The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data...The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data segments while designing SOH estimation algorithms that efficiently handle the large-scale computational demands of cloud-based battery management systems presents a substantial challenge.In this work,we propose a quantum convolutional neural network(QCNN)model designed for accurate,robust,and generalizable SOH estimation with minimal data and parameter requirements and is compatible with quantum computing cloud platforms in the Noisy Intermediate-Scale Quantum.First,we utilize data from 4 datasets comprising 272 cells,covering 5 chemical compositions,4 rated parameters,and 73operating conditions.We design 5 voltage windows as small as 0.3 V for each cell from incremental capacity peaks for stochastic SOH estimation scenarios generation.We extract 3 effective health indicators(HIs)sequences and develop an automated feature fusion method using quantum rotation gate encoding,achieving an R2of 96%.Subsequently,we design a QCNN whose convolutional layer,constructed with variational quantum circuits,comprises merely 39 parameters.Additionally,we explore the impact of training set size,using strategies,and battery materials on the model’s accuracy.Finally,the QCNN with quantum convolutional layers reduces root mean squared error by 28% and achieves an R^(2)exceeding 96% compared to other three commonly used algorithms.This work demonstrates the effectiveness of quantum encoding for automated feature fusion of HIs extracted from limited discharge data.It highlights the potential of QCNN in improving the accuracy,robustness,and generalization of SOH estimation while dealing with stochastic and noisy data with few parameters and simple structure.It also suggests a new paradigm for leveraging quantum computational power in SOH estimation.展开更多
Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices.Domain adaptation(DA)is an effective method for addressing the distribution discrepancy ...Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices.Domain adaptation(DA)is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed.In this paper,we propose a variational quantum domain adaptation method inspired by the quantum convolutional neural network,named variational quantum domain adaptation(VQDA).The data are first uploaded by a‘quantum coding module',then the feature information is extracted by several‘quantum convolution layers'and‘quantum pooling layers',which is named‘Feature Extractor'.Subsequently,the labels and the domains of the samples are obtained by the‘quantum fully connected layer'.With a gradient reversal module,the trained‘Feature Extractor'can extract the features that cannot be distinguished from the source and target domains.The simulations on the local computer and IBM Quantum Experience(IBM Q)platform by Qiskit show the effectiveness of the proposed method.The results show that VQDA(with 8 quantum bits)has 91.46%average classification accuracy for DA task between MNIST→USPS(USPS→MNIST),achieves 91.16%average classification accuracy for gray-scale and color images(with 10 quantum bits),and has 69.25%average classification accuracy on the DA task for color images(also with 10 quantum bits).VQDA achieves a 9.14%improvement in average classification accuracy compared to its corresponding classical domain adaptation method with the same parameter scale for different DA tasks.Simultaneously,the parameters scale is reduced to 43%by using VQDA when both quantum and classical DA methods have similar classification accuracies.展开更多
Current concrete surface crack detection methods cannot simultaneously achieve high detection accuracy and efficiency.Thus,this study focuses on the recognition and classification of crack images and proposes a concre...Current concrete surface crack detection methods cannot simultaneously achieve high detection accuracy and efficiency.Thus,this study focuses on the recognition and classification of crack images and proposes a concrete crack detection method that integrates the Inception module and a quantum convolutional neural network.First,the features of concrete cracks are highlighted by image gray processing,morphological operations,and threshold segmentation,and then the image is quantum coded by angle coding to transform the classical image information into quantum image information.Then,quantum circuits are used to implement classical image convolution operations to improve the convergence speed of the model and enhance the image representation.Second,two image input paths are designed:one with a quantum convolutional layer and the other with a classical convolutional layer.Finally,comparative experiments are conducted using different parameters to determine the optimal concrete crack classification parameter values for concrete crack image classification.Experimental results show that the method is suitable for crack classification in different scenarios,and training speed is greatly improved compared with that of existing deep learning models.The two evaluation metrics,accuracy and recall,are considerably enhanced.展开更多
基金funded by the Research on SOC/SOH Joint Estimation Technology of Electric Vehicle Battery System State Based on Online Parameter Identification Project(2019)the National Natural Science Foundation of China(Grant No.51877120)。
