State estimators play a crucial role in ensuring safe and efficient battery operation in real-world applications. Insufficient algorithms pose safety risks, user dissatisfaction, and accelerated battery degradation. However, the development and validation of state estimation algorithms for battery management systems (BMS) remains a complex challenge. A review of the literature reveals significant gaps, particularly regarding validation procedures, test scenario selection, and algorithm comparability.

Validation procedures range from simulation-based platforms to execution on real BMS hardware. The lack of standardized test scenarios and performance criteria complicates the validation. Environmental influences such as temperature further underline the need for validation under diverse and extreme conditions, while hardware-specific factors also impact estimator performance.
To address these challenges, our work explores validation strategies that ensure both robustness and comparability of state estimators. We emphasize the importance of representative test scenarios, which may be selected randomly, focused on critical boundary cases, or systematically identified through advanced methods such as Bayesian optimization.

In particular, we demonstrate how Bayesian optimization can efficiently identify weak points of a Kalman filter for State of Charge (SoC) estimation. The search space includes variations in key test parameters such as temperature, measurement noise, load profiles, and initial SoC levels. By exposing weaknesses systematically, this approach enables targeted algorithm improvements, reduces validation effort, and provides a more meaningful basis for performance assessment.

Ultimately, this methodology allows us to evaluate whether validation restricted to critical edge cases is sufficient, or whether broader parameter variations are required. Furthermore, the approach is transferable to other estimation methods and battery states, supporting robust algorithm development across a wide range of applications.