Online Internal Resistance Measurement Application in Lithium Ion Battery Capacity and State of Charge Estimation

Bao, Yun; Dong, Wenbin; Wang, Dian

doi:10.3390/en11051073

Cited by 65 publications

(22 citation statements)

References 22 publications

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“…This method is highly accurate at the end points of the discharge. Recently, a direct current short pulse (DCSP) method has been proposed to determine the IR [34]. The value of the resistance is very low which is challenging to measure [15].…”

Section: Internal Resistance (Ir)mentioning

confidence: 99%

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

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Section: Internal Resistance (Ir)mentioning

confidence: 99%

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

et al. 2019

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“…The literature [23] first discussed online internal resistance detection method. Secondly, established the relationship between the measured internal resistance and the LiBs capacity by linear fitting.…”

Section: Applications Of Detection Model By Fdfamentioning

confidence: 99%

Construction of an Energy Saving Model Based on FA and Its Applications for Detection of Battery Power Consumption

Wen

et al. 2020

IEEE Access

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Although some algebraic theories such as partial order set, lattice and their properties on fuzzy set had been studied, these algebraic theories on fuzzy automata (FA) have not been discussed at present. For saving the energy of the battery so as to make it play the best performance, this paper proposes first an algebraic system on a finite-state deterministic fuzzy automaton (FDFA), including the partial order set, lattice and its properties. Second, this paper constructs a detection model system to monitor the consumption of battery power, and the detection model consists of four parts, as well as including supervision function network and decision-making process. Third, this paper gives the whole dynamic process of consumption and consumption rate of battery power, as well as discusses the comparison of the detection accuracy of between the detection model and other detection methods in the battery power consumption by simulation. Through the homomorphism and the isomorphic mapping of the lattice, the partial state of the FDFA is studied to realize the function of the whole state. These research results will provide a theoretical reference, system modeling and application background for many departments such as control, detection, tracking, identification and so on. INDEX TERMS FDFA partial order set, FDFA lattice, detection model, homomorphism, isomorphism of lattice.

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“…21 The electrochemical model-based estimation algorithm was also investigated. 25 Furthermore, the polynomial equivalent circuit model was constructed 26 for the electrochemical impedance spectroscopy of the lithium-ion batteries. 23 Other research groups as well as our research team have carried out in-depth research to enhance the lithium-ion battery pack safety by using the associated BMS equipment.…”

Section: Introductionmentioning

confidence: 99%

“…The reliability of the SOC estimation method was calculated by using the smooth variable structure filter 24 and the online internal resistance measurement. 25 Furthermore, the polynomial equivalent circuit model was constructed 26 for the electrochemical impedance spectroscopy of the lithium-ion batteries. The comparative optimization methods were analyzed for the parameter identification using different Equivalent Circuit Models (ECMs) for lithium-ion batteries.…”

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 Internal Resistance Measurement Application in Lithium Ion Battery Capacity and State of Charge Estimation

Cited by 65 publications

References 22 publications

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Construction of an Energy Saving Model Based on FA and Its Applications for Detection of Battery Power Consumption

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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