Online Model Identification and State-of-Charge Estimate for Lithium-Ion Battery With a Recursive Total Least Squares-Based Observer

Wei, Zhongbao; Zou, Changfu; Feng, Linrun; Soong, Boon‐Hee; Tseng, King Jet

doi:10.1109/tie.2017.2736480

Cited by 214 publications

(87 citation statements)

References 30 publications

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“…Compared with tracking the absolute values [28,29], this approach updates the parameters using relative changes, whereby drastic oscillations can be avoided.…”

Section: On-line Parameter Identificationmentioning

confidence: 99%

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

Xie

2018

Energies

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To achieve accurate state-of-charge (SoC) estimation for LiFePO 4 (lithium iron phosphate) batteries under harsh conditions, this paper resorts to the Peukert's law to accommodate different temperatures and load excitations. By analyzing battery heat generation and dissipation, a thermal evolution model (TEM) is elaborated and exploited for on-line parameter identification of the equivalent circuit model (ECM). Then, a SoC estimation framework is proposed based on the Adaptive Extended Kalman Filter (AEKF) algorithm. Experimental results on a LiFePO 4 pack subject to the Federal Urban Driving Schedule (FUDS) profile under different temperatures and initial states suggest that the proposed SoC estimator provides good robustness and accuracy against changing temperature and highly dynamic loads.

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“…Compared with tracking the absolute values [28,29], this approach updates the parameters using relative changes, whereby drastic oscillations can be avoided.…”

Section: On-line Parameter Identificationmentioning

confidence: 99%

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

Xie

2018

Energies

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“…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%

“…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%

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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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“…Consequently, the applicability of this method for SOC estimation in real-life applications is questionable. Wei et al [13] estimate SOC online by identifying the OCV through a multi-timescale estimator, which is efficient in the calculation. The estimated SOC relies heavily on the look-up table, but the OCV-SOC relationship is dependent on the temperature and changes while the battery is aging.…”

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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Online Model Identification and State-of-Charge Estimate for Lithium-Ion Battery With a Recursive Total Least Squares-Based Observer

Cited by 214 publications

References 30 publications

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

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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