Robustness analysis for battery-supported cyber-physical systems

Zhang, Fumin; Shi, Zhenwu; Mukhopadhyay, Shayok

doi:10.1145/2442116.2442119

Cited by 28 publications

(16 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, every discharge current profile would need a differently tuned low-pass filter to ensure that sensor noise is rejected. Furthermore, setting constant thresholds on SOC or terminal voltage can result in many false alarms [15]. One may argue that solving (1)- (4) and then monitoring the solutions may allow accurate detection of battery failure.…”

Section: Methodsmentioning

confidence: 99%

Supervised learning for early and accurate battery terminal voltage collapse detection

Obeid

Tariq

Mukhopadhyay

2020

IET Circuits, Devices & Systems

Self Cite

View full text Add to dashboard Cite

Rechargeable batteries are critical components in many electrical systems nowadays. One has to ensure reliable diagnosis and assessment of the installed batteries for smooth and safe operations. Assessment of the remaining capacity of a battery is crucial diagnostic information. A battery management system (BMS) needs to reliably report the ability of the battery to supply power or the lack thereof. If the BMS fails to do so at an early stage, this may compromise the health of the entire electric system. When a battery nears a region where the battery state-of-charge (SOC) is low, there is a risk of an abrupt drop in the terminal voltage. An early detection of such a region is crucial; otherwise, the BMS may not have enough time to react. To address this issue, our work provides a novel supervised learning approach towards an early detection of Li-ion battery terminal voltage collapse. No knowledge of initial SOC or battery model parameters is required. This is particularly important as batteries lose their capacity to store charge over time. The efficacy of the proposed approach is demonstrated by an early and accurate detection of terminal voltage collapse over a set of discharge tests conducted using multiple batteries.

show abstract

Section: Methodsmentioning

confidence: 99%

Supervised learning for early and accurate battery terminal voltage collapse detection

Obeid

Tariq

Mukhopadhyay

2020

IET Circuits, Devices & Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…The challenge of establishing this model is to identify common ingredients and components of a CPS present in a variety of scenarios, models, and investigate them. To resolve this challenge, Zhang et al [34] propose a classification for CPS. In a Cyber-Physical System, physical devices such as batteries and sensors seem to be physical components.…”

Section: Literature Review a Related Workmentioning

confidence: 99%

Security Analysis of CPS Systems Under Different Swapping Strategies in IoT Environments

et al. 2020

View full text Add to dashboard Cite

In this paper, we give attention to the robustness of the Cyber-Physical System, which consists of interdependent physical resources and computational resources. Numerous infrastructure systems can evolve into the Cyber-Physical System, e.g., smart power grids, traffic control systems, and wireless sensor and actuator networks. These networks depend on their interdependent networks, which provide information or energy to function. In a Cyber-Physical System, a small failure could trigger serious cascading failures within the entire interdependent networks. In this paper, we try to alleviate these cascading failures between interdependent networks to reduce losses. We discuss the robustness of systems for random attacks by calculating the size of functioning components in entire networks. We change the inter-links topology of the coupled networks to enhance the reliability of the entire system. Then we get the most effective swapping strategy in enhancing the robustness of the Cyber-Physical System compared to previous studies. Different systems' structures would influence the performance of swap inter links strategies on improving the reliability of networks. Moreover, our work could guide how to optimize a Cyber-Physical System topology by reducing the influence of cascading failures. INDEX TERMS Cyber-physical system, interdependent networks, cascading failures, swap inter-links strategy, robustness, giant component.

show abstract

“…As reported in [32], the model parameters α 1 -α 21 are constants that satisfy α 14 < α 17 < α 15 < α 13 < α 18 < α 16 . The stability of this battery model was discussed in [19] and [33], where the stability was determined using two real thresholds, α 1α 21 , and ρ. Accordingly, the model is declared in stable, asymptotically stable, or unstable regions.…”

Section: Discrete Battery Modelmentioning

confidence: 99%

Fusion-Based Deterministic and Stochastic Parameters Estimation for a Lithium-Polymer Battery Model

Tameemi

2020

IEEE Access

View full text Add to dashboard Cite

In this study, a vehicle localization technique was employed to determine the required quantities in the identification of battery models by considering the behavior of multiple batteries instead of data from a single battery. In previous studies, a plant (e.g., a battery, motor, super-capacitor, or fuel cell) was identified based on a single piece of data. However, such an approach is disadvantageous in that it neglects the effect of process and measurement noise and assumes that the parameters obtained using data from a single plant are identical for all plants of the same type. First, deterministic parameter estimation (DPE), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) were initially applied to estimate the battery model parameters using data from a single battery. Second, a fusion-based approach was used to address the process and measurement noise problems through an adaptive unscented Kalman filter algorithm. With this approach, maximum likelihood estimation was employed to fuse multiple-battery data streams to enable the DPE, PSO, and TLBO to recalculate the model parameters based on filtered and fused quantities. A comparison between the experimental results and model outputs obtained using the aforementioned methods for parameter estimation indicated that the proposed multiple-battery approach enhances the accuracy of several identification methods. In contrast, it requires a high computational effort. INDEX TERMS universal adaptive stabilizer (UAS), particle swarm optimization (PSO), teachinglearning-based optimization (TLBO), unscented Kalman filter (UKF), and maximum likelihood estimation (MLE).

show abstract

Robustness analysis for battery-supported cyber-physical systems

Cited by 28 publications

References 35 publications

Supervised learning for early and accurate battery terminal voltage collapse detection

Supervised learning for early and accurate battery terminal voltage collapse detection

Security Analysis of CPS Systems Under Different Swapping Strategies in IoT Environments

Fusion-Based Deterministic and Stochastic Parameters Estimation for a Lithium-Polymer Battery Model

Contact Info

Product

Resources

About