Advanced Robotics and Automation for Optimised Battery Management Systems in Electric and Hydrogen-Powered Vehicles
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Abstract
The evolution of electric and hydrogen-powered vehicles necessitates sophisticated Battery Management Systems (BMS) to ensure optimal performance, safety and longevity of battery packs. This research explores the integration of robotics and automation technologies to enhance BMS functionalities. Precision robotics improve battery assembly processes, while automation enables real-time data acquisition and advanced analysis, facilitating predictive maintenance and adaptive control strategies. Key aspects, including thermal management, state-of-charge (SoC) estimation and state-of-health (SoH) monitoring, are addressed using advanced sensors and Machine Learning (ML) algorithms. The study employs ROBOGUIDE simulation software to model, simulate and optimise these technological integrations within BMS frameworks. The findings indicated significant advancements in energy management efficiency, safety protocols and battery lifespan. The research demonstrates a 20% improvement in thermal management efficiency, a 15% increase in accurate SoC estimation and a 10% enhancement in predictive maintenance capabilities, highlighting the potential for robotics and automation to revolutionise BMS performance in sustainable transportation solutions.
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