A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life

[1]  Laurence T. Yang,et al.  ADTT: A Highly Efficient Distributed Tensor-Train Decomposition Method for IIoT Big Data , 2021, IEEE Transactions on Industrial Informatics.

[2]  Yuan Zuo,et al.  Learning-based network path planning for traffic engineering , 2019, Future Gener. Comput. Syst..

[3]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[4]  Kaoru Ota,et al.  Enabling Computational Intelligence for Green Internet of Things: Data-Driven Adaptation in LPWA Networking , 2020, IEEE Computational Intelligence Magazine.

[5]  Tsorng-Juu Liang,et al.  Estimation of Battery State of Health Using Probabilistic Neural Network , 2013, IEEE Transactions on Industrial Informatics.

[6]  Yandong Hou,et al.  Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm , 2017 .

[7]  Kai Goebel,et al.  Bayesian hierarchical model-based prognostics for lithium-ion batteries , 2018, Reliab. Eng. Syst. Saf..

[8]  Kaoru Ota,et al.  Adaptive data and verified message disjoint security routing for gathering big data in energy harvesting networks , 2020, J. Parallel Distributed Comput..

[9]  Guangzhong Dong,et al.  Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression , 2018, IEEE Transactions on Industrial Electronics.

[10]  Christian Fleischer,et al.  Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .

[11]  K. Goebel,et al.  Prognostics in Battery Health Management , 2008, IEEE Instrumentation & Measurement Magazine.

[12]  A. Takuya Shimamoto,et al.  A study on evaluation method for the Lithium-ion battery life performance for stationary use , 2013, International Conference on Clean Electrical Power.

[13]  Lei Ren,et al.  A Data-Driven Approach of Product Quality Prediction for Complex Production Systems , 2021, IEEE Transactions on Industrial Informatics.

[14]  Laurence Tianruo Yang,et al.  A Tensor-Based Multiattributes Visual Feature Recognition Method for Industrial Intelligence , 2021, IEEE Transactions on Industrial Informatics.

[15]  Lei Ren,et al.  Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach , 2018, IEEE Access.

[16]  Seongjun Lee,et al.  State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge , 2008 .

[17]  Biao Huang,et al.  Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE , 2018, IEEE Transactions on Industrial Informatics.

[18]  Yu Peng,et al.  Lithium-ion Battery Remaining Useful Life Estimation Based on Nonlinear AR Model Combined with Degradation Feature , 2012 .

[19]  Xiaohong Su,et al.  Interacting multiple model particle filter for prognostics of lithium-ion batteries , 2017, Microelectron. Reliab..

[20]  Renquan Lu,et al.  A Wide-Deep-Sequence Model-Based Quality Prediction Method in Industrial Process Analysis , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Michael G. Pecht,et al.  A prognostics and health management roadmap for information and electronics-rich systems , 2010, Microelectron. Reliab..

[22]  Hongwen He,et al.  A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries , 2018, IEEE Transactions on Industrial Electronics.

[23]  Mianxiong Dong,et al.  Energy Cooperation in Battery-Free Wireless Communications with Radio Frequency Energy Harvesting , 2018, ACM Trans. Embed. Comput. Syst..

[24]  Yulei Wu,et al.  An Intelligent Anomaly Detection Scheme for Micro-Services Architectures With Temporal and Spatial Data Analysis , 2020, IEEE Transactions on Cognitive Communications and Networking.

[25]  Lei Ren,et al.  Coding-Based Large-Scale Task Assignment for Industrial Edge Intelligence , 2019, IEEE Transactions on Network Science and Engineering.