AI-Based Should-Cost Modeling for Customized Automotive Motors

Abstract

The automotive industry in the electric vehicles (EV) sector has been moving towards customization and small-batch production to respond to consumers' demand for custom vehicles. The way current cost modeling fits into the situation of tailored or small-volume automotive motors complicates the cost estimation and cannot be surmounted using traditional methods since the difficulty lies in the complex and variable parts of the automotive application, which traditional methods of handling cannot alleviate. This paper aims to evaluate the use of Artificial Intelligence (AI) and Machine Learning (ML) to model the costs of automotive motors and assess their ability to predict cost variability and risk introduced by existing complexities. The study also examines these approaches' accuracy, efficiency, and scalability relative to traditional cost modeling techniques. The authors of this study offer a direction toward getting a future cost modeling accuracy that is neutral to the underlying code. They analyze how AI can perceive and defeat challenges related to the supply chain, such as disruptions and volatility of prices, among others. All corresponding results demonstrate that AI models outperform conventional methods in terms of accuracy, efficiency, and scalability, particularly for large datasets and motor designs that contain variables. Individuals and companies may use AI models to make predictions (and thus improve decisions) about cost fluctuations. At the end of the paper, AI-based collaborative cost modeling, motor design optimization, and predictive analytics of commodity price fluctuations are viewed as future research recommendations. These advancements bring forth several benefits, which further aid in improving cost estimation and production processes, making them more efficient and less expensive in the automotive industry.