Reliability-Based Robust Design Optimization of Lithium-Ion Battery Cells for Maximizing the Energy Density by Increasing Reliability and Robustness

Park, Jin-Hwan; Yoo, Donghyeon; Moon, Jaemin; Yoon, Janghyeok; Park, Jungtae; Lee, Seungae; Lee, Doohee; Kim, Chang-Wan

doi:10.3390/en14196236

Cited by 15 publications

(5 citation statements)

References 34 publications

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“…According to [87], the use of lithium-ion batteries (LIBs) in electric vehicles (EVs) is becoming more and more common because of their advantages. Among their most important advantages are light weight, high energy and power density.…”

Section: Qualitative and Quantitative Analysis Of Failure Types And C...mentioning

confidence: 99%

Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport

Martyushev,

Malozyomov,

Sorokova

et al. 2023

Mathematics

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Modern power and transportation systems are subject to high requirements for reliability and performance in performing their specified functions. At the same time, these requirements are constantly increasing with the increasing complexity of technology and the introduction of electronics and computer technology into its structure. This is fully applicable to energy and transportation infrastructure, including electric vehicles. The complexity of the systems and increasing requirements for them have led to the fact that the problem of increasing their operational reliability has acquired great importance. The article presents a review of methods and justification of ensuring a high level of reliability and serviceability of technical systems as one of the most important tasks in the creation and operation of complex systems, such as modern energy and transportation systems. It is shown that a significant reserve in solving the problem of increasing the reliability and performance of technical systems is the information on failures and malfunctions of these systems obtained from the field of operation. The methodology of collection and processing of statistical information on failures of vehicles described by different distribution laws is outlined.

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Section: Qualitative and Quantitative Analysis Of Failure Types And C...mentioning

confidence: 99%

Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport

Martyushev,

Malozyomov,

Sorokova

et al. 2023

Mathematics

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“…To improve the accuracy of the metamodel, the design points should be evenly distributed within the design domain [21]. In this study, the DoE was performed using the OLHD sampling technique, which provides excellent space filling with limited sampling points [22]. To evaluate the accuracy of the metamodel, the number of experiments and test points were set as 27 and 3, respectively, using Equations ( 5) and ( 6) [23].…”

Section: Design Of Experimentsmentioning

confidence: 99%

Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels

Kim

You

2022

Applied Sciences

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Permanent magnet synchronous motors (PMSMs) with rectangular coils in hairpin windings exhibit improved fill factor and reduced end turn of the coils, which in turn improve the efficiency and power density of PMSMs, making them ideal for e-mobility applications. Herein, the shape of a PMSM was optimized for torque ripple reduction using metamodels to improve the noise and vibrational performance of the motor. The objective function of the optimal design aimed to minimize the torque ripple, and the average torque and efficiency were set as constraints. The notch width and depth and barrier length were selected as the design variables to satisfy the objective function and constraints. Using the optimal Latin hypercube design technique, 27 experimental points were selected, and a finite element analysis (FEA) was performed for each point. Furthermore, a function approximation was performed using six metamodels, and the best metamodel was selected using the root mean square error test. Moreover, the optimization was performed by combining the best metamodels for each variable with a sequential two-point diagonal quadratic approximation optimization algorithm. The torque ripple was improved by approximately 1.63% compared with the initial model, whereas the constraint values remained constant. Finally, an FEA was performed on the optimal point, and the FEA results matched with those of the optimal method.

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“…, 2020). Park applied the reliability-robust design method to the design phase of automotive lithium-ion batteries, which not only reduced the failure rate of the batteries but also improved their energy density while considering the manufacturing uncertainty (Park et al. , 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Gaurav proposed a new reliability-robust design method based on the cumulative distribution function and applied it to the reliability-robust design of building structures under extreme wind excitation, which not only improved the reliability and robustness of the structure, but also achieved a better computational efficiency than the traditional design method (Gaurav et al, 2020). Park applied the reliability-robust design method to the design phase of automotive lithium-ion batteries, which not only reduced the failure rate of the batteries but also improved their energy density while considering the manufacturing uncertainty (Park et al, 2021). Most of the reliability-robust design methods involved in the above literature reduced the sensitivity of the failure probability of the structure or system to the design variables, and the commonly used reliability sensitivity analysis methods Robust optimization design method have limited applicability and require huge computational volumes when dealing with small-failure-probability problems (Zhi et al, 2020;Iason et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Robust optimization design method for structural reliability based on active-learning MPA-BP neural network

Dong

Sheng

Zhao

et al. 2023

IJSI

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PurposeMechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.Design/methodology/approachThe MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.FindingsThe prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.Originality/valueThe MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

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Reliability-Based Robust Design Optimization of Lithium-Ion Battery Cells for Maximizing the Energy Density by Increasing Reliability and Robustness

Cited by 15 publications

References 34 publications

Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport

Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport

Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels

Robust optimization design method for structural reliability based on active-learning MPA-BP neural network

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