Parameter identification of lithium-ion battery pseudo-2-dimensional models using genetic algorithm and neural network cooperative optimization

Kim, Jungsoo; Chun, Huiyong; Baek, Jongchan; Han, Soohee

doi:10.1016/j.est.2021.103571

Cited by 57 publications

(5 citation statements)

References 32 publications

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“…Some parameters that determine the output characteristics of the model exhibit sensitivity to the long‐term aging scale, and they partially reflect the evolution of material and cell‐level aging. [ 212 ] At different aging stages, the dominant capacity loss parasitic reactions may be different or the degree of the reactions might fluctuate; consequently, the model aging parameters, including the kinetic parameters of the side reactions, should be updated in a timely manner in the prediction during the cell's full lifespan, as depicted in Figure 10 under “Accurate degradation modeling”. The resulting set of parameters would more accurately reflect the actual state of cell aging.…”

Section: Discussionmentioning

confidence: 99%

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review

Ding,

Li,

Dai

et al. 2023

Advanced Energy Materials

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Precise prediction of lithium‐ion cell level aging under various operating conditions is an imperative but challenging part of ensuring the quality performance of emerging applications such as electric vehicles and stationary energy storage systems. Accurate and real‐time battery‐aging prediction models, which require an exact understanding of the degradation mechanisms of battery components and materials, could in turn provide new insights for materials and battery basic research. Furthermore, the primary barrier to meaningful artificial intelligence/machine learning for accelerating the prediction period is the exploitation of accurate aging mechanistic descriptors. This review comprehensively summarizes the evolution of deterioration mechanisms at the material and cell level in different environments and usage scenarios, including the intricate relationships between aging mechanisms, degradation modes, and external influences, which are the cornerstones of modeling simulation and machine learning techniques. Recent advances in electrochemical models coupled with internal battery degradation mechanisms as well as identification and tracking of aging parameters are shown, with particular emphasis on electrode balance and the anticipated trend of machine learning‐assisted reliable remaining useful life prediction. Precise simulation prediction of cell level aging will continue to play an essential role in advanced smart battery research and management, enhancing its performance while shortening experimental sequences.

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Section: Discussionmentioning

confidence: 99%

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review

Ding,

Li,

Dai

et al. 2023

Advanced Energy Materials

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“…Nonetheless, the number of RC branches employed determines how accurate this model is; however, the model becomes increasingly difficult for users when the number of RC branches is increased to attain accuracy, affecting parameter identification and SOC estimation [45]. Parameter identification interests many academics due to its significance for various model-based SOC estimations [46,47], such as the neural network, genetic algorithm [48], optimization algorithm, and least square identification [49]. Optimization algorithms are better suited for parameter identification than neural network algorithms due to their simplicity and ease of setting.…”

Section: Literature Reviewmentioning

confidence: 99%

Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

et al. 2023

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This paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures’ foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms.

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“…In the context of recent research on lithium-ion battery modeling, optimization algorithms play a crucial role in tuning the model parameters to improve the accuracy and performance of the battery's models [28]. Several optimization techniques have been employed, including gradient-based optimization [29], genetic algorithms [30], and swarm intelligence-based algorithms. Among these algorithms, Particle Swarm Optimization (PSO) [31,32] and Grey Wolf Optimizer (GWO) [33] have emerged as promising tools for optimizing model parameters in lithium-ion battery modeling.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO Algorithms

Camas-Náfate,

Coronado-Mendoza,

Vargas-Salgado

et al. 2024

Energies

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In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000.

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Parameter identification of lithium-ion battery pseudo-2-dimensional models using genetic algorithm and neural network cooperative optimization

Cited by 57 publications

References 32 publications

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review

Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review

Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO Algorithms

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