The increasing demand for clean energy production has driven scientists and researchers to develop innovative and eco-friendly solutions that promote greener practices in the industry. Lithium-ion batteries have played a crucial role in energy storage due to their high efficiency, reliability, and seamless integration with renewable energy sources, making them a key component in sustainable power generation. Among these, lithium iron phosphate batteries stand out for their enhanced safety, longer lifespan, thermal stability, and environmental friendliness, making them an ideal choice for applications such as electric vehicles and grid storage. As artificial intelligence becomes increasingly integrated into the energy sector, its predictive capabilities are being leveraged to analyze and optimize lithium iron phosphate battery performance. A key aspect of this optimization is the estimation of State of Health, which is essential for accurate lifetime prediction and effective battery management. However, a major challenge with artificial intelligence, particularly deep learning models such as neural networks, is their black-box nature, making them difficult to interpret. Physics-informed neural networks offer a promising solution by embedding fundamental physical laws into the learning process, enhancing model interpretability while ensuring consistency with known battery dynamics. This paper proposes an interpretable Physics-informed neural network framework with a tailored loss function specifically designed for lithium iron phosphate batteries, enabling more accurate State of Health estimation, predictive maintenance, and performance optimization while maintaining transparency in decision-making. The proposed Physics-informed approach aligns with the global effort toward cleaner energy storage and responsible resource utilization.