Physics-Based SoH Estimation for Li-Ion Cells

Iurilli, Pietro; Brivio, Claudio; Carrillo, Rafael E.; Wood, Vanessa

doi:10.3390/batteries8110204

Cited by 16 publications

(6 citation statements)

References 54 publications

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“…(6) Update the present optimal solution and its fitness value. (7) Repeat the above steps (3) to (6) until the iteration limit T max is attained, and output the optimal parameters to the SVR model.…”

Section: Svr Methods Based On Dbo Optimizationmentioning

confidence: 99%

“…Equivalent circuit models or electrochemical models are the primary approaches utilized in model-based methods. Reference [7] proposed a method based on Electrochemical Impedance Spectroscopy (EIS) to estimate the SOH of lithium-ion batteries. Reference [8] estimated the SOH by constructing a nonlinear equivalent circuit battery model, where the incremental state of charge (∆SOC) is proportional to the SOH within a suitable voltage range.…”

Section: Introductionmentioning

confidence: 99%

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Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR

Huang

et al. 2023

Energies

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Accurate estimation of the state-of-health (SOH) of lithium-ion batteries is a crucial reference for energy management of battery packs for electric vehicles. It is of great significance in ensuring safe and reliable battery operation while reducing maintenance costs of the battery system. To eliminate the nonlinear effects caused by factors such as capacity regeneration on the SOH sequence of batteries and improve the prediction accuracy and stability of lithium-ion battery SOH, a prediction model based on Variational Modal Decomposition (VMD) and Dung Beetle Optimization -Support Vector Regression (DBO-SVR) is proposed. Firstly, the VMD algorithm is used to decompose the SOH sequence of lithium-ion batteries into a series of stationary mode components. Then, each mode component is treated as a separate subsequence and modeled and predicted directly using SVR. To address the problem of difficult parameter selection for SVR, the DBO algorithm is used to optimize the parameters of the SVR model before training. Finally, the predicted values of each subsequence are added and reconstructed to obtain the final SOH prediction. In order to verify the effectiveness of the proposed method, the VMD-DBO-SVR model was compared with SVR, Empirical Mode Decomposition-Support Vector Regression (EMD-SVR), and VMD-SVR methods for SOH prediction of batteries based on the NASA dataset. Experimental results show that the proposed model has higher prediction accuracy and fitting degree, with prediction errors all within 1% and better robustness.

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Section: Svr Methods Based On Dbo Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR

Huang

et al. 2023

Energies

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“…used ICA-Bi-LSTM in a NASA dataset to estimate SOH [167,170]. Physics-based internal chemical changes while cycling a lithium-ion battery were observed and analyzed for SOH estimation in [171].…”

Section: Literature Review On Soh Estimation Methodsmentioning

confidence: 99%

A Systematic Literature Review of State of Health and State of Charge Estimation Methods for Batteries Used in Electric Vehicle Applications

Swarnkar,

Ramachandran,

Ali

et al. 2023

WEVJ

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In recent years, artificial intelligence and machine learning have captured the attention of researchers and industrialists in order to estimate and predict the state of batteries. The quality of data must be good, and the source of data must be the same for different models’ performance comparisons. The lithium-ion battery is popularly used because of its high energy density and its compact size. Due to the non-linear and complex characteristics of lithium-ion batteries, electric vehicle users have to know about battery health conditions. Different types of state estimation methods are used, namely, electrochemical-based, equivalent circuit model (ECM) based, and data-driven approaches. This paper is a survey of electric vehicle history, different battery chemistries, battery management system (BMS) basics and key challenges and solutions in BMS, and in-depth discussions about other battery state of charge and state of health estimation methods. Research trend analysis, critical analysis of this work, limitations, and future directions of existing works are discussed. This paper also provides information on the open-access available datasets of different battery chemistry for a data-driven approach. This paper highlights the key challenges of state estimation techniques. Knowledge of accurate battery state of charge (SOC) provides critical information about remaining available energy. In comparison, battery state of health (SOH) indicates its current health condition, remaining lifetime, performance, and proper energy management of the electric vehicles.

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“…According to the characteristics that SVM is suitable for small-scale training samples and can solve the problems of nonlinear, high dimension and local minimum points, the system uses the method of improving ampere-hour and particle swarm to estimate the battery SOC and SOH in various working modes. [5]…”

Section: Battery Soc and Soh Online Estimation Methods Based On Multi...mentioning

confidence: 99%

Integrated Online Monitoring System for Predicting Battery State Based on PSO-LSSVM Model

2023

Advances in Transdisciplinary Engineering

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Physics-Based SoH Estimation for Li-Ion Cells

Cited by 16 publications

References 54 publications

Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR

Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR

A Systematic Literature Review of State of Health and State of Charge Estimation Methods for Batteries Used in Electric Vehicle Applications

Integrated Online Monitoring System for Predicting Battery State Based on PSO-LSSVM Model

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