文摘The accurate state of health(SOH)estimation of lithium-ion batteries is crucial for efficient,healthy,and safe operation of battery systems.Extracting meaningful aging information from highly stochastic and noisy data segments while designing SOH estimation algorithms that efficiently handle the large-scale computational demands of cloud-based battery management systems presents a substantial challenge.In this work,we propose a quantum convolutional neural network(QCNN)model designed for accurate,robust,and generalizable SOH estimation with minimal data and parameter requirements and is compatible with quantum computing cloud platforms in the Noisy Intermediate-Scale Quantum.First,we utilize data from 4 datasets comprising 272 cells,covering 5 chemical compositions,4 rated parameters,and 73operating conditions.We design 5 voltage windows as small as 0.3 V for each cell from incremental capacity peaks for stochastic SOH estimation scenarios generation.We extract 3 effective health indicators(HIs)sequences and develop an automated feature fusion method using quantum rotation gate encoding,achieving an R2of 96%.Subsequently,we design a QCNN whose convolutional layer,constructed with variational quantum circuits,comprises merely 39 parameters.Additionally,we explore the impact of training set size,using strategies,and battery materials on the model’s accuracy.Finally,the QCNN with quantum convolutional layers reduces root mean squared error by 28% and achieves an R^(2)exceeding 96% compared to other three commonly used algorithms.This work demonstrates the effectiveness of quantum encoding for automated feature fusion of HIs extracted from limited discharge data.It highlights the potential of QCNN in improving the accuracy,robustness,and generalization of SOH estimation while dealing with stochastic and noisy data with few parameters and simple structure.It also suggests a new paradigm for leveraging quantum computational power in SOH estimation.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62375140 and 61871234)。
文摘Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices.Domain adaptation(DA)is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed.In this paper,we propose a variational quantum domain adaptation method inspired by the quantum convolutional neural network,named variational quantum domain adaptation(VQDA).The data are first uploaded by a‘quantum coding module',then the feature information is extracted by several‘quantum convolution layers'and‘quantum pooling layers',which is named‘Feature Extractor'.Subsequently,the labels and the domains of the samples are obtained by the‘quantum fully connected layer'.With a gradient reversal module,the trained‘Feature Extractor'can extract the features that cannot be distinguished from the source and target domains.The simulations on the local computer and IBM Quantum Experience(IBM Q)platform by Qiskit show the effectiveness of the proposed method.The results show that VQDA(with 8 quantum bits)has 91.46%average classification accuracy for DA task between MNIST→USPS(USPS→MNIST),achieves 91.16%average classification accuracy for gray-scale and color images(with 10 quantum bits),and has 69.25%average classification accuracy on the DA task for color images(also with 10 quantum bits).VQDA achieves a 9.14%improvement in average classification accuracy compared to its corresponding classical domain adaptation method with the same parameter scale for different DA tasks.Simultaneously,the parameters scale is reduced to 43%by using VQDA when both quantum and classical DA methods have similar classification accuracies.
基金supported by 2023 National College Students'Innovation and Entrepreneurship Training Program project"Building Crack Structure Safety Detection based on Quantum Convolutional Neural Network intelligent Algorithm-A case study of Sanzhuang Town,Donggang District,Rizhao City"(NO.202310429224).
文摘Current concrete surface crack detection methods cannot simultaneously achieve high detection accuracy and efficiency.Thus,this study focuses on the recognition and classification of crack images and proposes a concrete crack detection method that integrates the Inception module and a quantum convolutional neural network.First,the features of concrete cracks are highlighted by image gray processing,morphological operations,and threshold segmentation,and then the image is quantum coded by angle coding to transform the classical image information into quantum image information.Then,quantum circuits are used to implement classical image convolution operations to improve the convergence speed of the model and enhance the image representation.Second,two image input paths are designed:one with a quantum convolutional layer and the other with a classical convolutional layer.Finally,comparative experiments are conducted using different parameters to determine the optimal concrete crack classification parameter values for concrete crack image classification.Experimental results show that the method is suitable for crack classification in different scenarios,and training speed is greatly improved compared with that of existing deep learning models.The two evaluation metrics,accuracy and recall,are considerably enhanced.
