Effective weight-reduction- and crashworthiness-analysis of a vehicle’s battery-pack system via orthogonal experimental design and response surface methodology

Mechanics-based design of lithium-ion batteries: a perspective

“…The lightweight of structural materials is a frontier issue in solid mechanics and is expected to be applied in the package and internal support materials of battery packs. 192,201,202 Thermal management of battery packs is generally related to the heat flow of the coolant, and therefore the pack-level thermal management is also expected to be designed by mechanical analysis. 200,[203][204][205] In addition, the design of crash-tolerant battery packs has also been developed by using mechanically sacrificing elements that dissipate impact energy.…”

Prediction of stamping parameters for imitation π-shaped lithium battery shells by building variable weight and threshold pelican-BP neural networks

“…During the calculation of the frequency, the two design variables in the fitting function are defined as X3 and L1 as independent variables, and the other-dimensional variables of the battery case are defined as fixed values. The pelican algorithm is used to calculate and train equation (11). In the calculation of the stress, the two design variables in the fitting function are defined as X3 and X1 as independent variables, and the other-dimensional variables of the battery case are defined as fixed values.…”

“…Ma et al used ant colony algorithm, genetic algorithm, and difference algorithm to adjust the initial threshold and optimize BP neural network for predicting the trajectory of a vessel, which solved the defects of traditional neural network such as easy to fall into local minimum and obtained more accurate prediction effect 6,7 ; Wei et al used Monte Carlo to process the parameters of input variables and used multi-objective genetic algorithm to optimize the feed-forward neural network model by calculating the optimization weights, so as to find the optimal solution and use it to predict the driving speed of excavators, which is of great practical significance 8,9 ; Li et al proposed a model based on the grey wolf algorithm to optimize BP neural networks with two design variables as input values and one objective function as output value and compared the prediction results with traditional neural network models, which showed that the optimized errors were smaller and more accurate 10,11 ; Yang et al used the additional momentum method and particle swarm optimization to train the thresholds and weights of the BP neural network model, and verified the reliability of the new network by estimating the predictions of the Kalman filter 12,13 ; to solve the problem of too many parameters, Zhang et al used genetic algorithm to normalize the data of the BP neural network before optimizing it to improve robustness and accuracy, and to control the error within 1%. 14,15 With the rise of oil prices and environmental pollution, 16,17 the development of new energy vehicles is becoming more and more important, the most critical of which is its power core: battery pack as well as the shell, from reference to several kinds of literature, we can know that the mass of most BPE is thick and heavy, which makes them useless work done by electric vehicles increase and seriously affects the improvement of the range of electric vehicles, so the design of the dimensional parameters of its BPE structure The design of the dimensional parameters of the BPE structure will be particularly necessary.…”

Research on energy-saving factors adaptability of exterior envelopes of university teaching-office buildings under different climates (China) based on orthogonal design and EnergyPlus

“…A more accurate and reliable optimization conclusion can be obtained with a fewer number of experiments after using an orthogonal design, and it allows for performing both range analysis (R) and analysis of variance (ANOVA) to evaluate test results. There is growing interest in the use of orthogonal design methods for optimization in various fields [ 28 , 51 , 52 , 53 , 54 , 55 ]. Therefore, a university teaching-office building was researched in this paper through orthogonal design and numerical simulation (EnergyPlus) to explore the adaptability (including the optimal parameters and influence degree of each factor of the envelope and the significance levels) of the energy-saving factor of the building envelopes under different climates, which aims to provide the optimal solution for the renovation of university teaching-office building envelopes under different climatic conditions in China to achieve maximum energy-saving building operations.…”