Accurate state-of-health (SOH) estimation is crucial for the reliable operation of lithium-ion batteries in sustainable energy systems. While existing methods often require complex feature engineering or focus on specific voltage ranges, this paper presents a novel and practical approach using readily available discharge data from Battery Management Systems (BMS). We introduce a hybrid feature set that combines the strengths of incremental capacity (IC) curves and temperature generation factors, capturing critical aspects of battery degradation. By employing a two-layer feedforward Artificial Neural Network (ANN) and analyzing the complete discharge cycle, our method was tested two different dataset, achieves an average root mean square error (RMSE) of 0.38% across two diverse battery degradation datasets with minimal computational cost. This approach offers a promising solution for real-world on-board SOH estimation, enabling efficient battery management and ensuring the longevity of energy storage systems.