Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

IEEE Transactions on Industrial Electronics

Published On 2024/4/15

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions using domain-adaptive multitask learning (MTL) with long-term regularization. First, features extracted from partial charging curves are utilized for short-term state of health predictions. Then, the long-term degradation trajectory is directly predicted by recursively using the predicted features within the multitask framework, enhancing the model integrity and lowering the complexity. Then, domain adaptation (DA) is adopted to reduce the discrepancies between different working conditions. Additionally, a long-term regularization is introduced to address the shortcoming that arises when the model is extrapolated recursively for future health predictions. Thus, the short …

Journal

IEEE Transactions on Industrial Electronics

Published On

2024/4/15

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

Yunhong Che

Yunhong Che

Chongqing University

Position

H-Index(all)

16

H-Index(since 2020)

16

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Energy Storage Systems

Transportation electrification

Prognostics and Health Management

Battery

University Profile Page

Florent Forest

Florent Forest

École Polytechnique Fédérale de Lausanne

Position

Scientific collaborator (École Polytechnique Fédérale de Lausanne)

H-Index(all)

8

H-Index(since 2020)

8

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

Clustering

Data Science

Aerospace

Health Monitoring

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Chunhua Yang

Chunhua Yang

Central South University

IEEE Transactions on Industrial Electronics

Multimodel Self-Learning Predictive Control Method With Industrial Application

In industrial sites, system operation conditions fluctuate due to changes in raw material and equipment status, making it critical to identify the operation conditions and obtain appropriate controllers accurately. Additionally, even for a specific operation condition, fixed control strategies may result in mismatches due to varying operational stages. To address the accurate control of industrial processes across multiple operation conditions, this article proposes a multimodel self-learning predictive control (MSLPC) method to simultaneously improve the accuracy of offline condition partition and online control performance. Specifically, in the offline stage, for complex and multidimensional industrial data, condition indicators are selected based on expert systems and data analytics, and a “presetting precise-fusion” two-stage operation condition learning (TSOCL) algorithm is proposed to accurately identify the operation …

Choon Ki Ahn

Choon Ki Ahn

Korea University

IEEE Transactions on Industrial Electronics

Model-Free Filter-Based Single-Loop Output-Feedback System Design for PMSMs With Critically Damped Performance

This article designs a filter-based output-feedback system to regulate the speed of permanent magnet synchronous motors (PMSMs), which structures a simple single-loop form compensated by feed-forward terms. The proposed controller design framework considerably reduces the dependence level of the PMSM model by requiring partial nominal parameter values for control law and removing the model structure and whole parameter information for the filter. The main advantages consist of two parts. First, the proposed observer employs the second-order pole-zero cancelation (PZC) technique to continuously extract the speed and acceleration from noisy position measurements by the rotary encoder, independent from the PMSM model. Second, the PZC filter-based proportional–integral control forms a single-loop feedback system including the active damping injection and disturbance observer, which assigns …

Sang-Won Lee

Sang-Won Lee

Kongju National University

IEEE Transactions on Industrial Electronics

Quasi-Resonant Fly-Buck Converter With Active Switching for Improved Output Voltage Boosting and Regulation

This article proposes a quasi-resonant fly-buck converter using an active switching operation at the isolated load side. The proposed circuit overcomes the cross-regulation and low voltage gain problems of the conventional fly-buck converter with the simple quasi-resonance operation between the inherent leakage inductance of a transformer and an auxiliary capacitor at the isolated secondary side. The converter implements the high step-up operation using an auxiliary switch; therefore, the reduced winding ratio of the transformer improves the power density of the circuit. In addition, this circuit implements the soft switching at all active components, and does not suffer from the reverse recovery problem at a diode. A 20 W prototype having an input voltage of 12 V was built to prove the theoretical analyses, and it regulated 5 V for 10 W and 15 V for 10 W at the primary nonisolated and secondary isolated load sides …

Xiaoshan Bai

Xiaoshan Bai

Technische Universiteit Delft

IEEE Transactions on Industrial Electronics

Efficient Performance Impact Algorithms for Multirobot Task Assignment With Deadlines

This article investigates the multirobot task assignment problem with deadlines, where a group of distributed heterogeneous robots needs to collaborate effectively to first maximize the number of successful search and rescue missions and then minimize the robots' total service time. First, a distributed performance impact algorithm is designed to obtain the initial assignment solution, where each robot can compute its assignment solution independently. Subsequently, based on different local search strategies, a disturbance mechanism and two improvement strategies, namely, first, global task inclusion first and decoupled 1-opt second phase and, second, global task inclusion first and coupled 1-opt second phase, are proposed to disrupt and refine the initial solution. The integration of the distributed performance impact algorithm with the two refinement strategies leads to a decoupled performance impact algorithm …

zhiliang zhang

zhiliang zhang

Nanjing University of Aeronautics and Astronautics

IEEE Transactions on Industrial Electronics

Digital Synchronous Rectifier Control Using Extended Harmonics Impedance Model for High-Frequency GaN-Based LLC Converters

Conventional LLC synchronous rectifier (SR) schemes use detection circuits to sense the signals of voltage or current. But it typically operates below 500 kHz, which is unsuitable for high-frequency applications as it may lose large SR duty cycle caused by high dv/dt and GaN devices usage. To address the serious challenge, a digital SR control using extended harmonics impedance model is proposed for GaN LLC converters. By building the impedance model, the SR on-time is calculated accurately in the microcontroller. It not only minimizes the on-time of SR body diode with high precision, but also owns high immunity. A 650-kHz 1.5-kW GaN LLC converter was built. With the output of 28 V/53.5 A, the efficiency is as high as 98.1% at 1.5 kW by using the proposed SR. The power density is also up to 524 W/in 3 . Compared to the conventional SR, the efficiency improves 1.6% at full load.