A Novel Fractional Order Model for State of Charge Estimation in Lithium Ion Batteries

Xiong, Rui; Tian, Jinpeng; Shen, Weixiang; Sun, Fuchun

doi:10.1109/tvt.2018.2880085

Cited by 225 publications

(97 citation statements)

References 46 publications

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“…19 The rapidly capacity degradation at low temperature also poses significant challenges to the SOC and state of health (SOH) prediction and estimation functions of BMS. 20,21 To describe the evolution law of battery degradation, some aging models have been established. Wang 27 formed a holistic aging model coupled with electrothermal model to describe calendar and cycle aging in different cycle and management strategies.…”

Section: And Wrong Judgments On Battery Operatingmentioning

confidence: 99%

“…However, the unrecoverable lithium coating continued growing on the surface of the negative solid electrolyte interphase (SEI) film, and the lithium dendrites that were fully grown on the coating would even pierce the separator under stress, causing an internal short circuit and premature failure of the battery . The rapidly capacity degradation at low temperature also poses significant challenges to the SOC and state of health (SOH) prediction and estimation functions of BMS …”

Section: Introductionmentioning

confidence: 99%

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Effect of charge rate on capacity degradation of LiFePO ₄ power battery at low temperature

Wang

2019

Int J Energy Res

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Summary Limited by the current power battery technology, electric vehicles show extremely poor duration performance and potential risk at low temperature, which is mainly caused by poor charging performance of lithium‐ion batteries. To explore the impact of charging process on cycle degradation at low temperatures, a cycle aging experimental scheme with different charging C‐rate (0.3C and 0.5C) under −10°C and −20°C was designed for the commercial LiFePO4 battery. The experimental batteries showed severe degradation after a few of cycles. The phenomenon of reduced internal resistance and up‐shift of the charging curve was found during the early cycle stages (0th‐20th cycle). The influence of low‐temperature cycle on battery was analyzed by the increment capacity analysis (ICA); the fast decreasing intensity of ①*II showed sharp loss of lithium ions. Those lithium ions mainly transformed into lithium plating and built up dendrites instead of reintercalating into the anode crystal structure, causing the further degradation of capacity and ohmic resistance. Degradation law was obtained by curve regression analysis in the end.

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Section: And Wrong Judgments On Battery Operatingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of charge rate on capacity degradation of LiFePO ₄ power battery at low temperature

Wang

2019

Int J Energy Res

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“…35,36 The purpose of battery fault diagnosis/prognosis is to detect the fault/abnormity and inform the drivers in time to avoid more serious accidents. 35,36 The purpose of battery fault diagnosis/prognosis is to detect the fault/abnormity and inform the drivers in time to avoid more serious accidents.…”

Section: Challenges For Fault Diagnosis/prognosis Of Battery Systemsmentioning

confidence: 99%

Multi‐fault synergistic diagnosis of battery systems based on the modified multi‐scale entropy

et al. 2019

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Summary Faults of lithium batteries in their early stage in electric vehicles (EVs) are usually undetectable, and their characteristics are difficult to be extracted by conventional methods. This paper presents a novel synergistic diagnosis scheme for multiple battery faults using the modified multi‐scale entropy (MMSE). The proposed MMSE can effectively extract the multi‐scale features of complex battery signals in the early stages of battery faults as well as overcome the shortage of the coarse‐grained mode in the standard multi‐scale entropy. The simulation results on experimental data and the real‐world operational vehicles show that the proposed method can effectively detect and locate multiple battery faults/abnormities before they trigger the alarm thresholds. The defined sensitivity factor can implement real‐time evaluation on abnormities with high efficiency and stability, and the developed variable‐calculation‐window diagnosis scheme can synchronously detect and locate different fault types in real time. Furthermore, feasibility, stability, reliability, versatility, robustness, and practicality of the proposed method are separately verified using multiple sets of real‐world operation data. More importantly, the proposed method also provides feasibility to effectively prevent battery thermal runaway caused by multiple battery abnormities/faults. The applications of multi‐scale entropy theory is the first of its kind to battery fault diagnosis on the real‐world operational vehicles.

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“…For example, genetic algorithms have been used to estimate the physical parameters of an electrochemical model with good accuracy . Besides, Xiong et al suggest the combination of genetic algorithm and least‐squares method to identify parameters in a novel fractional order battery model . Ramadesigan et al mathematically reformulate a porous electrode model to analyze the capacity fade of a Li‐ion battery, with parameters being estimated through a Gauss‐Newton method .…”

Section: Introductionmentioning

confidence: 99%

“…5,6 Besides, Xiong et al suggest the combination of genetic algorithm and least-squares method to identify parameters in a novel fractional order battery model. 7 Ramadesigan et al mathematically reformulate a porous electrode model to analyze the capacity fade of a Li-ion battery, with parameters being estimated through a Gauss-Newton method. 8 An extended Levenberg-Marquardt algorithm is of equivalent circuit models, whose prediction accuracy is compared thereafter.…”

Section: Introductionmentioning

confidence: 99%

A novel interval‐based approach for quantifying practical parameter identifiability of a lithium‐ion battery model

et al. 2020

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Summary Practical identifiability of battery model parameters, on which both modeling accuracy and robustness rely, is considered as a very important prerequisite for advanced onboard monitoring and control of Lithium‐ion batteries. In this paper, a novel confidence‐interval‐based approach is proposed for the quantification and assessment of the practical identifiability of a widely used second order battery equivalent circuit model (ECM). This method utilizes profile likelihood and likelihood ratio subset statistic to calculate each parameter's confidence interval, based on which a normalized index is further derived for facilitating quantification and fast comparison of the identifiability degree among different parameters. Using this approach, the practical identifiability of the second order ECM under lab‐collected experimental data is successfully evaluated, and the influences of several real‐world factors are systematically examined through extensive simulations. The results show that the open circuit voltage and ohmic internal resistance have a much larger degree of identifiability in all the investigated conditions. Some practically useful insights on performing battery parameter identification are also provided.

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A Novel Fractional Order Model for State of Charge Estimation in Lithium Ion Batteries

Cited by 225 publications

References 46 publications

Effect of charge rate on capacity degradation of LiFePO ₄ power battery at low temperature

Effect of charge rate on capacity degradation of LiFePO ₄ power battery at low temperature

Multi‐fault synergistic diagnosis of battery systems based on the modified multi‐scale entropy

A novel interval‐based approach for quantifying practical parameter identifiability of a lithium‐ion battery model

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A Novel Fractional Order Model for State of Charge Estimation in Lithium Ion Batteries

Cited by 225 publications

References 46 publications

Effect of charge rate on capacity degradation of LiFePO 4 power battery at low temperature

Effect of charge rate on capacity degradation of LiFePO 4 power battery at low temperature

Multi‐fault synergistic diagnosis of battery systems based on the modified multi‐scale entropy

A novel interval‐based approach for quantifying practical parameter identifiability of a lithium‐ion battery model

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Product

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Effect of charge rate on capacity degradation of LiFePO ₄ power battery at low temperature

Effect of charge rate on capacity degradation of LiFePO ₄ power battery at low temperature