The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charg...The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.展开更多
The lifespan models of commercial 18650-type lithium ion batteries （nominal capacity of 1150 mA-h） were presented. The lifespan was extrapolated based on this model. The results indicate that the relationship of cap...The lifespan models of commercial 18650-type lithium ion batteries （nominal capacity of 1150 mA-h） were presented. The lifespan was extrapolated based on this model. The results indicate that the relationship of capacity retention and cycle number can be expressed by Gaussian function. The selecting function and optimal precision were verified through actual match detection and a range of alternating current impedance testing. The cycle life model with high precision （〉99%） is beneficial to shortening the orediction time and cutting the prediction cost.展开更多
In this paper,a novel model-based fault detection in the battery management system of an electric vehicle is proposed.Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults,cons...In this paper,a novel model-based fault detection in the battery management system of an electric vehicle is proposed.Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults,considering the impact of battery aging.Battery aging primarily affects capacity and resistance,becoming more pronounced in the later stages of a battery lifespan.By incorporating aging effects into our fault diagnosis scheme,our proposed approach prevents false or missed alarms for the aged battery cells.The aging effect of battery,capacity fading and resistance growth,are considered unknown parameters.An adaptive observer is employed to design a fault detector,considering unknown parameters in the battery model.The adaptive observers are designed for two different scenarios:In the first scenario,it is presumed that aging effects remain constant over time due to their slow rate of change.Then,it is assumed that aging effects are time-varying.Therefore,the fault detection scheme can detect faults of new battery cells as well as aged cells.Some simulations have been conducted on a Lithium-ion battery cell and extended to battery pack,to demonstrate the performance of the proposed approach in more real-world scenarios.The results showed that the designed observers can detect faults correctly in a seven years old battery as well as a new one.展开更多
This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules.Improper use of batteries can lead to electrolyte decomposition,resulting in the formation of lithium dendri...This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules.Improper use of batteries can lead to electrolyte decomposition,resulting in the formation of lithium dendrites.These dendrites may pierce the separator,leading to the failure of the insulation layer between electrodes and causing micro short circuits.When a micro short circuit occurs,the electrolyte typically undergoes exothermic reactions,leading to thermal runaway and posing a safety risk to users.Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention.To address this issue,the article develops a multi-tiered fault detection algorithm for series-connected lithium-ion batteries.This algorithm can effectively diagnose micro short circuits,aging,and normal batteries using minimal battery data,thereby improving diagnostic accuracy and enhancing the flexibility of fault detection.Simulations and experiments conducted under various levels of micro short circuits validate the effectiveness of the algorithm,demonstrating its ability to distinguish between short-circuited,aged,and normal batteries under different conditions.This technology can be applied to electric vehicles and energy storage systems,enabling early warnings to ensure safety and prevent thermal runaway.展开更多
The autonomous navigation of an electric vehicle requires the implementation of a number of sensors and actuators intended to inform it about his environment or his position and velocity and deliver necessary inputs. ...The autonomous navigation of an electric vehicle requires the implementation of a number of sensors and actuators intended to inform it about his environment or his position and velocity and deliver necessary inputs. That＇s why it is important to detect and locate sensor and actuator faults as soon as possible to enable the operator to run the vehicle in degraded mode or use the fault tolerant control system if it exists. The main purpose of this paper deals with sensors or actuators faults diagnosis of autonomous vehicle. A diagnosis method using a nonlinear model of the vehicle is developed. Nonlinear state space model of the autonomous electric vehicle is used with the method of nonlinear analytical redundancy to detect and to isolate faults occurred on sensors or actuators. Computer simulations are carried out to verify the effectiveness of the method.展开更多
The air conditioning systems in electric city buses usually operate in rapidly changing ambient conditions and are more likely to suffer from mechanical faults. Although many fault detection and diagnosis (FDD) method...The air conditioning systems in electric city buses usually operate in rapidly changing ambient conditions and are more likely to suffer from mechanical faults. Although many fault detection and diagnosis (FDD) methods have been developed for building air conditioning systems, they are difficult to be applied to bus air conditioners since its operation is highly dynamic and fault-free data are usually unavailable. Therefore, this paper proposes an FDD method for electric bus air conditioners to tackle the above issues. First, the method identifies faults in an unsupervised manner by comparing selected features among a group of peer systems. Then, considering the features are influenced by the operating conditions, Gaussian process regression (GPR) models are established to find the relationships between each feature and its influential parameters. The probabilistic nature of the GPR is used to differentiate predictions with large uncertainty, which are then excluded from FDD. In this way, robustness of the method is evidently improved. Finally, fault indexes are defined to detect and diagnose mechanical faults. We applied the method to a group of air conditioners in a city bus fleet. Results showed that it can effectively identify refrigerant undercharge and indoor and outdoor fan problems with low false positive/genitive rates. Also, the method is highly robust and not sensitive to the faulty systems in the bus fleet.展开更多
In this paper,a metal object detection(MOD)for wireless electric vehicle charger(WEVC)employing DD coils is proposed.Conventional single-layer symmetric coils exhibit reduced sensitivity near the coils and blind-zone ...In this paper,a metal object detection(MOD)for wireless electric vehicle charger(WEVC)employing DD coils is proposed.Conventional single-layer symmetric coils exhibit reduced sensitivity near the coils and blind-zone along their symmetry axis.To address these limitations,we propose a dual-layer MOD coil configuration.And in this configuration,the second coil layer features rectangular coils in the less sensitive regions,and an optimal concave-convex coil design is given.By using the configuration,the proposed design can enhance the sensitivity and overcome the blind-zone challenges.Finally,simulation and experimental results also show the effectiveness and robustness of the proposed design,which can also be used to improve the detection capability in wireless power transmission applications.展开更多
基金supported by Jiangsu Provincial Science and Technology Project,grant number J2023124.Jing Guo received this grant,the URLs of sponsors’website is https://kxjst.jiangsu.gov.cn/(accessed on 06 June 2024).
