SoCSoHSchätzung

SoCSoHSchätzung, sometimes written as SoC- and SoH-Schätzung, is an integrated approach to estimating both the state of charge (SoC) and the state of health (SoH) of a rechargeable battery. SoC indicates the remaining usable capacity at a given time, expressed as a percentage of the nominal capacity, while SoH characterizes the overall condition of the battery, including capacity fade and increases in internal resistance due to aging. The combined estimation aims to provide accurate real-time information for control, prognostics, and safety.

In practice, SoCSoHSchätzung relies on mathematical models of the battery that relate electrical measurements to the hidden states. Model types include physics-based electrochemical models, reduced-order electrochemical models, and electrical equivalent circuit models. Observables such as voltage, current, and temperature feed the estimator, which often uses Kalman filter variants (EKF, UKF, CKF), or particle filters, to update SoC and SoH estimates. Age-related parameters (capacity nominal, degradation factors, resistance) may be treated as states to be estimated online or identified offline and updated with aging models.

Sensor data and estimator design must address challenges like nonlinearity, model mismatch, sensor noise, temperature effects, and initialization sensitivity. Observability conditions and the quality of aging models strongly influence accuracy and required rest times for voltage-based SoC estimation.

Applications include electric vehicles, energy storage systems, and portable electronics, where accurate joint estimation improves battery management, safety, maintenance planning, and end-of-life prognostics. Ongoing research explores hybrid data-driven and physics-informed approaches to enhance robustness under diverse usage and aging scenarios.