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This article proposes a bias detection method in the voltage measurement of lithium-ion (Li-ion) battery cells to identify faulty sensor(s). The proposed method is based on a Bayesian probabilistic approach that detects possible measurement bias in any battery cell in real-time. A hypothesis bank is constructed for possible bias magnitudes in each cell. Subsequently, the fault detection algorithm computes the probability associated with all hypotheses. Once the probability of a certain hypothesis converges to unity, the faulty sensor and its associated bias are identified. The quantified bias can then be compensated in the measurement model of the associated cell. Details on the proposed method followed by experimental verification using commercial lithium-ion battery datasets are provided.

State-of-charge estimation is an essential part of a battery management system. Charging and discharging batteries involve complex chemical processes that could lead to undesired consequences, such as premature end of life or fire hazards if the battery's state-of-charge is not closely monitored. This work proposes a state-of-charge estimation method that compensates for noisy measurements and parametric uncertainties in a battery cell to improve the estimation accuracy. In this work, a robust and adaptive estimation scheme based on a Cubature Kalman filter is proposed. The algorithm utilizes the Variational Bayesian method to identify the measurements noise covariance magnitude online. Additionally, the algorithm is augmented with the ability to suppress outliers based on the Maximum Correntropy Criterion. The proposed filter is experimentally verified on standard tests including aggressive electric vehicle drive cycles, and it is compared against the extended Kalman filter, the Cubature Kalman filter, and the Variational Bayesian Cubature Kalman filter.