A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model

Wei, Zhongbao; Zhao, Jiyun; Ji, Dongxu; Tseng, King Jet

doi:10.1016/j.apenergy.2017.02.016

Cited by 279 publications

(132 citation statements)

References 44 publications

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“…Many on-line approaches have been reported to identify ECM parameters [21][22][23][24]. However, these approaches either require considerable computational overhead or incur significant cross interference between the states and parameters [21].…”

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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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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“…A BSS has a relatively high energy density and a high efficiency [10][11][12][13], which makes it an effective method to tackle the power balancing issues in a microgrid [14]. Nevertheless, a BSS faces two challenges.…”

Section: Introductionmentioning

confidence: 99%

Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime

Xiong

et al. 2018

Applied Energy

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• A new control method of the hybrid energy storage system is introduced.• The battery lifetime has been proved to be extended in the real-time experiment.• A new experiment method is proposed using the RTDS&HIL to give real-time verification.• A battery lifetime prediction method is introduced.• The RTDS&HIL scheme highlights a flexible real-time experimental approach. This study proposes a hybrid energy storage system (HESS) composed of the superconducting energy storage system (SMES) and the battery. The system is designed to compensate power fluctuations within a microgrid. A novel control method is developed to share the instantaneous power between the SMES and the battery. The new control scheme takes into account the characteristics of the components of the HESS, and the battery charges and discharges as a function of the SMES current rather than directly to the power disturbances. In this way, the battery is protected from the abrupt power changes and works as an energy buffer to the SMES. An new hardware-in-loop experiment approach is introduced by integrating a real-time digital simulator (RTDS) with a control circuit to verify the proposed hybrid scheme and the new control method. This paper also presents a battery lifetime prediction method to quantify the benefits of the HESS in the microgrid. A much better power sharing between the SMES and the battery can be observed from the experimental results with the new control method. Moreover, compared to the battery only system the battery lifetime is quantifiably increased from 6.38 years to 9.21 years.

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“…They proposed a recursive least square-based Frisch scheme to enhance the model identification and estimation of the SOC in the presence of noise. In their subsequent work, Wei et al [24] proposed a multi-timescale method for dual estimation of the SOC with the online battery model. Their results suggest that this method determines SOC and capacity with fast convergence and high accuracy.…”

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%

“…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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A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model

Cited by 279 publications

References 44 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

Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime

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

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