Data-Driven Thermal Anomaly Detection in Large Battery Packs

Abstract

The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time voltage and temperature data from multiple Li-ion battery cells. Mean-based residuals are generated for cell groups and evaluated using Principal Component Analysis. The evaluated residuals are then thresholded using a cumulative sum control chart to detect anomalies. The mild external short circuits associated with cell balancing are detected in the voltage signals and necessitate voltage retraining after balancing. Temperature residuals prove to be critical, enabling anomaly detection of module balancing events within 14 min that are unobservable from the voltage residuals. Statistical testing of the proposed approach is performed on the experimental data from a battery electric locomotive injected with model-based anomalies. The proposed anomaly detection approach has a low false-positive rate and accurately detects and traces the synthetic voltage and temperature anomalies. The performance of the proposed approach compared with direct thresholding of mean-based residuals shows a 56% faster detection time, 42% fewer false negatives, and 60% fewer missed anomalies while maintaining a comparable false-positive rate.

Multifeature‐based online remaining useful life prediction of lithium‐ion batteries in stages using cascaded data‐driven algorithm

“…The first dataset originated from NASA's Ames Prediction Center of Excellence (PCoE), 31 and included three lithium‐ion batteries (B5, B7, and B18) with a rated capacity of 2Ah. The battery end of life (EOL) is defined as 70% of the rated capacity.…”

Battery management system enhancement for lithium-ions battery cells using switched shunt resistor approach based on finite state machine control algorithm

“…A key requirement of the BMS is safe charging and operation. As discussed by (Bhaskar et al, 2023;Habib and Hasan, 2023;Thiagarajan et al, 2023;Xu et al, 2023), improper management of LiBs can lead to battery degradation or damage to other systems. The BMS must be able to handle various fault scenarios, in addition to the normal charging cycle.…”

“…The proposed anomaly detection approach detects and traces voltage and temperature anomalies accurately. According to the proposed method, 56% fewer false negatives, 42% fewer false positives, and 60% fewer missed anomalies are achieved compared with direct thresholding (Bhaskar et al, 2023).…”

Systematic Approach for the Test Data Generation and Validation of ISC/ESC Detection Methods

“…Despite these very promising studies, no systematic side-by-side comparison of different methods has been published yet-not even in the context of recent extensive review studies [29,[33][34][35]. There are, however, studies evaluating advantages and disadvantages of certain methods, e.g., by Hu et al [29], but the classification based on measures such as sensitivity for noise or high precision [29] is rather subjective and vague [36]. In addition, some researches have published a brief comparison with alternative methods, e.g., [37][38][39], but both implementation and evaluation criteria are limited.…”