Integrating Artificial Intelligence Management into Mechatronic Systems for Next-Generation Automotive Control Architectures

The integration of artificial intelligence management into mechatronic systems represents a paradigm shift in automotive control architectures, enabling unprecedented levels of autonomy, efficiency, and adaptability. This article investigates the convergence of AI technologies, particularly machine learning, deep learning, and reinforcement learning, with traditional mechatronic systems to create next-generation automotive control frameworks. Current literature demonstrates significant progress in sensor fusion, predictive maintenance, and adaptive control strategies, but still reveals gaps in holistic system integration and real-time decision-making capabilities. This research uses a mixed approach, combining simulation-based modeling with case study analysis of contemporary automotive systems. The study examines the implementation of AI-based predictive control in electric vehicle battery management systems as a practical application. The results indicate that AI-enhanced mechatronic architectures can increase energy efficiency by 18-23%, reduce system response time by 35%, and improve fault detection accuracy to 94%. From a management perspective, the findings suggest that organizations should adopt agile development frameworks and invest in cross-functional teams that combine AI expertise with automotive engineering knowledge. This article contributes to this field by proposing an integrated framework for AI-based mechatronic systems that addresses both technical implementation requirements and organizational change management.