Yunhong Che

Yunhong Che

Chongqing University

H-index: 16

Asia-China

About Yunhong Che

Yunhong Che, With an exceptional h-index of 16 and a recent h-index of 16 (since 2020), a distinguished researcher at Chongqing University, specializes in the field of Energy Storage Systems, Transportation electrification, Prognostics and Health Management, Battery.

His recent articles reflect a diverse array of research interests and contributions to the field:

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

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

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

Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection

Battery aging behavior evaluation under variable and constant temperatures with real loading profiles

Early Prediction of Lithium-Ion Batteries Lifetime via Few-Shot Learning

Machine learning to predict electrochemical impedance spectra (EIS): Can EIS be replaced by constant current techniques?

Spatial–temporal data-driven full driving cycle prediction for optimal energy management of battery/supercapacitor electric vehicles

Yunhong Che Information

University

Chongqing University

Position

___

Citations(all)

1380

Citations(since 2020)

1369

Cited By

99

hIndex(all)

16

hIndex(since 2020)

16

i10Index(all)

17

i10Index(since 2020)

17

Email

University Profile Page

Chongqing University

Yunhong Che Skills & Research Interests

Energy Storage Systems

Transportation electrification

Prognostics and Health Management

Battery

Top articles of Yunhong Che

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Authors

Yunhong Che,Florent Forest,Yusheng Zheng,Le Xu,Remus Teodorescu

Journal

IEEE Transactions on Industrial Electronics

Published Date

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 …

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

Authors

Yusheng Zheng,Yunhong Che,Xiaosong Hu,Xin Sui,Remus Teodorescu

Journal

IEEE/ASME Transactions on Mechatronics

Published Date

2024/3/1

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 …

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

Authors

Yusheng Zheng,Yunhong Che,Xiaosong Hu,Xin Sui,Daniel-Ioan Stroe,Remus Teodorescu

Published Date

2024/1/1

Transportation electrification is a promising solution to meet the ever-rising energy demand and realize sustainable development. Lithium-ion batteries, being the most predominant energy storage devices, directly affect the safety, comfort, driving range, and reliability of many electric mobilities. Nevertheless, thermal-related issues of batteries such as potential thermal runaway, performance degradation at low temperatures, and accelerated aging still hinder the wider adoption of electric mobilities. To ensure safe, efficient, and reliable operations of lithium-ion batteries, monitoring their thermal states is critical to safety protection, performance optimization, as well as prognostics, and health management. Given insufficient onboard temperature sensors and their inability to measure battery internal temperature, accurate and timely temperature estimation is of particular importance to thermal state monitoring. Toward …

Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection

Authors

Yunhong Che,Yusheng Zheng,Florent Evariste Forest,Xin Sui,Xiaosong Hu,Remus Teodorescu

Journal

Reliability Engineering & System Safety

Published Date

2023/8/29

Predictive health assessment is of vital importance for smarter battery management to ensure optimal and safe operations and thus make the most use of battery life. This paper proposes a general framework for battery aging prognostics in order to provide the predictions of battery knee, lifetime, state of health degradation, and aging rate variations, as well as the assessment of battery health. Early information is used to predict knee slope and other life-related information via deep multi-task learning, where the convolutional-long-short-term memory-bayesian neural network is proposed. The structure is also used for online state of health and degradation rate predictions for the detection of accelerating aging. The two probabilistic predicted boundaries identify the accelerating aging regions for battery health assessment. To avoid wrong and premature alarms, the empirical model is used for data preprocessing and …

Battery aging behavior evaluation under variable and constant temperatures with real loading profiles

Authors

Yunhong Che,Daniel-Ioan Stroe,Xin Sui,Sϕren Byg Vilsen,Xiaosong Hu,Remus Teodorescu

Published Date

2023/3/19

Studying and analyzing battery aging behavior is crucial in battery health prognostic and management. This paper conducts novel and comprehensive experiments to evaluate battery aging under variable external stresses, including different dynamic load profiles and variable environmental temperatures. Respond analysis in the time and frequency domain is performed to account for the different aging rates under different current loadings, where the statistic calculation and fast Fourier transform are used for the analysis. The empirical model is used to fit the fade curve for the comparisons between constant and variable temperatures. The capacity decrease and internal resistance increase are extracted to evaluate capacity and power fade, respectively. The experimental results show that the urban dynamic operating conditions help to prolong the service life compared to the constant current aging case. In contrast …

