Optimization of Battery System Development Process for Electric Vehicles

Abstract

The increasing complexity of electric vehicle (EV) battery systems demands innovative methodologies to meet evolving market requirements, regulatory constraints, and development challenges. The development of advanced battery systems is increasingly challenged by inefficiencies in traditional engineering methodologies, primarily the limitations of the conventional V-model which is mainly rigid and resource-intensive. These inefficiencies restrict flexibility, scalability and the ability to handle complex and multi-domain system interactions. The research critically examines the limitations of traditional V-model in managing varied requirements and system variants, proposing a digital transformation strategy based on virtual development. This thesis work proposes a novel and integrated framework that combines Artificial Intelligence (AI), Digital Twin (DT) technology and Model-Based Systems Engineering (MBSE) to address shortcomings in current development process. A literature-based methodology supported by industrial insights is used to analyse system architectures, variant management, and customer-centric requirements engineering. Emphasizing a shift from the physical based testing from right side of the V-model to the virtual and simulation-based left side, this study proposes an optimized V-model framework. The optimized V-model integrated with DT framework significantly reduce development time and cost by enhancing traceability and supporting modularity. Moreover, DT-assisted V-model framework enables virtual testing, simulation-driven validation and early-stage design optimization through AI and Machine Learning (ML) techniques. AI techniques including natural language processing (NLP), machine learning and generative AI are utilized to automate requirements engineering, enhance variant management and streamline simulations using data-driven reduced order models (ROMs). Surrogate models (ROM based on data driven approach) can speed up complex simulations and enhance predictive accuracy both from CAE simulation data and real-time data. The study concludes that incorporating Digital Twins (DT), Model-Based Systems Engineering (MBSE) and Artificial Intelligence (AI) enhances battery system development by addressing existing process challenges, minimizing development time and facilitating quicker adaptation to changing requirements and demands.