The increasing reliance on lithium-ion batteries in Electric Vehicles (EVs) and energy storage systems demands accurate, real-time estimation of State of Charge (SOC) and State of Health (SOH). Conventional digital estimation methods such as Kalman Filters and machine-learning models provide high accuracy but require complex computation, expensive microcontrollers, and high power consumption. This project presents a hardware-efficient, analog-centric estimation system that performs both SOC and SOH analysis using low-power operational amplifier circuits. A Hybrid Coulomb Counting technique, combined with voltage-based correction, enables driftfree SOC tracking, while a pulse-based internal resistance measurement accurately detects battery degradation for SOH estimation. Cadence Virtuoso simulations validate the design's ability to distinguish healthy and degraded batteries through measurable voltage sag characteristics. The proposed approach significantly reduces system complexity, cost, and computational load, making it suitable for compact, real-time Battery Management Systems (BMS) in low-cost EVs and stationary energy storage applications.