Early Prediction of Lithium-Ion Batteries Lifetime via Few-Shot Learning

Authors

Xin Sui,Shan He,Yusheng Zheng,Yunhong Che,Remus Teodorescu

Published Date

2023

Artificial intelligence (AI) has been widely studied for batteries remaining useful lifetime prediction. However, the requirement of big datasets to train a robust AI model limits its practical application, particularly when batteries exhibit diverse degradation behaviors under different working conditions. Collecting sufficient data through laboratory testing can take several years. To tackle these challenges, a few-shot learning-based method for battery early lifetime prediction is proposed where only 6 cycles of charging data are required. The proposed method models batteries with different lengths of cycle life separately, considering that aging features recognized from early cycles might be different for long-life and short-life batteries. First, an auto encoder is trained to group batteries into long-life and short-life classes. The prototypical networks algorithm is employed to learn a metric space where samples from the same …

Machine learning to predict electrochemical impedance spectra (EIS): Can EIS be replaced by constant current techniques?

Authors

Jia Guo,Yunhong Che,Siyu Jin,Daniel-Ioan Stroe

Published Date

2023/7/3

Electrochemical impedance spectra (EIS) have been widely used to diagnose the battery state of health (SOH), because the battery SOH is influenced by resistance-induced overvoltage (polarizations), ie, ohmic (RΩ), activation (charge transfer; Rct), and concentration polarization (mass transport; Rmt)[1]. However, the implementation of onboard EIS measurement is hindered, due to the high cost of the measurement equipment, test results subject to SOC, and time-demanding measurements. Here, we predicted impedance spectrum by battery charging voltage curve based on electrochemical mechanistic analysis and machine learning.

Spatial–temporal data-driven full driving cycle prediction for optimal energy management of battery/supercapacitor electric vehicles

Authors

Yue Wu,Zhiwu Huang,Yusheng Zheng,Yongjie Liu,Heng Li,Yunhong Che,Jun Peng,Remus Teodorescu

Journal

Energy Conversion and Management

Published Date

2023/2/1

For multi-energy storage vehicles, the performance of online predictive energy management strategies largely relies on the length and effective utilization of predictive information. In this context, this paper proposes a novel velocity prediction method for the full driving cycle of electric vehicles based on the spatial–temporal commuting data, then the predicted velocity is applied to predictive energy management in electric vehicles with battery/supercapacitor hybrid energy storage system. Firstly, an one-year real-world commuting data set is collected on a Chinese arterial road with 10 intersections, 225 records are classified into 79 categories. Then, a real-time two-stage full driving cycle prediction method is proposed, where a medium-term prediction based on a long–short term memory (LSTM) network and a long-term prediction generated by a spatial–temporal interpolation method (STIM) are spliced for each …

Spatial-Temporal Data-Driven Speed Prediction for Energy Management of Battery/Supercapacitor Electric Vehicles

Authors

Yue Wu,Zhiwu Huang,Yunhong Che,Zini Wang,Jun Peng

Published Date

2023/10/16

Accurate speed prediction plays a critical role in the predictive energy management of electric vehicles. This paper proposes a spatial-temporal data-driven speed prediction method for the predictive energy management of battery/supercapacitor electric vehicles. The proposed speed prediction method is performed using a long short-term memory network and validated on a real-world commuting data set in China. Different from existing prediction methods based only on speed and acceleration, we take spatial information as an additional input to improve speed prediction accuracy. The predicted future speed is then leveraged by a model predictive control-based energy management strategy to minimize the battery degradation cost. Quantitative comparisons illustrate that the proposed speed prediction method can reduce the root mean square error and mean absolute error by 10.01-19.15% compared with no …

Battery health prognostic with sensor-free differential temperature voltammetry reconstruction and capacity estimation based on multi-domain adaptation

Authors

Yunhong Che,Søren Byg Vilsen,Jinhao Meng,Xin Sui,Remus Teodorescu

Journal

Etransportation

Published Date

2023/7/1

Battery health prognostic is a key part of battery management used to ensure safe and optimal usage. A novel method for end-to-end sensor-free differential temperature voltammetry reconstruction and state of health estimation based on the multi-domain adaptation is proposed in this paper. Firstly, the partial charging or discharging curve is used to reconstruct the differential temperature curve, removing the requirement for the temperature sensor measurement. The partial differential capacity curve and the reconstructed differential temperature curve are input and then used in an end-to-end state of health estimation. Finally, to reduce the domain discrepancy between the source and target domains, the maximum mean discrepancy is included as an additional loss to improve the accuracy of both differential temperature curve reconstruction and state of health estimation with unlabeled data from the testing battery …

Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects

Authors

Kailong Liu,Qiao Peng,Yunhong Che,Yusheng Zheng,Kang Li,Remus Teodorescu,Dhammika Widanage,Anup Barai

