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Rechargeable energy storage systems with Lithium-ion pouch cells are subject to various ambient temperature conditions and go through thousands of charge-discharge cycles during the life time of operation. The cells may change their thickness with internal heat generation, cycling and any other mechanisms. The stacked prismatic cells thus experience face pressure and this could impact the pack electrical performance. The pack consists of stiff end plates keeping the pack in tact using bolts, cooling fins to maintain cell temperature and foam padding in between cells. The pack level thermal requirements limit the amount of temperature increase during normal operating conditions. Similarly, the structural requirements state that the stresses and the deflection in the end plates should be minimal. Uncertainties in cell, foam mechanical and thermal properties might add variation to the pack performance.

This paper proposes a methodology to estimate errors in computational models and to include them in reliability-based design optimization (RBDO). Various sources of uncertainties, errors and approximations in model form selection and numerical solution are considered. The solution approximation error is quantified based on the model itself while the model form error is quantified based on the comparison of model prediction with physical observations using an interpolated resampling approach. The error in reliability analysis is also quantified and included in the RBDO formulation. The proposed methods are illustrated through numerical examples.

This paper investigates a wide range of statistical methods for application in model validation under uncertainty. Hypothesis testing methods are explored first and an interval-based testing is found to be more practically useful for model validation than the commonly used point null hypothesis testing. Also, a more direct approach is proposed by formulating model validation as a reliability estimation problem. The proposed methods are illustrated and compared using numerical examples.

This paper proposes new methods to assess the validity of reliability prediction models through a Bayesian approach. The concept of Bayesian hypothesis testing is extended to system-level problems where full-scale testing is impossible. Component-level validation results are used to derive a system-level validation measure. This derivation depends on the knowledge of interrelationships between component modules. Bayes networks are used for the propagation of validation information from the component -level to system-level. Validation of reliability prediction model for a single degree of freedom oscillator under high-cycle fatigue and fatigue life prediction of a helicopter rotor hub is illustrated for this purpose.