A Statistical Model-Based Cell-to-Cell Variability Management of Li-ion Battery Pack

Shin, Donghwa; Poncino, Massimo; Macii, Enrico; Chang, Naehyuck

doi:10.1109/tcad.2014.2384506

Cited by 35 publications

(7 citation statements)

References 14 publications

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“…The tolerance that occurs in the continuous process causes the distribution of design variables, which in turn results in variations in the performance o LIB. As the battery system expands in units of modules and packs, the variation in perfor mance of LIBs caused by the uncertainty in the manufacturing process degrades the ca pacity, lifespan, and safety [18].…”

Section: Manufacturing Uncertainties Of Libsmentioning

confidence: 99%

“…The capacity variation of LIBs reduces the life and safety of the battery pack and increase the risk of failures [16][17][18]. Baumhöfer et al performed an experiment, wherein 48 identical LIBs were charged and discharged over 1800 cycles.…”

Section: Introductionmentioning

confidence: 99%

“…Shin et al used the LIB equivalent circuit model and verified that the capacity variation between cells reduces the capacity of the battery pack. They determined that assembling the modules by minimizing the capacity variation between modules during the manufacturing of a battery pack can increase the capacity of the pack [18]. These studies reported that the distribution of design variables causes variations in the performance of LIBs by reducing the capacity, lifespan, and reliability of battery packs.…”

Section: Introductionmentioning

confidence: 99%

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Reliability-Based Design Optimization for Reducing the Performance Failure and Maximizing the Specific Energy of Lithium-Ion Batteries Considering Manufacturing Uncertainty of Porous Electrodes

et al. 2021

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Uncertainty quantification in LIB manufacturing has received interest in order to improve the reliability of LIB. The uncertainty generated during the manufacturing causes variations in the performance of LIBs, thereby increasing capacity degradation and leading to failure. In this study, a reliability-based design optimization (RBDO) of LIBs is conducted to reduce performance failure while maximizing the specific energy. The design variables with uncertainty are the thickness, porosity, and particle size of the anode and cathode. The specific energy is defined as the objective function in the optimization design problem. To maintain the specific power in the initial design of the LIB, it is defined as the constraint function. Reliability is evaluated as the probability that the battery satisfies the performance of the required design. The results indicate that the design optimized through RBDO increases the specific energy by 42.4% in comparison with that of the initial design while reducing the failure rate to 1.53%. Unlike the conventional deterministic design optimization method (DDO), which exhibits 55.09% reliability, the proposed RBDO method ensures 98.47% reliability. It is shown that the proposed RBDO approach is an effective design method to reduce the failure rate while maximizing the specific energy.

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Section: Manufacturing Uncertainties Of Libsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reliability-Based Design Optimization for Reducing the Performance Failure and Maximizing the Specific Energy of Lithium-Ion Batteries Considering Manufacturing Uncertainty of Porous Electrodes

et al. 2021

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“…Specifically, it may be possible to derive a unique signature from the battery's physical features to authenticate the battery to detect/prevent counterfeit, swapping, and tampering. There are several sources which suggest that each battery has unique features [7,46], but there are no related works on using them for the purpose of authentication. There is also a strong necessity to prevent IC counterfeit/tampering to secure the system.…”

Section: Future Directionsmentioning

confidence: 99%

A Security Perspective on Battery Systems of the Internet of Things

et al. 2017

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Battery (sub)systems are used in many systems (systems-of-systems) in the Internet of Things (IoT) ranging from everyday ones (e.g., mobile systems, home appliances, etc.) to safety-critical and/or mission-critical ones (e.g., electrical vehicles, unmanned aerial vehicles, autonomous underwater vehicles, etc.). As these systems become more interconnected with each other and their environments and batteries become more energy dense, the safety risks of using batteries increase. To guarantee effectiveness and prevent potential safety threats (i.e., failure, overheating, explosion), it is not only crucial to ensure that batteries are functioning correctly (via safety circuits and battery management system), but to also prevent security threats that specifically target the battery system from different parts of these systems. A security analysis is necessary for system manufacturers and users to understand what threats and solutions exist for battery system security. In University of Florida, Gainesville, FL 32611, USA this paper, we present a security perspective on battery systems, where we use a layered approach to analyze vulnerabilities, threats, and potential effects. We divide the battery system into the Physical, Battery Management System, and Application layers and use mobile systems and cyber-physical systems as case studies for IoT applications. We then highlight and discuss some existing solutions and mention the potential research directions on battery system security.

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“…When discharging, these differences in voltage between the cells reduce the battery's usable capacity because of the cell with the lowest voltage; conversely, during charging, the cell with the highest voltage causes overcharging. Thus, the CC-CV method is disadvantageous in terms of efficiency and battery life for battery charging [8,9]. In particular, in large applications like electric vehicle (EV) and energy storage system (ESS), damages can be considerably reduced using cell balancing every time because the lifetime and efficiency can be improved.…”

Section: Introductionmentioning

confidence: 99%

New Cell Balancing Charging System Research for Lithium-ion Batteries

2020

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With recent advancements in the electrical industry, the demand for high capacity and high energy density batteries has increased, subsequently increasing the demand for fast and reliable battery charging. A battery is an assembly of a plurality of cells, in which maintaining a balance between neighboring cells is crucial for stable charging. To this end, various methods have been applied to battery management systems. Representative methods for maintaining the balance in battery cells include a passive method of adjusting the balance using a resistor and an active method involving the exchange of energy between the cells. However, these methods are limited in terms of efficiency, lifespan, and charging time. Therefore, in this study, we propose a new charging method at the battery cell level and demonstrate its effectiveness through experiments.

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A Statistical Model-Based Cell-to-Cell Variability Management of Li-ion Battery Pack

Cited by 35 publications

References 14 publications

Reliability-Based Design Optimization for Reducing the Performance Failure and Maximizing the Specific Energy of Lithium-Ion Batteries Considering Manufacturing Uncertainty of Porous Electrodes

Reliability-Based Design Optimization for Reducing the Performance Failure and Maximizing the Specific Energy of Lithium-Ion Batteries Considering Manufacturing Uncertainty of Porous Electrodes

A Security Perspective on Battery Systems of the Internet of Things

New Cell Balancing Charging System Research for Lithium-ion Batteries

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