A novel aging parameter method for online estimation of Lithium-ion battery states of charge and health

Park, Sun Woo; Lee, Hyunju; Won, Yong Sul

doi:10.1016/j.est.2022.103987

Cited by 12 publications

(3 citation statements)

References 23 publications

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“…In [18], a multivariable fractional polynomial regression approach is proposed to model the degradation of LiBs. In [19], a data-driven approach is proposed to forecast LiBs' state of health (SOH) and SOC under constant current discharge conditions. Semi-empirical aging models stand in the middle between electrochemical and datadriven models, balancing complexity, computational effort, applicability, and data requirements; furthermore, they can be applied to different types of LiBs [20].…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Electrothermal and Aging Parameter Identification for Lithium-Ion Batteries

Conte,

Giallongo,

Kaza

et al. 2024

Energies

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Modeling and predicting the long-term performance of Li-ion batteries is crucial for the effective design and efficient operation of integrated energy systems. In this paper, we introduce a comprehensive semi-empirical model for Li-ion cells, capturing electrothermal and aging features. This model replicates the evolution of cell voltage, capacity, and internal resistance, in relation to the cell actual operating conditions, and estimates the ongoing degradation in capacity and internal resistance due to the battery use. Thus, the model articulates into two sub-models, an electrothermal one, describing the battery voltage, and an aging one, computing the ongoing degradation. We first propose an approach to identify the parameters of both sub-models. Then, we validate the identification procedure and the accuracy of the electrothermal and aging models through an experimental campaign, also comprising two real cycle load tests at different temperatures, in which real measurements collected from real Li-ion cells are used. The overall model demonstrates good performances in simulating battery characteristics and forecasting degradation. The results show a Mean Absolute Percentage Error (MAPE) lower than 1% for battery voltage and capacity, and a maximum absolute error on internal resistance that is on par with the most up-to-date empirical models. The proposed approach is therefore well-suited for implementation in system modeling, and can be employed as an informative tool for enhancing battery design and operational strategies.

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Section: Introductionmentioning

confidence: 99%

Experimental Validation of Electrothermal and Aging Parameter Identification for Lithium-Ion Batteries

Conte,

Giallongo,

Kaza

et al. 2024

Energies

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“…The results indicated that the method has high estimation accuracy. Park et al 38 presented a data‐driven approach to estimate SOC and SOH through curve fitting and regression analysis. Zheng et al 39 investigated the incremental and low‐current open circuit voltage (OCV) under different temperatures and compared the estimation accuracy, robustness, and convergence time for SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms

Chen

Zhang

et al. 2022

Intl J of Energy Research

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Aiming at the problems of time-varying battery parameters and inaccurate estimations of state of charge (SOC) and state of health (SOH), a joint estimation algorithm of SOC and SOH is proposed. A particle filter algorithm is used to identify the parameters online on the basis of a second-order equivalent circuit model. The algorithm feasibility is verified through the terminal voltage estimation accuracy. Considering that an accurate SOH is one of the foundations to achieve an accurate SOC estimation, a dual particle filter is used to jointly estimate SOC and SOH. Under different test conditions, the effect of different initial values (initial SOC and capacity), temperatures, operation conditions, particle number, and model parameters on the estimation accuracy and robustness is compared and analyzed. The effectiveness of the proposed algorithm is validated by experimental data under different operation conditions. Experimental results show that the online particle filter algorithm can well predict the dynamic battery model parameters. The proposed algorithm has high robustness and a good tracking effect when estimating SOC with a mean absolute error of less than 1.3%, a root mean square error of less than 1%, and a tracking terminal voltage. K E Y W O R D Sonline joint estimation, online parameter identification, particle filter algorithm, state of charge, state of health

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“…There are two main common SOH estimation methods, one is based on an electrochemical model or equivalent circuit model, and the other is based on a data-driven approach. [12][13][14] The method based on the electrochemical model or equivalent circuit model uses the parameters identified by the model to calibrate the SOH of the lithium-ion battery. At the same time, some scholars combine parameter identification and filtering algorithms to complete SOH estimation.…”

mentioning

confidence: 99%

State-of-Health Estimation of Lithium-ion Batteries Based on Singular Value Decomposition and an Improved Group Method of Data Handling

Li,

Bai,

Yan

et al. 2024

J. Electrochem. Soc.

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Lithium-ion batteries are complex electrochemical systems, and the degradation of their state-of-health (SOH) is a nonlinear process. Accurate SOH estimation is critical to lithium-ion battery life and safety. We used a data-driven approach to study SOH estimation of lithium-ion batteries. First, we used the singular value decomposition method to extract features from the battery charging history data. Second, the particle swarm (PSO) algorithm was used to optimize the parameter configuration of the group method of data handling (GMDH). Finally, the SOH estimation wa completed using the optimized GMDH. The results show that the proposed PSO-GMDH estimation model maintains an error within 0.89% for estimating its subsequent SOH using historical data of a certain battery, and maintains an error within 0.5% for estimating the SOH of another battery of the same model using historical data of multiple batteries. At the same time, the results also show that the PSO-GMDH estimation model has higher estimation accuracy than the GMDH model without parameter optimization.

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A novel aging parameter method for online estimation of Lithium-ion battery states of charge and health

Cited by 12 publications

References 23 publications

Experimental Validation of Electrothermal and Aging Parameter Identification for Lithium-Ion Batteries

Experimental Validation of Electrothermal and Aging Parameter Identification for Lithium-Ion Batteries

Online identification of battery model parameters and joint state of charge and state of health estimation using dual particle filter algorithms

State-of-Health Estimation of Lithium-ion Batteries Based on Singular Value Decomposition and an Improved Group Method of Data Handling

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