Boosting battery state of health estimation based on self-supervised learning

Journal of Energy Chemistry

Published On 2023/6/8

State of health (SoH) estimation plays a key role in smart battery health prognostic and management. However, poor generalization, lack of labeled data, and unused measurements during aging are still major challenges to accurate SoH estimation. Toward this end, this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation. Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells, the proposed method achieves accurate and robust estimations using limited labeled data. A filter-based data preprocessing technique, which enables the extraction of partial capacity-voltage curves under dynamic charging profiles, is applied at first. Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder. The learned network parameters are …

Journal

Journal of Energy Chemistry

Published On

2023/6/8

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

Xin Sui

Xin Sui

Aalborg Universitet

Position

H-Index(all)

14

H-Index(since 2020)

14

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Li-ion batteries

SOH estimation

RUL prediction

University Profile Page

Other Articles from authors

Yunhong Che

Yunhong Che

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Small-Sample-Learning-Based Lithium-Ion Batteries Health Assessment: An Optimized Ensemble Framework

Machine Learning is widely studied in battery state of health (SOH) estimation due to its advantage in establishing the non-linear mapping between measurements and SOH. However, the requirement of a big dataset and the lack of robustness limit the practical application, especially in small sample learning. To tackle these challenges, an optimal ensemble framework called BaggELM (bagging extreme learning machine) is proposed for battery SOH estimation. Specifically, the required dataset is reduced by optimizing the input voltage and the hyperparameters of the BaggELM algorithm. Moreover, a statistical post-processing method is used to aggregate multiple ELMs, and the final estimate is determined by the maximum probability density value. As a result, the effects of random parameterization of ELM and the training data size on SOH estimation are suppressed, thus improving the robustness and accuracy of …

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Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities

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Yunhong Che

Yunhong Che

Chongqing University

Reliability Engineering & System Safety

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Electronics

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

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 …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE/ASME Transactions on Mechatronics

Online Sensorless Temperature Estimation of Lithium-Ion Batteries Through Electro-Thermal Coupling

Owing to the nonnegligible impacts of temperature on the safety, performance, and lifespan of lithium-ion batteries, it is essential to regulate battery temperature to an optimal range. Temperature monitoring plays a fundamental role in battery thermal management, yet it is still challenged by limited onboard temperature sensors, particularly in large-scale battery applications. As such, developing sensorless temperature estimation is of paramount importance to acquiring the temperature information of each cell in a battery system. This article proposes an estimation approach to obtain the cell temperature by taking advantage of the electrothermal coupling effect of batteries. An electrothermal coupled model, which captures the interactions between the electrical and the thermal dynamics, is established, parameterized, and experimentally validated. A closed-loop observer is then designed based on this coupled model …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

arXiv preprint arXiv:2402.07777

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Remus Teodorescu

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Aalborg Universitet

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Remus Teodorescu

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Remus Teodorescu

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Aalborg Universitet

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Journal of Energy Chemistry

Influence of O–O formation pathways and charge transfer mediator on lipid bilayer membrane-like photoanodes for water oxidation

Inspired by the function of crucial components in photosystem II (PSII), electrochemical and dye-sensitized photoelectrochemical (DSPEC) water oxidation devices were constructed by the self-assembly of well-designed amphipathic Ru(bda)-based catalysts (bda = 2,2ʹ-bipyrdine-6,6ʹ-dicarbonoxyl acid) and aliphatic chain decorated electrode surfaces, forming lipid bilayer membrane (LBM)-like structures. The Ru(bda) catalysts on electrode-supported LBM films demonstrated remarkable water oxidation performance with different O–O formation mechanisms. However, compared to the slow charge transfer process, the O–O formation pathways did not determine the PEC water oxidation efficiency of the dye-sensitized photoanodes, and the different reaction rates for similar catalysts with different catalytic paths did not determine the PEC performance of the DSPECs. Instead, charge transfer plays a decisive role in …

Feng Hao

Feng Hao

University of Electronic Science and Technology of China

Journal of Energy Chemistry

Regulation the quantum barrier and carrier transport toward high-efficiency quasi-2D Dion-Jacobson tin perovskite solar cells

