A hybrid multi‐output‐predictive modelling based NSGA II approach for dimensions design optimization of battery pack module for electric vehicles. Issue 3 (19th January 2020)

• Record Type: Journal Article
• Authors: Ruhatiya, Chaitanya; Gia Bao, Pham N.; Quan, Tram L.; Tho, Quan T.; Xinyu, Li
• Other Names: Gao Liang guestEditor.; Garg Akhil guestEditor.
• Is Part Of: Energy storage. Volume 2:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-19
• Subjects: battery pack design -- hybrid multi‐predictive modelling -- multi‐objective optimization -- non‐dominated sorted genetic algorithm; Energy storage -- Periodicals; Energy storage; Periodicals; 621.04205
• DOI: 10.1002/est2.130
• Languages: English
• ISSNs: 2578-4862

Abstract

Abstract: Battery module safety is a major concern for the commercial success of electric vehicles (EVs). Concurrently, it is also important to have a mechanically sound and ergonomically fit battery pack design. To solve this problem, a hybrid multi‐output‐predictive modelling based NSGA II approach is proposed. In this approach, the multiple predictive modelling methods (linear regression, regression with AdaBoost, decision tree regression and multi‐layer perceptron [MLP]) are applied to predict the deformation, natural frequency and mass of battery pack enclosure. By performing the comparative analysis of these methods, the decision tree regression model was selected for deformation, MLP with tanh function for frequency and MLP with ReLU function for mass. Further, these selected models were optimized using NSGA II which resulted in optimum combination of input variables for achieving the maximum deformation (0.0019 m), minimum natural frequency (91.60 Hz), and mass (12.41 kg) simultaneously.