive the charging current trajectory. A temperature control unit was also implemented to evade the effects of fast charging on the aging mechanism. The proposed method of charging also protects the battery from overvoltage and overheating. Extensive testing and comprehensive analysis were conducted to examine the proposed charging technique. The results show that the proposed charging strategy favors a full battery recharging in 9.76% less time than the conventional constant-current-constant-voltage (CC/CV) method. The strategy charges the battery at a 99.26% state of charge (SOC) without significant degradation. The entire scheme was implemented in real time, using Arduino interfaced with MATLAB TM Simulink. This decrease in charging time assists in the fast charging of cell phones and notebooks and in the large-scale deployment of EVs.

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“…Furthermore, model-based methods are insensitive to the initial SOC and measurement noise. Kalman filter [16][17][18], H infinity filter [19,20], PI-observer [21], Particle filter [22], RTLS-based observer [23], FBCRLS-based observer [24] etc. are applied to calculate the correcting gain in the SOC estimation structure.…”

Section: Introductionmentioning

confidence: 99%

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

et al. 2018

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State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.

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Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer

Cited by 160 publications

References 40 publications

Recent Progress in Metal–Organic Frameworks and Their Derived Nanostructures for Energy and Environmental Applications

Recent Progress in Metal–Organic Frameworks and Their Derived Nanostructures for Energy and Environmental Applications

A Real-Time Simulink Interfaced Fast-Charging Methodology of Lithium-Ion Batteries under Temperature Feedback with Fuzzy Logic Control

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

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