文摘The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.
基金Projects(51204209,51274240)supported by the National Natural Science Foundation of ChinaProject(HNDLKJ[2012]001-1)supported by Henan Electric Power Science&Technology Supporting Program,China
文摘The lifespan models of commercial 18650-type lithium ion batteries （nominal capacity of 1150 mA-h） were presented. The lifespan was extrapolated based on this model. The results indicate that the relationship of capacity retention and cycle number can be expressed by Gaussian function. The selecting function and optimal precision were verified through actual match detection and a range of alternating current impedance testing. The cycle life model with high precision （〉99%） is beneficial to shortening the orediction time and cutting the prediction cost.
文摘In this paper,a novel model-based fault detection in the battery management system of an electric vehicle is proposed.Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults,considering the impact of battery aging.Battery aging primarily affects capacity and resistance,becoming more pronounced in the later stages of a battery lifespan.By incorporating aging effects into our fault diagnosis scheme,our proposed approach prevents false or missed alarms for the aged battery cells.The aging effect of battery,capacity fading and resistance growth,are considered unknown parameters.An adaptive observer is employed to design a fault detector,considering unknown parameters in the battery model.The adaptive observers are designed for two different scenarios:In the first scenario,it is presumed that aging effects remain constant over time due to their slow rate of change.Then,it is assumed that aging effects are time-varying.Therefore,the fault detection scheme can detect faults of new battery cells as well as aged cells.Some simulations have been conducted on a Lithium-ion battery cell and extended to battery pack,to demonstrate the performance of the proposed approach in more real-world scenarios.The results showed that the designed observers can detect faults correctly in a seven years old battery as well as a new one.
文摘This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules.Improper use of batteries can lead to electrolyte decomposition,resulting in the formation of lithium dendrites.These dendrites may pierce the separator,leading to the failure of the insulation layer between electrodes and causing micro short circuits.When a micro short circuit occurs,the electrolyte typically undergoes exothermic reactions,leading to thermal runaway and posing a safety risk to users.Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention.To address this issue,the article develops a multi-tiered fault detection algorithm for series-connected lithium-ion batteries.This algorithm can effectively diagnose micro short circuits,aging,and normal batteries using minimal battery data,thereby improving diagnostic accuracy and enhancing the flexibility of fault detection.Simulations and experiments conducted under various levels of micro short circuits validate the effectiveness of the algorithm,demonstrating its ability to distinguish between short-circuited,aged,and normal batteries under different conditions.This technology can be applied to electric vehicles and energy storage systems,enabling early warnings to ensure safety and prevent thermal runaway.
文摘The autonomous navigation of an electric vehicle requires the implementation of a number of sensors and actuators intended to inform it about his environment or his position and velocity and deliver necessary inputs. That＇s why it is important to detect and locate sensor and actuator faults as soon as possible to enable the operator to run the vehicle in degraded mode or use the fault tolerant control system if it exists. The main purpose of this paper deals with sensors or actuators faults diagnosis of autonomous vehicle. A diagnosis method using a nonlinear model of the vehicle is developed. Nonlinear state space model of the autonomous electric vehicle is used with the method of nonlinear analytical redundancy to detect and to isolate faults occurred on sensors or actuators. Computer simulations are carried out to verify the effectiveness of the method.
基金support of this research by the Research Talent Hub for ITF Project(ITP/002/22LP)sponsored by Hong Kong Innovation and Technology Fund and the Research Grants Council of the Hong Kong SAR(C5018-20GF).
文摘The air conditioning systems in electric city buses usually operate in rapidly changing ambient conditions and are more likely to suffer from mechanical faults. Although many fault detection and diagnosis (FDD) methods have been developed for building air conditioning systems, they are difficult to be applied to bus air conditioners since its operation is highly dynamic and fault-free data are usually unavailable. Therefore, this paper proposes an FDD method for electric bus air conditioners to tackle the above issues. First, the method identifies faults in an unsupervised manner by comparing selected features among a group of peer systems. Then, considering the features are influenced by the operating conditions, Gaussian process regression (GPR) models are established to find the relationships between each feature and its influential parameters. The probabilistic nature of the GPR is used to differentiate predictions with large uncertainty, which are then excluded from FDD. In this way, robustness of the method is evidently improved. Finally, fault indexes are defined to detect and diagnose mechanical faults. We applied the method to a group of air conditioners in a city bus fleet. Results showed that it can effectively identify refrigerant undercharge and indoor and outdoor fan problems with low false positive/genitive rates. Also, the method is highly robust and not sensitive to the faulty systems in the bus fleet.
基金financial support provided by National Key R&D Program of China(Grant NO.2021YFB3301000)National Natural Science Foundation of China(NSFC:62173297)+1 种基金Zhejiang Key R&D Program(Grant NO.2022C01035)Project of Ningbo Automotive Electronics Intelligentifi-cation Innovation Union(2022H007).
文摘In this paper,a metal object detection(MOD)for wireless electric vehicle charger(WEVC)employing DD coils is proposed.Conventional single-layer symmetric coils exhibit reduced sensitivity near the coils and blind-zone along their symmetry axis.To address these limitations,we propose a dual-layer MOD coil configuration.And in this configuration,the second coil layer features rectangular coils in the less sensitive regions,and an optimal concave-convex coil design is given.By using the configuration,the proposed design can enhance the sensitivity and overcome the blind-zone challenges.Finally,simulation and experimental results also show the effectiveness and robustness of the proposed design,which can also be used to improve the detection capability in wireless power transmission applications.
