Adaptive state of charge (SOC) estimation for batteries with parametric uncertainties

Chaoui, Hicham; Sicard, Pierre; Ndjana, Herve Joel Nanga

doi:10.1109/aim.2010.5695870

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…46 In particular, the battery throughput is revised to reflect the number of cycles, DOD, and battery capacity. The so-called pre-exponential factor B is updated, as in Equation (18). Since neglecting the SOC reduces the accuracy, the general expression of the cycle-dependent capacity degradation was determined by Wang et al 46 as in Equation (19).…”

Section: Battery Aging Equationsmentioning

confidence: 99%

“…The equations characterize the Joule heating and heat caused by the entropy change by considering the battery heat conduction resistance (R C ), battery surface (C S ), and internal (C C ) capacity. Half of the sum of T C (t) and T S (t) represents the radial mean temperature of the Li-ion battery at time t. 14,15 Other circuit approaches are Randle, 16 dynamic, 17,18 and RC [19][20][21][22] battery models. The RC model was used by Babazadeh and Khiabani, 23 which gave successful results for Li-ion and lead-acid.…”

Section: Battery Modelsmentioning

confidence: 99%

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A comprehensive review of the aging mechanism and degradation costs of fresh and second‐life batteries based on analytical and deterministic methods

Terkes,

Demirci,

Gokalp

2024

Energy Storage

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Batteries' aging evolution and degradation functions may vary depending on the application area and various stress factors. Studies on its aging characteristics are ongoing, considering the unpredictable tendency of battery degradation during first and secondary usage periods. Battery degradation directly affects operating costs and prevents many stakeholders from making reliable short‐ or long‐term investment plans. Thus, this review study first introduces the battery models commonly used by researchers and provides an overview of the aging mechanism and estimation methods for health status and remaining capacity. Analytical and deterministic aging/degradation functions/models proposed by the researchers are discussed in detail, and cost equations based on degradation are reviewed. This approach was followed for the fresh and second‐life batteries by further investigating the impact of stress factors on the aging process and cost. The details of aging prediction approaches based on traditional methods, machine learning, and artificial intelligence are out of the scope of this review article. Discussing the shortcomings of aging analyses/functions and introducing different perspectives on the degradation characteristics will help researchers and provide a roadmap for many stakeholders.

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Section: Battery Aging Equationsmentioning

confidence: 99%

Section: Battery Modelsmentioning

confidence: 99%

A comprehensive review of the aging mechanism and degradation costs of fresh and second‐life batteries based on analytical and deterministic methods

Terkes,

Demirci,

Gokalp

2024

Energy Storage

View full text Add to dashboard Cite

show abstract

“…However, it requires the knowledge of battery's parameters, which results in accuracy reduction as batteries age. This drawback has been overcome in [18], where an adaptive SOC estimation strategy is proposed for leadacid batteries. Particle filter (PF) is a sequential Monte Carlo method that uses weighted random samples (particles) to estimate the probability distribution function of any nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive State of Charge Estimation of Lithium-Ion Batteries With Parameter and Thermal Uncertainties

Chaoui

Gualous

2017

IEEE Trans. Contr. Syst. Technol.

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In this brief, an adaptive state of charge (SOC) estimation strategy is proposed for lithium-ion batteries. The proposed methodology makes use of adaptive control theory to track online parameter variation. The convergence and stability of the proposed estimator are guaranteed by Lyapunov's direct method as opposed to many existing procedures. Since temperature variations introduce a drift in the battery's parameters, a compensation methodology is also proposed to cope with this effect. Therefore, robustness to both parameter and temperature variations is obtained, which yields precise SOC estimation. The proposed estimation scheme is validated through a set of experiments under different currents and temperatures. The experimental results reveal high performance in determining the SOC with high accuracy.Index Terms-Lithium-ion batteries, Lyapunov stability, parameter estimation, state of charge (SOC). 1063-6536

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Adaptive Fuzzy Logic Control for a Class of Unknown Nonlinear Dynamic Systems with Guaranteed Stability

Chaoui

Gualous

2017

J Control Autom Electr Syst

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Adaptive state of charge (SOC) estimation for batteries with parametric uncertainties

Cited by 12 publications

References 11 publications

A comprehensive review of the aging mechanism and degradation costs of fresh and second‐life batteries based on analytical and deterministic methods

A comprehensive review of the aging mechanism and degradation costs of fresh and second‐life batteries based on analytical and deterministic methods

Adaptive State of Charge Estimation of Lithium-Ion Batteries With Parameter and Thermal Uncertainties

Adaptive Fuzzy Logic Control for a Class of Unknown Nonlinear Dynamic Systems with Guaranteed Stability

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