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This paper presents a comprehensive survey of optimization developments in various aspects of electric vehicles (EVs). The survey covers optimization of the battery, including thermal, electrical, and mechanical aspects. The use of advanced techniques such as generative design or origami-inspired topological design enables by additive manufacturing is discussed, along with sensitivity studies of battery performance with alternate materials and incorporating sustainability considerations. Strategies for battery charging/discharging and battery swapping are reviewed, taking into consideration factors such as operation, cost, battery performance, and range anxiety. Future research is suggested to address uncertainties in charging ecosystem design and incorporate both forward and inverse prediction capabilities, leveraging benefits for both the grid and individual vehicles. The optimization techniques for other EV components, such as motors, powertrains, tires, and chassis, are also discussed. Finally, this paper presents a review of the EV management, specifically the optimization of charging station, grid, and fleet management, including research on charging station construction, charging station operation strategies, and power system operation strategies. The need for further research on robustness, reliability, and sustainability is emphasized to justify the use of EVs in the future. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024., Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 22AG001, 22AG024

Asymptotic sampling is an efficient simulation-based technique for estimating small failure probabilities of structures. The concept of asymptotic sampling utilizes the asymptotic behavior of the reliability index with respect to the standard deviations of the random variables. In this method, the standard deviations of the random variables are progressively increased using a scale parameter to obtain a set of scaled reliability indices. The collection of the standard deviation scale parameters and corresponding scaled reliability indices are called support points. Then, a regression is performed using these support points to establish a relationship between the scale parameter and scaled reliability indices. Finally, an extrapolation is performed to estimate the actual reliability index. In the previous studies, the relationship between reliability indices and support points has been established using nonlinear regression. In this study, we explored the use of more advanced machine learning (e.g., Gaussian process, support vector regression) and surrogate modeling (e.g., Kriging, linear Shepard) techniques, and compared the accuracies of these techniques to that of the nonlinear regression on six benchmark problems. It is found that using nonlinear regression yields more accurate results than machine learning and surrogate modeling techniques evaluated within the scope of this study.

Asymptotic sampling is an efficient simulation-based technique for estimating small failure probabilities of structures. The concept of asymptotic sampling utilizes the asymptotic behavior of the reliability index with respect to the standard deviations of the random variables. In this method, the standard deviations of the random variables are progressively increased using a scale parameter to obtain a set of scaled reliability indices. The collection of the standard deviation scale parameters and corresponding scaled reliability indices are called support points. Then, a regression is performed using these support points to establish a relationship between the scale parameter and scaled reliability indices. Finally, an extrapolation is performed to estimate the actual reliability index. In the previous studies, the relationship between reliability indices and support points has been established using nonlinear regression. In this study, we explored the use of more advanced machine learning (e.g., Gaussian process, support vector regression) and surrogate modeling (e.g., Kriging, linear Shepard) techniques, and compared the accuracies of these techniques to that of the nonlinear regression on six benchmark problems. It is found that using nonlinear regression yields more accurate results than machine learning and surrogate modeling techniques evaluated within the scope of this study.

54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference -- 8 April 2013 through 11 April 2013-Boston, MA-98243, Reliability-based sizing optimization of a stiffened fuselage panel together with the numbers of coupon and element tests is performed for minimum lifecycle cost. The probability of passing certification test is used as the measure of reliability. Certification test evaluates the capability of the panel to sustain a two bay skin crack with broken central stiffener under ultimate load. Uncertainty in the fracture toughness prediction, variability due to the finite number of coupon tests, and uncertainties in geometry and loading conditions are studied. The effects of structural element tests on reliability are evaluated through Bayesian updating of the fracture toughness distribution. It is found that the number of coupon tests is only marginally sensitive to the weight penalty parameter, while the number of element tests is strongly dependent on it. If the weight penalty parameter increases, each pound of structural weight becomes more valuable, the designer tend to increase the additional knockdown factors to save weight and the number of element tests increases to compensate for the increased knockdown factor. © 2012 AIAA.

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 2012 -- 23 April 2012 through 26 April 2012-Honolulu, HI-98132, American Institute of Aeronautics and Astronautics (AIAA), Metal forming process involves many uncertainties that can compromise the safety and performance characteristics of the manufactured components. In this paper, the stochastic uncertainties associated with the material, process, and product are represented and propagated to process and performance responses. A finite element-based sequential coupled process-performance framework is used to simulate the forming and energy absorption responses of a thin-walled tube in a manner that both material properties and component geometry can evolve from one stage to the next for better prediction of the structural performance measures. Metamodeling techniques are used to develop surrogate models for rupture, thinning, springback, maximum crush force, mean crush force and mass. One set of metamodels relates the responses to the random variables whereas the other relates the mean and standard deviation of the responses to the selected design variables. A robust multi-objective optimization problem is formulated and solved to illustrate the methodology and the influence of uncertainties on manufacturability and energy absorption of a metallic double-hat tube. The results are compared with those of deterministic and augmented robust optimization problems. © 2012 by Ali Najafi.

