A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries

Shen, Wenjing; Xu, Kangkang; Deng, Liming; Zhang, Shupeng

doi:10.1109/access.2020.3030719

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…can be derived by projecting spatiotemporal samples onto the BFs. With time coefficients and the corresponding input signal ( ) ut , the low-order temporal model can be estimated using (17) to (21). Finally, D-ELM based spatiotemporal model can be reconstructed using space/time synthesis.…”

Section: B D-elm Based Offline Spatiotemporal Modelmentioning

confidence: 99%

Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

Xu¹,

Tan²,

Fan³

et al. 2021

IEEE Access

Self Cite

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Many industrial thermal processes are large-scale time-varying nonlinear distributed parameter systems (DPSs). To effectively model such systems, dual extreme learning machine based online spatiotemporal modeling with adaptive forgetting factor (AFFD-ELM) is proposed in this paper. This method can recursively update the parameters of the low-order temporal model by using newly arriving data under Karhunen-Loè ve (KL) based space/time separation. In this way, the time-varying dynamics can be tracked real-time very well as output data increases over time. Besides, since the training samples are usually timeliness, adaptive forgetting factor (AFF) is also embedded in this method to improve the online learning effects by adding a reasonable weight to previous data. This online learning strategy makes the process promising for online modeling under continuously samples environment. The proposed method is utilized for online temperature prediction of the curing oven. Simulation results verify the efficiency and viability of the online spatiotemporal model.

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Section: B D-elm Based Offline Spatiotemporal Modelmentioning

confidence: 99%

Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

Xu¹,

Tan²,

Fan³

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…I NTELLIGENT manufacturing is the combination of advanced sensing, detection, control, and process optimization technologies and practices that fuse information and the manufacturing environment to enable precise management of energy, production efficiency, and costs in factories [1]. The snap curing oven during chip packaging [2], the thermal monitoring of lithium-ion battery charge and discharge experiments [3], and the thermal management of chemical engineering reactors are all topics of intelligent manufacturing research. They have infinite-dimensional properties described by partial differential equations (PDEs).…”

Section: Introduction a Backgroundmentioning

confidence: 99%

SSAE and GRU Based Joint Modeling for Nonlinear Distributed Parameter Systems

Gan

Feng

et al. 2022

IEEE Access

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The modeling and control issues for distributed parameter systems (DPSs) have received a great deal of attention. Because linear model order reduction (MOR) methods may ignore the nonlinear dynamics and lose some details, it is difficult to describe DPS accurately by common modeling methods. To effectively model such systems, a sparse stacked auto-encoder and gated recurrent unit (SSAE-GRU) model is proposed in this paper. Under the time/space separation theory, it is the mainstream way to perform MOR and identification of time series respectively. In the SSAE-GRU model, this practice is still adhered to but joint learning is recommended. SSAE can be used as an excellent MOR technique. A sparse activation strategy that is introduced makes its model space simple and easy to train. GRU has the ability to represent such complex temporal properties because the information stored by previous neurons can be transmitted to the current moment selectively. The joint training method allows them to be responsible and consider the connection between adjacent moments and spatial energy transfer overall. Then, we use L2 regularization in back-propagation to reduce the difficulty of model optimization and prevent overfitting. The modeling scheme is simulated on two typical chemical thermal processes. This article demonstrates the effectiveness of the proposed method as well as outstanding performance compared to existing methods.INDEX TERMS Distributed parameter systems, model order reduction, sparse stacked auto-encoder, gated recurrent unit, joint learning

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A nonlinear spatiotemporal modeling method combined with t-distributed stochastic neighbor embedding and broad learning system for the lithium-ion battery thermal process

Zhu,

Xie,

Yang

et al. 2024

Engineering Applications of Artificial Intelligence

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A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries

Cited by 6 publications

References 48 publications

Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

SSAE and GRU Based Joint Modeling for Nonlinear Distributed Parameter Systems

A nonlinear spatiotemporal modeling method combined with t-distributed stochastic neighbor embedding and broad learning system for the lithium-ion battery thermal process

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