Knowledge-guided data-driven model with transfer concept for battery calendar ageing trajectory prediction

IEEE/CAA Journal of Automatica Sinica

Published On 2023/1/6

Dear Editor, Lithium-ion (Li-ion) battery has become a promising source to supply and absorb energy/power for many energy-transportation applications. However, Li-ion battery capacity would inevitably degrade over time, making its related ageing prediction necessary. This letter presents effective battery calendar ageing trajectory prediction by deriving a knowledge-guided data-driven model with transfer concept. More specifically, this data-driven model is based on the support vector regression (SVR) technology. To ensure highly-accurate prognostics of battery calendar ageing trajectory under wit-nessed conditions, a knowledge-guided kernel is first developed by coupling the mechanism and empirical knowledge elements of battery storage temperature, state-of-charge (SoC), and time. To im-prove model's generalization ability under unwitnessed conditions, the knowledge-guided data-driven model is then …

Journal

IEEE/CAA Journal of Automatica Sinica

Published On

2023/1/6

Volume

10

Issue

1

Page

272-274

Authors

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Position

Professor at

H-Index(all)

104

H-Index(since 2020)

72

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

Smart Batteries

AI

University Profile Page

Aoife M. Foley

Aoife M. Foley

Queen's University Belfast

Position

H-Index(all)

44

H-Index(since 2020)

41

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

energy policy

energy markets

wind power

energy storage

University Profile Page

Qiao Peng

Qiao Peng

Queen's University Belfast

Position

PhD in Finance

H-Index(all)

9

H-Index(since 2020)

9

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Machine learning

Data science

Carbon finance

Energy system management

University Profile Page

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Chunyu Yang

China University of Mining and Technology

IEEE/CAA Journal of Automatica Sinica

Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems with Application to Power Systems

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems (SPSs) with unknown dynamics based on reinforcement learning (RL). Taking into account the slow and fast characteristics among system states, the interconnected SPS is decomposed into the slow time-scale dynamics and the fast time-scale dynamics through singular perturbation theory. For the fast time-scale dynamics with interconnections, we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function. For the slow time-scale dynamics with unknown system parameters, an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data. By combining the slow and fast controllers, we establish the composite decentralized adaptive optimal output regulator, and rigorously …

Fen Wu

Fen Wu

North Carolina State University

IEEE/CAA Journal of Automatica Sinica

Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuous-time Markov jump piecewise-affne (PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed ∞ performance are demon-strated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation. Finally, two illustrative examples …

Naiqi Wu

Naiqi Wu

Macau University of Science and Technology

IEEE/CAA Journal of Automatica Sinica

State-Based Opacity Verification of Networked Discrete Event Systems Using Labeled Petri Nets

The opaque property plays an important role in the operation of a security-critical system, implying that pre-defined secret information of the system is not able to be inferred through partially observing its behavior. This paper addresses the verification of current-state, initial-state, infinite-step, and -step opacity of networked discrete event systems modeled by labeled Petri nets, where communication losses and delays are considered. Based on the symbolic technique for the representation of states in Petri nets, an observer and an estimator are designed for the verification of current-state and initial-state opacity, respectively. Then, we propose a structure called an I-observer that is combined with secret states to verify whether a networked discrete event system is infinite-step opaque or -step opaque. Due to the utilization of symbolic approaches for the state-based opacity verification, the computation of the …

Hai-Tao Zhang

Hai-Tao Zhang

Huazhong University of Science and Technology

IEEE/CAA Journal of Automatica Sinica

Reinforcement Learning-Based MAS Interception in Antagonistic Environments

Dear Editor, As a promising multi-agent systems (MASs) operation, autonomous interception has attracted more and more attentions in these years, where defenders prevent intruders from reaching destinations. So far, most of the relevant methods are applied in ideal environments without agent damages. As a remedy, this letter proposes a more realistic interception method for MASs suffered by damages, where the defenders are fewer than the intruders. Firstly, a multiagent interception frame (MAIF) is proposed, enabling the defenders to take actions and interact with the environments. To address non-stationarity issue induced by MAIF, a multi-agent reinforcement learning-based interception method (MAIM) is developed by sophisticatedly designing a reward function. Sufficient conditions are derived to guarantee the convergence of MAIM. Finally, numerical simulations are conducted to verify the effectiveness of …

Jiayi Ma

Jiayi Ma

Wuhan University

IEEE/CAA Journal of Automatica Sinica

Paps: Progressive attention-based pan-sharpening

Pan-sharpening aims to seek high-resolution multi-spectral (HRMS) images from paired multispectral images of low resolution (LRMS) and panchromatic (PAN) images, the key to which is how to maximally integrate spatial and spectral information from PAN and LRMS images. Following the principle of gradual advance, this paper designs a novel network that contains two main logical functions, i.e., detail enhancement and progressive fusion, to solve the problem. More specifically, the detail enhancement module attempts to produce enhanced MS results with the same spatial sizes as corresponding PAN images, which are of higher quality than directly up-sampling LRMS images. Having a better MS base (enhanced MS) and its PAN, we progressively extract information from the PAN and enhanced MS images, expecting to capture pivotal and complementary information of the two modalities for the purpose of …

Qing-Long Han, MAE, FIEEE, FIFAC, HonFIEAust, FCAA, Pro Vice-Chancellor and Distinguished Professor

Qing-Long Han, MAE, FIEEE, FIFAC, HonFIEAust, FCAA, Pro Vice-Chancellor and Distinguished Professor

Swinburne University of Technology

IEEE/CAA Journal of Automatica Sinica

When Does Sora Show: The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering

During our discussion at workshops for writing “What Does ChatGPT Say: The DAO from Algorithmic Intelligence to Linguistic Intelligence” [1], we had expected the next milestone for Artificial Intelligence (AI) would be in the direction of Imaginative Intelligence (II), i.e., something similar to automatic words-to-videos generation or intelligent digital movies/theater technology that could be used for conducting new “Artificiofactual Experiments” [2] to replace conventional “Counterfactual Experiments” in scientific research and technical development for both natural and social studies [2]–[6]. Now we have OpenAI's Sora, so soon, but this is not the final, actually far away, and it is just the beginning.