The tension between occupant safety during a crash and lightweight designs continues to be an important driver of modern vehicle designs. While occupant safety may be defined and evaluated in various ways, maximising energy absorption of structural components during impact has been adopted for vehicle designs by many manufacturers. An alternative method to evaluate safety but often not directly used in the design of structural components is the use of a dummy model in the finite element (FE) simulation to estimate the forces and accelerations that would be experienced by a human in a crash environment. This paper investigates the similarities/differences between designing vehicular structural components experiencing side impacts based upon two different criteria: (1) the energy absorption of collapsed components in the absence of a dummy and (2) an injury metric-based approach with the responses of the dummy as the bases. Multi-objective optimisation methods are used with finite element analysis (FEA) in the lightweight design for side-impact crashworthiness, considering the two different criterion. The results show that the optimum designs based on the two criteria are significantly different and that the injury-based approach should be incorporated into vehicular lightweight design process when considering crashworthiness., Center for Advanced Vehicular Systems (CAVS); Mississippi State University (MSU); Department of Energy (DOE) [DEFC2606NT42755], This study was supported by the Center for Advanced Vehicular Systems (CAVS) at Mississippi State University (MSU) and the Department of Energy (DOE) under contract no. DEFC2606NT42755.

In this study, the roller and ring raceway profiles of a cylindrical roller bearing (CRB) are designed. Starting from an initial design (i.e., an existing CRB, named NJ 309 EP4), it is intended to minimize the friction moment without sacrificing the fatigue life to have an energy efficient roller bearing. The crown heights in the roller profile, and the radius of the raceway camber profile are selected as design variables. Finite element models are developed for friction moment and fatigue life estimation using ANSYS software. Various combinations of the crown height and the radius of the raceway camber profile are evaluated, and the combination leading to minimum friction moment without sacrificing the fatigue life is obtained (i.e., a redesigned CRB is obtained). Friction moment tests and fatigue tests are conducted on the redesigned CRB (named NJ 309 OP). When the test results of the existing and redesigned bearings are compared, it is found that the friction moment of the redesigned bearing is 29% lower than that of the existing bearing, while the L10 fatigue life being only 3% smaller. When the test results are evaluated statistically, it is concluded that the difference between the friction moment test results of the existing and redesigned bearings is statistically significant, whereas the difference between the fatigue test results of the existing and redesigned bearings is statistically insignificant. © 2022 Taylor ; Francis Group, LLC., Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (5180049)

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 2012 -- 23 April 2012 through 26 April 2012-Honolulu, HI-98132, American Institute of Aeronautics and Astronautics (AIAA), Metal forming process involves many uncertainties that can compromise the safety and performance characteristics of the manufactured components. In this paper, the stochastic uncertainties associated with the material, process, and product are represented and propagated to process and performance responses. A finite element-based sequential coupled process-performance framework is used to simulate the forming and energy absorption responses of a thin-walled tube in a manner that both material properties and component geometry can evolve from one stage to the next for better prediction of the structural performance measures. Metamodeling techniques are used to develop surrogate models for rupture, thinning, springback, maximum crush force, mean crush force and mass. One set of metamodels relates the responses to the random variables whereas the other relates the mean and standard deviation of the responses to the selected design variables. A robust multi-objective optimization problem is formulated and solved to illustrate the methodology and the influence of uncertainties on manufacturability and energy absorption of a metallic double-hat tube. The results are compared with those of deterministic and augmented robust optimization problems. © 2012 by Ali Najafi.

Influence diagrams (IDs) are well-known formalisms extending Bayesian networks to model decision situations under uncertainty. Although they are convenient as a decision theoretic tool, their knowledge representation ability is limited in capturing other crucial notions such as logical consistency. We complement IDs with the light-weight description logic (DL) ℰℲ to overcome such limitations. We consider a setup where DL axioms hold in some contexts, yet the actual context is uncertain. The framework benefits from the convenience of using DL as a domain knowledge representation language and the modelling strength of IDs to deal with decisions over contexts in the presence of contextual uncertainty. We define related reasoning problems and study their computational complexity.