Battery SOC and SOH Simulation Calculations

Battery SOC (State of Charge) and SOH (State of Health) simulations are core computational tasks in Battery Management Systems (BMS). These parameters directly impact battery lifespan, safety, and performance optimization.

The key to SOC simulation lies in accurately estimating remaining battery capacity. Common methods include Open Circuit Voltage (OCV) method, Ampere-hour (Ah) integration, and model-based state estimation algorithms like Kalman filters. The OCV method suits static scenarios, while Ah integration accumulates errors under dynamic conditions - hence modern BMS typically employ hybrid algorithms or filtering techniques to enhance accuracy. Code implementation often involves voltage-temperature-SOC lookup tables for OCV methods and real-time current integration with Coulomb efficiency compensation for Ah counting.

SOH simulation focuses on battery degradation, typically evaluated through capacity fade and internal resistance growth. Laboratory environments obtain data through cycle testing, while practical applications rely on parameter identification and machine learning models to predict aging trends. Implementation may include capacity estimation through full-cycle discharge tests or incremental capacity analysis (ICA) for real-time BMS applications.

Simulations are typically based on equivalent circuit models (ECM) or electrochemical models. ECM offers higher computational efficiency suitable for real-time BMS, while electrochemical models provide greater accuracy at the cost of complexity, mainly used for offline analysis. The integration of optimization algorithms and adaptive learning techniques further improves simulation reliability, establishing foundations for intelligent battery management. Code implementations often utilize recursive least squares (RLS) for parameter identification and extended Kalman filters (EKF) for nonlinear state estimation.