Optimal Sensor Placement for Multifault Detection and Isolation in Lithium-Ion Battery Pack

Cheng, Yu‐Shan; D’Arpino, Matilde; Rizzoni, Giorgio

doi:10.1109/tte.2021.3137462

Cited by 16 publications

(6 citation statements)

References 46 publications

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“…Secondly, accurately parsing FBG sensor data used for battery assessment is a major challenge [70,110,121,124,125]. (1) Due to the sensitivity of fiber optic sensors to multiple parameters, such as temperature and stress, achieving decoupling and precise measurement of multidimensional parameters is crucial for accurately evaluating the health status of batteries [138][139][140][141]. This includes addressing the coupling of temperature and strain, electrolyte RI, and other influencing factors, such as the solid electrolyte layer versus the electrochemical interfacial layers.…”

Section: Challenges and Outlooksmentioning

confidence: 99%

Advancements in Battery Monitoring: Harnessing Fiber Grating Sensors for Enhanced Performance and Reliability

Yu,

Chen,

Deng

et al. 2024

Sensors

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Batteries play a crucial role as energy storage devices across various industries. However, achieving high performance often comes at the cost of safety. Continuous monitoring is essential to ensure the safety and reliability of batteries. This paper investigates the advancements in battery monitoring technology, focusing on fiber Bragg gratings (FBGs). By examining the factors contributing to battery degradation and the principles of FBGs, this study discusses key aspects of FBG sensing, including mounting locations, monitoring targets, and their correlation with optical signals. While current FBG battery sensing can achieve high measurement accuracies for temperature (0.1 °C), strain (0.1 με), pressure (0.14 bar), and refractive index (6 × 10−5 RIU), with corresponding sensitivities of 40 pm/°C, 2.2 pm/με, −0.3 pm/bar, and −18 nm/RIU, respectively, accurately assessing battery health in real time remains a challenge. Traditional methods struggle to provide real-time and precise evaluations by analyzing the microstructure of battery materials or physical phenomena during chemical reactions. Therefore, by summarizing the current state of FBG battery sensing research, it is evident that monitoring battery material properties (e.g., refractive index and gas properties) through FBGs offers a promising solution for real-time and accurate battery health assessment. This paper also delves into the obstacles of battery monitoring, such as standardizing the FBG encapsulation process, decoupling multiple parameters, and controlling costs. Ultimately, the paper highlights the potential of FBG monitoring technology in driving advancements in battery development.

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Section: Challenges and Outlooksmentioning

confidence: 99%

Advancements in Battery Monitoring: Harnessing Fiber Grating Sensors for Enhanced Performance and Reliability

Yu,

Chen,

Deng

et al. 2024

Sensors

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“…For this reason, PS configuration is mostly used in automotive applications allowing for cost and complexity reduction. As matter of fact, even if N •M current sensors would be required to properly diagnose and isolate faults in PS modules in realworld scenarios [12], current sensors are avoided at module level. This results in the adoption of only one current sensor for the PS architecture.…”

Section: Qualitative Comparison Of Battery Pack Architecturesmentioning

confidence: 99%

“…The SP architecture is found in fewer automotive applications, among these the Honda Fit EV [9]. However, the SP configuration is drawing increasing attention especially in space and aviation applications [10,11] since it can potentially increase reliability and diagnosability [12,13]. However, a systematic comparison between these two different battery pack architectures has not been fully investigated in literature due to the lack of tools for a comprehensive analysis.…”

Section: Introductionmentioning

confidence: 99%

Reduced Order Model of Common Battery Pack Architectures for Assessment of Cell Parameter Variation Propagation

Porpora,

D'Arpino,

Cheng

et al. 2023

IEEE Access

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Nowadays, the series-parallel (SP) and the parallel-series (PS) configurations represent the main architectures considered for designing a battery pack. In both architectures, cell-to-cell parameters' variations due to manufacturing tolerances, thermal gradients, and cell degradation can strongly impact the overall performance of battery packs. However, the effect of the parameter variation on the pack performance changes depending on the architecture and the number of cells. This manuscript aims at providing a method for the assessment of the impact of cell-to-cell parameters' variations due to potential different aging and thermal conditions for the cells in a generic SP and PS pack architecture. For this purpose, a generalized reduced order equivalent circuit model for the battery pack is defined, leading to a set of steady-state equations for easily and systematically calculating the current and voltage imbalances due to capacity and internal resistance variations independently from the pack architecture and the number of cells. A parametric analysis is reported for quantitatively evaluating the severity of the imbalances due to the cellto-cell parameters' variations for both battery pack architectures. The results of the comparative analysis demonstrate that PS architecture is more impacted by parameters' variations with respect to the SP one, since a higher current imbalance is always obtained. The difference in the severity of the current imbalance in the pack architectures strongly increases as the number of series-connected cells rises. Moreover, among the cell parameters' variations, a major impact of the capacity imbalance on the reduction of the overall performance of the battery pack is observed. INDEX TERMSLithium-ion battery, Battery pack, Cell parameter variation, Voltage and current imbalance, Battery management system, Pack architecture. • mechanical design features for providing protection and robustness to the battery pack [4, 5]; • thermal stability achieved by thermal management systems [6, 7]; • selection and sizing of components needed for ensuring

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“…State observers, such as extended Kalman filters (EKF) [10,11], adaptive EKF [9], unscented Kalman filters (UKF) [12], dual EKF [13], and nonlinear observers [3], have been used, along with parameter estimation techniques, such as recursive least squares [7,11,12,14] and particle swarm optimization [8,14], to generate residuals. Anomaly detection is also performed by thresholding the model-based voltage, temperature, and state of charge (SoC) residuals against predetermined thresholds [9,13,15,16]. However, generating modelbased residuals is computationally expensive for battery packs, as it involves estimators for many cells.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the aforementioned approaches are applicable to only one type of fault [7][8][9][10]. Some techniques work only if no two faults occur at the same time [16,19,20]. Some of the aforementioned approaches require parameter estimation by performing specific characteristic tests [6,9,18].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Thermal Anomaly Detection in Large Battery Packs

et al. 2023

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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.

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Optimal Sensor Placement for Multifault Detection and Isolation in Lithium-Ion Battery Pack

Cited by 16 publications

References 46 publications

Advancements in Battery Monitoring: Harnessing Fiber Grating Sensors for Enhanced Performance and Reliability

Advancements in Battery Monitoring: Harnessing Fiber Grating Sensors for Enhanced Performance and Reliability

Reduced Order Model of Common Battery Pack Architectures for Assessment of Cell Parameter Variation Propagation

Data-Driven Thermal Anomaly Detection in Large Battery Packs

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