State of Charge (SOC) estimation is a critical function in battery management systems, particularly for applications like grid storage where partial charge and discharge cycles are common. Accurate SOC estimation ensures optimal performance, longevity, and safety of the battery system. However, partial cycling introduces unique challenges that complicate SOC estimation, including depth-of-discharge (DoD) effects, voltage hysteresis, and the need for precise algorithm tuning.

Depth-of-discharge (DoD) plays a significant role in SOC estimation accuracy. In grid storage applications, batteries often operate within a limited SOC window to extend cycle life. For example, a system may restrict cycling between 20% and 80% SOC to minimize degradation. While this improves longevity, it reduces the observable voltage range, making SOC estimation more sensitive to measurement errors. The relationship between voltage and SOC becomes less distinct in partial cycles, as the open-circuit voltage (OCV) curve tends to flatten in mid-SOC regions. This flattening reduces the signal-to-noise ratio, increasing reliance on coulomb counting, which accumulates errors over time due to current sensor inaccuracies and inefficiencies.

DoD also influences the battery's aging characteristics, which must be accounted for in SOC estimation. Repeated partial cycling alters the electrode kinetics and electrolyte decomposition rates differently compared to full cycles. Over time, these changes affect the OCV-SOC relationship, requiring adaptive algorithms to maintain accuracy. Empirical studies have shown that batteries cycled at 50% DoD exhibit different degradation patterns than those cycled at 80% DoD, necessitating periodic recalibration of SOC models to reflect these shifts.

Voltage hysteresis further complicates SOC estimation during partial cycling. Hysteresis refers to the divergence in voltage between charge and discharge paths at the same SOC, caused by thermodynamic and kinetic irreversibilities in electrode materials. In lithium-ion batteries, hysteresis is particularly pronounced in certain chemistries, such as lithium iron phosphate (LFP), where the voltage plateau masks SOC variations. During partial cycling, the battery may not traverse the full hysteresis loop, making it difficult to resolve the true SOC without additional measurements or modeling techniques.

Hysteresis modeling is essential for accurate SOC estimation in partial cycles. Common approaches include Preisach or Duhem models, which mathematically describe the hysteresis loop based on historical charge-discharge data. These models require extensive parameterization and may increase computational overhead, posing challenges for real-time BMS implementations. Simplified methods, such as state-space representations with hysteresis states, offer a trade-off between accuracy and computational efficiency. However, their performance depends on the battery chemistry and the specific partial cycling profile.

Algorithm tuning is another critical factor in addressing SOC estimation challenges. Many SOC estimation methods, such as Kalman filters or neural networks, rely on parameters that must be carefully adjusted for partial cycling conditions. For example, the process and measurement noise covariance matrices in an extended Kalman filter (EKF) must be tuned to account for reduced voltage sensitivity in mid-SOC ranges. Adaptive filtering techniques, such as sliding window observers or recursive least squares, can help mitigate drift by continuously updating model parameters based on recent data.

Machine learning approaches have gained traction for SOC estimation in dynamic cycling conditions. These methods leverage large datasets to learn the nonlinear relationships between voltage, current, temperature, and SOC. However, their effectiveness depends on the representativeness of training data, which must encompass diverse partial cycling scenarios to ensure robustness. Overfitting is a common pitfall, especially when training data lacks variability in DoD and environmental conditions.

Temperature effects further compound SOC estimation challenges. Partial cycling often occurs in non-isothermal environments, where temperature fluctuations alter internal resistance and OCV. Compensation techniques, such as reference temperature normalization or multi-parameter look-up tables, are necessary to maintain accuracy. Some advanced BMS implementations integrate thermal models to predict temperature gradients within the cell, improving SOC estimation under uneven heating conditions.

Real-world validation of SOC estimation algorithms remains a hurdle. Laboratory tests often use controlled cycling profiles that may not fully replicate grid storage operating conditions. Field data from grid-scale batteries show that irregular charge-discharge patterns, intermittent renewable energy inputs, and varying load demands introduce additional noise and transients. Algorithms must be robust enough to handle these irregularities without frequent recalibration.

In summary, SOC estimation during partial charge-discharge cycles presents distinct challenges driven by DoD effects, hysteresis, and environmental variability. Effective solutions require a combination of advanced modeling techniques, adaptive algorithms, and robust validation under realistic conditions. As grid storage systems expand, improving SOC estimation accuracy will remain a key focus for enhancing battery performance and reliability.