This study addresses the challenge of accurate and real-time battery health diagnostics in autonomous systems. Conventional voltage-based methods often fail to capture complex electrochemical dynamics

limiting their reliability in safety-critical applications. To overcome this limitation

this work proposes an integrated framework combining alternating current impedance spectroscopy with machine learning techniques for enhanced battery state estimation. Electrochemical impedance features are extracted under varying operating conditions

including state of charge

temperature

and cycling behavior

and are used as inputs to a data-driven diagnostic model. Experimental results demonstrate that the proposed method achieves a battery health prediction accuracy of 93.2%

reduces false alarm rates by ∼40% compared with traditional methods

and maintains a response time within 500 ms. Additionally

a human–machine interface is developed to translate diagnostic results into intuitive visual outputs for decision support in autonomous cockpits. The proposed approach provides a reliable and efficient solution for advanced battery management systems in intelligent transportation environments.