Finding a better fit for lithium ion batteries: A simple, novel, load dependent, modified equivalent circuit model and parameterization method

Hua, Xing; Zhang, Cheng; Offer, Gregory J.

doi:10.1016/j.jpowsour.2020.229117

Cited by 42 publications

(22 citation statements)

References 47 publications

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“…The longer the prediction window, the worse the model accuracy. Moreover, online techniques treat battery dynamics under excitation and during relaxation in a mutual time constant, while the research in [83] discovered that a battery will show a noticeably distinction in time constants between these two operations. The experimental results demonstrate a compromised performance in battery voltage prediction, using a mutual time constant.…”

Section: Parameter Identification Techniquesmentioning

confidence: 99%

A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

Guo

Shen

2021

Vehicles

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With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent circuit model (ECM)-based methods are considered as the mainstream technique for online SOP estimation. They primarily vary in their basic principle, technical contribution, and validation approach, which have not been systematically reviewed. This paper provides an overview of the improvements on ECM-based online SOP estimation methods in the past decade. Firstly, online SOP estimation methods are briefed, in terms of different operation modes, and their main pros and cons are also analysed accordingly. Secondly, technical contributions are reviewed from three aspects: battery modelling, online parameters identification, and SOP estimation. Thirdly, SOP testing methods are discussed, according to their accuracy and efficiency. Finally, the challenges and outlooks are presented to inspire researchers in this field for further developments in the future.

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Section: Parameter Identification Techniquesmentioning

confidence: 99%

A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

Guo

Shen

2021

Vehicles

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“…Whereas, electrochemical, and even single particle models require substantial amounts of data and computation time. There have been recent efforts related to coming up with SoC dependent Li-ion battery models [19]- [21]. However, such efforts use a simplified model of a Li-ion battery with only one time constant, which may be limiting in terms of modeling transient voltage dynamics.…”

Section: Introductionmentioning

confidence: 99%

Real Time Li-Ion Battery Bank Parameters Estimation via Universal Adaptive Stabilization

Mukhopadhyay

Usman

Rehman

2022

IEEE Open J. Control. Syst.

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This paper proposes an accurate and efficient Universal Adaptive Stabilizer (UAS) based online parameters estimation technique for a 400 V Li-ion battery bank. The battery open circuit voltage, parameters modeling the transient response, and series resistance are all estimated in a single real-time test. In contrast to earlier UAS based work on individual battery packs, this work does not require prior offline experimentation or any post-processing. Real time fast convergence of parameters' estimates with minimal experimental effort enables update of battery parameters during run-time. The proposed strategy is mathematically validated and its performance is demonstrated on a 400 V, 6.6 Ah Li-ion battery bank powering an induction motor driven prototype electric vehicle (EV) traction system.

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“…Furthermore, several studies have focused on enhancing the parameter estimation method used in equivalent circuit models. Hau et al [14] optimized passive circuit components as a function of the state of charge and different temperature levels. In addition, they were able to evaluate these parameters for different input current profiles to capture the different time constants that a lithium-ion battery cell exhibits during underload operation and compare them with the relaxation period.…”

Section: Introductionmentioning

confidence: 99%

A Novel Modal Representation of Battery Dynamics

et al. 2022

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Electrochemical cells are complicated energy storage systems with nonlinear voltage dynamics.There is a need for accurate dynamic modeling of the battery system to predict its behavior over time when discharging. The study conducted in this paper developed an intuitive model for electrochemical cells based on a simple mechanical analogy. A three-degree-of-freedom, spring-mass-damper system is decomposed into modal coordinates that represent the overall discharge, mass transport, and double-layer effect of the electrochemical cell. The developed model was experimentally demonstrated through pulsed discharge tests of commercially available lithium-ion and nickel metal hydride cells. The modal parameters of the natural frequency and damping ratio for each mode were determined by numerically minimizing the error in the time responses. Additionally, the mechanical analog was applied to two datasets [23,24]. The first dataset was used to optimize the modal parameters whereas the second dataset was utilized to validate the tuned parameters. It was found that the modal representation of the mechanical analog could accurately predict the time-response dynamics of all the cells considered. Additionally, by considering the discharge modal coordinate, the open-circuit voltage was determined and validated to that measured experimentally from the voltage relaxation peaks.

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Finding a better fit for lithium ion batteries: A simple, novel, load dependent, modified equivalent circuit model and parameterization method

Cited by 42 publications

References 47 publications

A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

Real Time Li-Ion Battery Bank Parameters Estimation via Universal Adaptive Stabilization

A Novel Modal Representation of Battery Dynamics

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