Active Diagnosability of Discrete Event Systems and its Application to Battery Fault Diagnosis

Chen, Ziqiang; Lin, Feng; Wang, Caisheng; Wang, Yi Le; Xu, Min

doi:10.1109/tcst.2013.2291069

Cited by 38 publications

(8 citation statements)

References 16 publications

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“…In addition, with an RBS, a new control lever is established at the ESU level while conventional battery systems have no ability of error handling, except shutting down the entire system. In a first step, the switches of an RBS can be used to identify weak cells, since they are advantageously for active fault diagnosis [32,383,384]. Secondly, cells with unusual behavior can be removed from the load path at an early stage without having to interrupt the operation of the load [385].…”

Section: Bad Block Managementmentioning

confidence: 99%

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

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Section: Bad Block Managementmentioning

confidence: 99%

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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“…The growing interest and recent breakthroughs in AI and ML have actively contributed to the development of new methods for FD and fault prognosis approaches. Figure 1 illustrates the FD methods which have been a hot topic [21] around the world. As mentioned in [22], the results of the FD can provide many benefits and save costs.…”

Section: Data Analytics and Deep Learning In Evsmentioning

confidence: 99%

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

2021

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Diagnosing faults in electric vehicles (EVs) is a great challenge. The purpose of this paper is to demonstrate the detection of faults in an electromechanical conversion chain for conventional or autonomous EVs. The information and data coming from different sensors make it possible for EVs to recover a series of information including currents, voltages, speeds, and so on. This information is processed to detect any faults in the electromechanical conversion chain. The novelty of this study is to develop an architecture for a fault diagnosis model by means of the feature extraction technique. In this regard, the long short-term memory (LSTM) approach for the fault diagnosis is proposed. This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning. An EV in an urban context is modeled, and then the fault diagnosis approach is applied based on deep learning architectures. The EV and the fault diagnosis model is simulated in Matlab software. It is also revealed how deep learning contributes to the fault diagnosis of EVs. The simulation and practical results confirm that higher accuracy in the fault diagnosis is obtained by applying the LSTM.

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“…In (Cabasino, Lafortune and Seatzu, 2013) a sensor location problem is solved to guarantee diagnosability, but this is impractical because of the number of reachable markings may increase exponentially according to the size of the net. In (Chen, Lin, Wang, Le Wang, and Xu, 2014) diagnosability is forced by selecting the appropriate words to achieve fault detection and isolation for a specific case and cannot be generalized.…”

Section: Fault Diagnosis Of Des Have Been Widelymentioning

confidence: 99%

Forcing faults diagnosis in K steps in discrete event systems

2021

EJT

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Discrete Event Systems occur naturally in engineering practice and include industrial processes, production systems, robotics, among others. So, its diagnosis is important. The aim of this work is to present a new approach to enforce faults diagnosis in Discrete Event Systems modeled by Interpreted Petri nets without limited to much its language. This approach is based on a Regulation Circuit that reduces the relative distance between any two pair of transitions that prevent the detection of the firing of transitions whose occurrence indicates that a fault occurred. By modifying the initial marks of the net that make up the Regulation Circuit, language limitation is avoided, and the diagnosis is established in k steps. First, the terminology used in the Interpreted Petri nets and the diagnosis are established, then the relationship between theses nets and the Regulation Circuit is reviewed to determine the characterization of nets where they can be added, they are analysed and then the proposal is made. The proposal is an option to force faults diagnosis in binary Interpreted Petri nets that are not diagnosable without limiting so much its language that represents their behavior.

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Active Diagnosability of Discrete Event Systems and its Application to Battery Fault Diagnosis

Cited by 38 publications

References 16 publications

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

Forcing faults diagnosis in K steps in discrete event systems

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