Published Date

2023/2/1

With the advent of sustainable and clean energy transitions, lithium-ion batteries have become one of the most important energy storage sources for many applications. Battery management is of utmost importance for the safe, efficient, and long-lasting operation of lithium-ion batteries. However, the frequently changing load and operating conditions, the different cell chemistries and formats, and the complicated degradation patterns pose challenges for traditional battery management. The data-driven solutions that have emerged in recent years offer great opportunities to uncover the underlying data mapping within a battery system. In particular, transfer learning improves the performance of data-driven strategies by transferring existing knowledge from different but related domains, and if properly applied, would be a promising approach for smarter battery management. To this end, this paper presents a systematic …

Sensorless State of Temperature Estimation for Smart Battery based on Electrochemical Impedance

Authors

Yusheng Zheng,Nicolai André Weinreich,Abhijit Kulkarni,Yunhong Che,Hoda Sorouri,Xin Sui,Remus Teodorescu

Published Date

2023/9/6

Temperature plays a significant role in the safety, performance, and lifetime of lithium-ion batteries (LIBs). Therefore, monitoring battery temperature becomes one of the fundamental tasks for the safe and efficient operation of LIBs. Given the limited onboard temperature sensors, this paper proposes a sensorless temperature estimation method suitable for the smart battery system by obtaining the electrochemical impedance of batteries online via bypass actions. A suitable frequency is selected from the battery electrochemical impedance spectroscopy (EIS) to achieve an accurate and robust estimation of the battery temperature through online impedance measurement. Using the battery impedance with this selected frequency, the battery temperature can be estimated under different scenarios, with RMSE less than 1.5 ℃.

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

Authors

Yunhong Che,Yusheng Zheng,Xin Sui,Remus Teodorescu

Journal

Journal of Energy Chemistry

Published Date

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 …

State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning

Authors

Yunhong Che

Published Date

2023

He won the 2023 Automotive Innovation Best Paper Award. He serves as a special session chair in ECCE-Asia 2024, and reviewer for more than 15 top-tier journals. His research interests include battery health estimation and prediction, fault diagnostics, physics-informed machine learning in modeling and prognostics, and intelligent control and management for energy storage systems.

Battery impedance spectrum prediction from partial charging voltage curve by machine learning

Authors

Jia Guo,Yunhong Che,Kjeld Pedersen,Daniel-Ioan Stroe

Journal

Journal of Energy Chemistry

Published Date

2023/4/1

Electrochemical impedance spectroscopy (EIS) is an effective technique for Lithium-ion battery state of health diagnosis, and the impedance spectrum prediction by battery charging curve is expected to enable battery impedance testing during vehicle operation. However, the mechanistic relationship between charging curves and impedance spectrum remains unclear, which hinders the development as well as optimization of EIS-based prediction techniques. In this paper, we predicted the impedance spectrum by the battery charging voltage curve and optimized the input based on electrochemical mechanistic analysis and machine learning. The internal electrochemical relationships between the charging curve, incremental capacity curve, and the impedance spectrum are explored, which improves the physical interpretability for this prediction and helps define the proper partial voltage range for the input for …

Increasing generalization capability of battery health estimation using continual learning

Authors

Yunhong Che,Yusheng Zheng,Simona Onori,Xiaosong Hu,Remus Teodorescu

Journal

Cell Reports Physical Science

Published Date

2023/12/20

Accurate and reliable estimation of battery health is crucial for predictive health management. We report a strategy to strengthen the accuracy and generalization of battery health estimation. The model can be initially built based on one battery and then continuously updated using unlabeled data and sparse limited labeled data collected in early stages of testing batteries in different scenarios, satisfying incremental improvement in practical applications. We generate our datasets from 55 commercial pouch and prismatic batteries aged for more than 116,000 cycles under various scenarios. Our model achieves a root mean-square error of 1.312% for the estimation of different dynamic current modes and rates and variable temperature conditions over the entire lifespan using partial charging data. Our model is interpreted by the post hoc strategy with unbiased hidden features, prevents catastrophic forgetting, and …

Opportunities for battery aging mode diagnosis of renewable energy storage

Authors

Yunhong Che,Xiaosong Hu,Remus Teodorescu

Journal

Joule

Published Date

2023/7/19

Lithium-ion batteries are key energy storage technologies to promote the global clean energy process, particularly in power grids and electrified transportation. However, complex usage conditions and lack of precise measurement make it difficult for battery health estimation under field applications, especially for aging mode diagnosis. In a recent issue of Nature Communications, Dubarry et al. shed light on this issue by investigating the solution based on machine learning and battery digital twins. They achieved aging modes diagnosis of photovoltaics-connected batteries working for 2 years with more than 10,000 degradation paths under different seasons and cloud shading conditions.

