On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis

Wang, Limei; Pan, Chaofeng; Liu, Liang; Yong, Cheng; Zhao, Xinbing

doi:10.1016/j.apenergy.2016.01.125

Cited by 205 publications

(60 citation statements)

References 24 publications

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“…To ensure safe and reliable operation, an effective battery management system (BMS) is required to monitor and control these cells. Much of the BMS functionalities, such as the state of charge (SOC) estimation, state of health estimation, cell monitoring and balancing techniques [3][4][5][6][7][8], have been sophisticatedly developed for applications. Nevertheless, due to the nonlinear battery characteristics and unpredictable operating conditions, accurate and reliable battery state of energy (SOE) and maximum available energy estimations still pose significant challenges.…”

Section: Introductionmentioning

confidence: 99%

Novel methods for estimating lithium-ion battery state of energy and maximum available energy

et al. 2016

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h i g h l i g h t sStudy on temperature, current, aging dependencies of maximum available energy. Study on the various factors dependencies of relationships between SOE and SOC. A quantitative relationship between SOE and SOC is proposed for SOE estimation. Estimate maximum available energy by means of moving-window energy-integral. The robustness and feasibility of the proposed approaches are systematic evaluated. Keywords: Battery management system (BMS) State of energy (SOE) State of charge (SOC) Maximum available energy a b s t r a c tThe battery state of energy (SOE) allows a direct determination of the ratio between the remaining and maximum available energy of a battery, which is critical for energy optimization and management in energy storage systems. In this paper, the ambient temperature, battery discharge/charge current rate and cell aging level dependencies of battery maximum available energy and SOE are comprehensively analyzed. An explicit quantitative relationship between SOE and state of charge (SOC) for LiMn 2 O 4 battery cells is proposed for SOE estimation, and a moving-window energy-integral technique is incorporated to estimate battery maximum available energy. Experimental results show that the proposed approaches can estimate battery maximum available energy and SOE with high precision. The robustness of the proposed approaches against various operation conditions and cell aging levels is systematically evaluated.

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Section: Introductionmentioning

confidence: 99%

Novel methods for estimating lithium-ion battery state of energy and maximum available energy

et al. 2016

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“…In [91,92] and [93] the authors propose a novel method for the estimation of battery SoH based on differential voltage (DV) curves. The main idea of this method is to find the inflection points of the DV curve.…”

Section: Other Soh/rul Estimation Methodsmentioning

confidence: 99%

Battery state of health estimation: a structured review of models, methods and commercial devices

Ungurean

Cârstoiu

Micea

et al. 2016

Int. J. Energy Res.

181

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Summary Estimating the dynamic status parameters of a battery, such as its state of health (SoH) and remaining useful life (RUL), is still a very difficult and complex task. In this paper we perform a structured review of the most relevant state of the art models, algorithms and commercial devices employed in the estimation of the SoH/RUL battery performance figures, in the context of embedded applications. The models and estimation techniques are thoroughly classified and, for each taxonomy class, a presentation of the working principles is made. A comprehensive set of metrics is then introduced for the evaluation of the SoH/RUL estimation techniques from the perspective of their implementation and operation efficiency in embedded systems. These algorithms are then analyzed and discussed in a comparative manner, with concrete figures and results. The capability and the performance of the different types of off‐the‐shelf fuel gauges to estimate the battery SoH/RUL parameters are also evaluated in this paper. Copyright © 2016 John Wiley & Sons, Ltd.

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“…In applications, battery cells are often connected in series and in parallel to form a battery pack to meet the requirements of voltage and energy level in EVs. 30,31 Due to the inconsistent manufacturing processes and the different operating temperatures, the State-of-Health (SOH) of the cells in a battery pack are different. The influence of SOH on the characteristic of EIS must be considered to ensure the accuracy of internal temperature estimation in a battery pack.…”

Section: Contribution Of the Papermentioning

confidence: 99%

Instantaneous estimation of internal temperature in lithium‐ion battery by impedance measurement

et al. 2020

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Summary Due to the various drawbacks of collecting temperature using embedded or patch thermocouple sensor, the internal temperature estimation is getting more and more attention in the field of lithium power battery. In this paper, the commercial 18650 LiFePO4 battery is selected to analyze the characteristic of Electrochemical Impedance Spectroscopy (EIS) from 0°C to 55°C of 0.1 to 10 000 Hz. The results reveal that there exists intrinsic relationship between the alternating current (AC) impedance phase shift and the internal temperature in the range of 10 to 100 Hz from 5 to 55°C. And the intrinsic relationship is not interfered with the State‐of‐Charge (SOC) and the State‐of‐Health (SOH). Subsequently, the relationship is described with a modified Arrhenius equation under the excitation frequency of 12, 44, and 79 Hz. Finally, a novel internal temperature estimation method is proposed by the AC impedance phase shift. The applicability and accuracy of the method are further verified via 10 temperature points. The results indicate that the estimation error is within 1°C in the common operating temperature range (15‐45°C), suggesting that the proposed method can be applied to estimate battery internal temperature. Finally, the implementation system of real‐time estimation for engineering application is constructed.

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On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis

Cited by 205 publications

References 24 publications

Novel methods for estimating lithium-ion battery state of energy and maximum available energy

Novel methods for estimating lithium-ion battery state of energy and maximum available energy

Battery state of health estimation: a structured review of models, methods and commercial devices

Instantaneous estimation of internal temperature in lithium‐ion battery by impedance measurement

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