Quasi-2D Dion-Jacobson (DJ) tin halide perovskite has attracted much attention due to its elimination of Van der Waals gap and enhanced environmental stability. However, the bulky organic spacers usually form a natural quantum well structure, which brings a large quantum barrier and poor film quality, further limiting the carrier transport and device performance. Here, we designed three organic spacers with different chain lengths (ethylenediamine (EDA), 1,3-propanediamine (PDA), and 1,4-butanediamine (BDA)) to investigate the quantum barrier dependence. Theoretical and experimental characterizations indicate that EDA with short chain can reduce the lattice distortion and dielectric confinement effect, which is verified to be beneficial to the effective dissociation of excitons and inhibit the trap-free non-radiative relaxation. In addition, EDA cation shows strong interaction with the inorganic octahedron …

Jinyu Wen

Jinyu Wen

Huazhong University of Science and Technology

Journal of Energy Chemistry

Effect of preload forces on multidimensional signal dynamic behaviours for battery early safety warning

Providing early safety warning for batteries in real-world applications is challenging. In this study, comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces. The time-sequence relationship among expansion force, voltage, and temperature during thermal abuse under five categorised stages is revealed. Three characteristic peaks are identified for the expansion force, which correspond to venting, internal short-circuiting, and thermal runaway. In particular, an abnormal expansion force signal can be detected at temperatures as low as 42.4 °C, followed by battery thermal runaway in approximately 6.5 min. Moreover, reducing the preload force can improve the effectiveness of the early-warning method via the expansion force. Specifically, reducing the preload force from 6000 to 1000 N prolongs the warning time (i.e. 227 to …

M.L. Sui

M.L. Sui

Beijing University of Technology

Journal of Energy Chemistry

Selective core-shell doping enabling high performance 4.6 V-LiCoO2

Constructing robust surface and bulk structure is the prerequisite for realizing high performance high voltage LiCoO2 (LCO). Herein, we manage to synthesize a surface Mg-doping and bulk Al-doping core-shell structured LCO, which demonstrates excellent cycling performance. Half-cell shows 94.2% capacity retention after 100 cycles at 3-4.6 V (vs. Li/Li+) cycling, and no capacity decay after 300 cycles for full-cell test (3.0-4.55 V). Based on comprehensive microanalysis and theoretical calculations, the degradation mechanisms and doping effects are systematically revealed. For the undoped LCO, high voltage cycling induces severe interfacial and bulk degradations, where cracks, stripe defects, fatigue H2 phase, and spinel phase are identified in grain bulk. For the doped LCO, Mg-doped surface shell can suppress the interfacial degradations, which not only stabilizes the surface structure by forming a thin rock …

Dominic Bresser

Dominic Bresser

Karlsruher Institut für Technologie

Journal of Energy Chemistry

Comprehension-driven design of advanced multi-block single-ion conducting polymer electrolytes for high-performance lithium-metal batteries

Design and modification of high-performance single-ion conducting polymer electrolytes providing high interfacial stability, enhanced safety, and superior charge transport towards high-energy lithium-metal batteries containing high-voltage, nickel-rich LiNi 1-xy Co x Mn y O 2 (NCM) cathodes.

Wenxin Mei

Wenxin Mei

University of Science and Technology of China

Journal of Energy Chemistry

Effect of safety valve types on the gas venting behavior and thermal runaway hazard severity of large-format prismatic lithium iron phosphate batteries

The safety valve is an important component to ensure the safe operation of lithium-ion batteries (LIBs). However, the effect of safety valve type on the thermal runaway (TR) and gas venting behavior of LIBs, as well as the TR hazard severity of LIBs, are not known. In this paper, the TR and gas venting behavior of three 100 A h lithium iron phosphate (LFP) batteries with different safety valves are investigated under overheating. Compared to previous studies, the main contribution of this work is in studying and evaluating the effect of gas venting behavior and TR hazard severity of LFP batteries with three safety valve types. Two significant results are obtained: (I) the safety valve type dominates over gas venting pressure of battery during safety venting, the maximum gas venting pressure of LFP batteries with a round safety valve is 3320 Pa, which is one order of magnitude higher than other batteries with oval or cavity …

Journal of Energy Chemistry

Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

Battery management systems (BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion batteries. The main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage (OCV). However, acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation models. In addressing these concerns, this study introduces a deep neural network-combined framework for accurate and robust OCV estimation, utilizing partial daily charging data. We incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV curves. Correlation analysis is employed to identify the optimal partial charging data, optimizing the OCV estimation precision while preserving exceptional …