Early detection of anomalous degradation behavior in lithium-ion batteries

Before lithium-ion batteries are purchased in volume, they are typically tested (qualified) to determine if they meet the life-cycle reliability requirements for the targeted applications. To ensure that subsequent production lots of batteries continue to meet the reliability requirements, ongoing reliability testing is often conducted on production lot samples. However, a key challenge is how to quickly determine if the samples have substantially similar reliability as those batteries that were initially qualified, and, in particular, how to detect early signs of unacceptable degradation. This paper uses five data-driven methods (regression model with prediction bound, one-class support vector machine, local outlier factor, Mahalanobis distance, and sequential probability ratio test) to detect anomalous degradation behavior of samples from actual production lots subjected to ongoing reliability tests. An ensemble approach was then developed because it was observed that no single method always gave the earliest warning. The approach can be used by device companies for warranty, recall, and technical decisions.