Minimal data set. by Peijian Jin (22265437)

“…<div><p>With the rapid development of new energy vehicles and renewable energy storage systems, the safety and reliability of lithium-ion batteries have garnered significant attention. Therefore, it is crucial to study the aging and damage mechanisms of these batteries, explore effective damage monitoring and characterization methods, and improve their safety. …”

Experimental lithium-ion batteries. by Peijian Jin (22265437)

“…<div><p>With the rapid development of new energy vehicles and renewable energy storage systems, the safety and reliability of lithium-ion batteries have garnered significant attention. Therefore, it is crucial to study the aging and damage mechanisms of these batteries, explore effective damage monitoring and characterization methods, and improve their safety. …”

Schematic diagram of two time intervals. by Peijian Jin (22265437)

“…<div><p>With the rapid development of new energy vehicles and renewable energy storage systems, the safety and reliability of lithium-ion batteries have garnered significant attention. Therefore, it is crucial to study the aging and damage mechanisms of these batteries, explore effective damage monitoring and characterization methods, and improve their safety. …”

The comparison of ν_s – DTs trends models with experimental measured values for RHOB = 2.72 g/cc and DTc = 51.47 us/ft. by Fahd Saeed Alakbari (10701871)

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Error boxplot and violin graphs for the previously published and proposed GPR models. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s cross-plotting for the testing dataset. by Fahd Saeed Alakbari (10701871)

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Residual error of fracture pressure for all studied models. by Fahd Saeed Alakbari (10701871)

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Fracture pressure based on previous Poisson’s ratio models. by Fahd Saeed Alakbari (10701871)

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Group error analysis of bulk formation density for the proposed GPR and some previous approaches. by Fahd Saeed Alakbari (10701871)

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Trend analysis of the ν_s – DTs in the range found in the dataset used for the optimized GPR. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s error histogram of the training dataset. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s errors histogram of the testing dataset. by Fahd Saeed Alakbari (10701871)

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Group error analysis of shear time for the proposed GPR and some previous approaches. by Fahd Saeed Alakbari (10701871)

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The comparison of ν_s – DTc trends models with experimental measured values for RHOB = 2.27 g/cc and DTs =  248.99 us/ft. by Fahd Saeed Alakbari (10701871)

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The optimized parameters of the proposed GPR method. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s error histogram of the validation dataset. by Fahd Saeed Alakbari (10701871)

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Cross-plots of (a) the proposed GPR, Ranjbar-Karaml et al. [14], Christaras et al. [52], Feng et al. [16], Gowida et al. [17] models and (b) Khandelwal et al. [13], Brandås et al.... by Fahd Saeed Alakbari (10701871)

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Group error analysis of compressional time for the proposed GPR and some previous approaches. by Fahd Saeed Alakbari (10701871)

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The optimized GPR approach’s cross-plotting for the validation dataset. by Fahd Saeed Alakbari (10701871)

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(a) Average absolute percentage relative error (AAPRE), and, (b) Coefficient of determination (R²) of the models. by Fahd Saeed Alakbari (10701871)

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