Online dynamic equalization adjustment of high-power lithium-ion battery packs based on the state of balance estimation

Wang, Shunli; Shang, Liping; Li, Zhanfeng; Deng, Hang; Li, Jianchao

doi:10.1016/j.apenergy.2016.01.013

Cited by 65 publications

(23 citation statements)

References 64 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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“…The Ah segmentation method is also used in the SOC estimation process of the power lithium-ion batteries [38]. The KF-based methods can realize the online parameter identification and correction in the iterate SOC calculation process, which is applied to the working state monitoring of the power lithium-ion battery packs that are settled in various working conditions [39]. The parameter named as State of Function (SOF) is also utilized in the associated BMS equipment, according to which the battery packs are maintained [40].…”

Section: 1working Principle Analysismentioning

confidence: 99%

A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction

Wang

Fernandez

Zou

et al. 2019

Energy

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A comprehensive working state monitoring method is proposed to protect the power lithium-ion battery packs, implying accurate estimation effect but using minimal time demand of self-learning treatment. A novel state of charge estimation model is conducted by using the improved unscented Kalman filtering method, in which the state of balance and aging process correction is considered, guaranteeing the powered battery supply reliability effectively. In order to realize the equilibrium state evaluation among the internal battery cells, the numerical description and evaluation is putting forward, in which the improved variation coefficient is introduced into the iterative calculation process. The intermittent measurement and real-time calibration calculation process is applied to characterize the capacity change of the battery pack towards the cycling maintenance number, according to which the aging process impact correction can be investigated. This approach is different to the traditional methods by considering the multi-input parameters with real-time correction, in which every calculation step is investigated to realize the working state estimation by using the synthesis algorithm. The state of charge estimation error is 1.83%, providing the technical support for the reliable power supply application of the lithium-ion battery packs.

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“…The comparative optimization methods were analyzed for the parameter identification using different Equivalent Circuit Models (ECMs) for lithium-ion batteries. 27 The related research work was concentrated on the energy transferring consumption policies 28 and the SOC determination methods were analyzed. 29 A data-driven bias-correctionbased lithium-ion battery modeling was conducted 30 and the estimation process was also investigated by using the nonlinear fractional model.…”

Section: Introductionmentioning

confidence: 99%

An improved packing equivalent circuit modeling method with the cell‐to‐cell consistency state evaluation of the internal connected lithium‐ion batteries

Wang

Shi

Fernandez

et al. 2019

Energy Science & Engineering

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The existing equivalent modeling methods reported in literature focuses mainly on the battery cells and do not take the packing consistency state into consideration, which exists on the internal connected cells of the lithium‐ion battery pack. An improved equivalent circuit model is constructed and reported in this manuscript for the first time, which can be used to characterize the working characteristics of the packing lithium‐ion batteries. A new equilibrium concept named as state of balance is proposed as well as the calculation process, which is realized by considering the real‐time detected internal battery cell voltages. In addition, this new equilibrium concept aims to obtain more information on the real‐time consistency characterization of the battery pack. The improved adaptive equivalent circuit model is investigated by using the improved splice modeling method, in which the statistical noise properties are corrected and the additional parallel resistance‐capacitance circuit is introduced. The parameter correction treatment is carried out by comparing the estimated and experimental detected closed circuit voltages. Furthermore, the tracking error is found to be 0.005 V and accounts for 0.119% of the nominal battery voltage. By taking the packing consistency state and temperature correction into consideration, the accurate working characteristic expression is realized in the improved equivalent circuit modeling process. Finally, the model proposed in this manuscript presents a great number of advantages compared to other methods reported so far, like has the high accuracy, and the ability to protect the security of the lithium‐ion battery pack in the power supply application.

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Online dynamic equalization adjustment of high-power lithium-ion battery packs based on the state of balance estimation

Cited by 65 publications

References 64 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

A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction

An improved packing equivalent circuit modeling method with the cell‐to‐cell consistency state evaluation of the internal connected lithium‐ion batteries

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