State of Health Estimation for Smart Batteries using Transfer Learning with Data Cleaning

Authors

Yunhong Che,Xin Sui,Remus Teodorescu

Journal

IFAC-PapersOnLine

Published Date

2023/1/1

Smart battery with optimized pulsed current produced by the bypass is one prospective technology to prolong the service life of the batteries in electric vehicles. The accurate and reliable state of health (SOH) estimation is one significant process before the decision of the control. This paper proposes a proper solution for battery SOH estimation that can be applied to both constant and pulsed current charging scenarios. Specifically, a data cleaning process is proposed for preprocessing the fluctuated measurement, while retaining the main aging information. From the pre-processed data under different charging profiles, four SOH features are extracted, and the correlation coefficients prove their effectiveness with both constant current and pulsed currents. Later, a transfer learning-based model is developed which shows improved accuracy of the SOH estimations under pulsed current scenarios. Finally, experiments …

Battery states monitoring for electric vehicles based on transferred multi-task learning

Authors

Yunhong Che,Yusheng Zheng,Yue Wu,Xianke Lin,Jiacheng Li,Xiaosong Hu,Remus Teodorescu

Journal

IEEE Transactions on Vehicular Technology

Published Date

2023/3/22

State/temperature monitoring is one of the key requirements of battery management systems that facilitates efficient and intelligent management to ensure the safe operation of batteries in electrified transportation. This paper proposes an online end-to-end state monitoring method based on transferred multi-task learning. Measurement data is directly used for sharing information generation with the convolutional neural network. Then, the multiple task-specific layers are added for state/temperature monitoring. The transfer learning strategy is designed to improve accuracy further under various application scenarios. Experiments under different working profiles, temperatures, and aging conditions are conducted to evaluate the method, which covers the wide usage ranges in electric vehicles. Comparisons with several benchmarks illustrate the superiority of the proposed method with better accuracy and …

Degradation Pattern Recognition and Features Extrapolation for Battery Capacity Trajectory Prediction

Authors

Jinwen Li,Zhongwei Deng,Yunhong Che,Yi Xie,Xiaosong Hu,Remus Teodorescu

Journal

IEEE Transactions on Transportation Electrification

Published Date

2023/11/28

The successful integration of statistical machine learning techniques into battery health diagnosis has significantly advanced the development of transportation electrification. To achieve predictive maintenance of batteries, we propose a comprehensive data-driven approach for battery capacity trajectory prediction based on degradation pattern (DP) recognition and health indicators (HIs) extrapolation. First, two HIs ( Qmean/RVmean ) are extracted from 10-minute sequence data before and after a full charge. Second, an unsupervised learning approach is employed for the early-stage battery DP analysis and clustering. Finally, a long short-term memory (LSTM) network is utilized to construct the HIs extrapolation and capacity prediction models. Multi-task learning (MTL) is implemented to predict HI sequences, enabling simultaneous extrapolation of multiple HIs and the sharing of parameters between different HIs …

See List of Professors in Yunhong Che University(Chongqing University)

Yunhong Che FAQs

What is Yunhong Che's h-index at Chongqing University?

The h-index of Yunhong Che has been 16 since 2020 and 16 in total.

What are Yunhong Che's top articles?

The articles with the titles of

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

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

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

Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection

Battery aging behavior evaluation under variable and constant temperatures with real loading profiles

Early Prediction of Lithium-Ion Batteries Lifetime via Few-Shot Learning

Machine learning to predict electrochemical impedance spectra (EIS): Can EIS be replaced by constant current techniques?

Spatial–temporal data-driven full driving cycle prediction for optimal energy management of battery/supercapacitor electric vehicles

...

are the top articles of Yunhong Che at Chongqing University.

What are Yunhong Che's research interests?

The research interests of Yunhong Che are: Energy Storage Systems, Transportation electrification, Prognostics and Health Management, Battery

What is Yunhong Che's total number of citations?

Yunhong Che has 1,380 citations in total.

What are the co-authors of Yunhong Che?

The co-authors of Yunhong Che are Remus Teodorescu, Michael G. Pecht, Kang Li, Daniel Stroe, Simona Onori.

    Co-Authors

    H-index: 104
    Remus Teodorescu

    Remus Teodorescu

    Aalborg Universitet

    H-index: 104
    Michael G. Pecht

    Michael G. Pecht

    University of Maryland

    H-index: 55
    Kang Li

    Kang Li

    University of Leeds

    H-index: 49
    Daniel Stroe

    Daniel Stroe

    Aalborg Universitet

    H-index: 46
    Simona Onori

    Simona Onori

    Stanford University

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