<p>Carbon fiber structural batteries, as multifunctional composites integrating mechanical load-bearing, energy storage, and self-sensing capabilities, enable energy storage while maintaining load-bearing performance. By utilizing solid-state systems, they enhance operational safety, significantly improve lightweighting and space utilization, and demonstrate broad application prospects in aerospace and electric vehicles. However, these materials endure coupled effects of mechanical loading and electrochemical reactions during service. Damage not only reduces structural load-bearing capacity but may also compromise energy storage safety. Therefore, real-time, in-situ health monitoring is crucial for reliability assessment and lifespan prediction. This paper proposes an electrical resistivity tomography (ERT) method based on the intrinsic conductivity of the material, enabling structural health monitoring of carbon fiber structural batteries during energy storage. The forward problem constructs control equations by modeling the battery as an equivalent resistive network, with finite element simulation verifying its convergence. The inverse problem employs the Levenberg–Marquardt algorithm for iterative inversion. Experimental results demonstrate the method’s ability to accurately identify damaged regions under battery energy storage conditions, validating its feasibility and effectiveness. This research provides a novel approach for integrated load-bearing, energy storage, and self-monitoring applications in carbon fiber structural batteries, offering significant reference value for their engineering implementation and service safety assurance.</p>