Weixiang Shen (沈维祥)

Weixiang Shen (沈维祥)

Swinburne University of Technology

H-index: 51

Oceania-Australia

About Weixiang Shen (沈维祥)

Weixiang Shen (沈维祥), With an exceptional h-index of 51 and a recent h-index of 46 (since 2020), a distinguished researcher at Swinburne University of Technology, specializes in the field of Battery modeling, battery management systems, electric vehicles, renewable energy integration.

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

Electrochemical Impedance Spectroscopy: A Novel High-Power Measurement Technique for Onboard Batteries Using Full-Bridge Conversion

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

Advancing fault diagnosis in next-generation smart battery with multidimensional sensors

Managing the surge: a comprehensive review of the entire disposal framework for retired Lithium-ion batteries from electric vehicles

An electric vehicle-oriented approach for battery multi-constraint state of power estimation under constant power operations

Multi-objective nonlinear observer design for multi-fault detection of lithium-ion battery in electric vehicles

Recent Advances in Model-Based Fault Diagnosis for Lithium-Ion Batteries: A Comprehensive Review

Battery Peak Power Assessment under Various Operational Scenarios: A Comparative Study

Weixiang Shen (沈维祥) Information

University

Swinburne University of Technology

Position

Professor of Electrical Engineering Australia

Citations(all)

9973

Citations(since 2020)

7196

Cited By

4614

hIndex(all)

51

hIndex(since 2020)

46

i10Index(all)

138

i10Index(since 2020)

113

Email

University Profile Page

Swinburne University of Technology

Weixiang Shen (沈维祥) Skills & Research Interests

Battery modeling

battery management systems

electric vehicles

renewable energy integration

Top articles of Weixiang Shen (沈维祥)

Electrochemical Impedance Spectroscopy: A Novel High-Power Measurement Technique for Onboard Batteries Using Full-Bridge Conversion

Authors

Kui Zhang,Rui Xiong,Siyu Qu,Boran Zhang,WX Shen

Journal

IEEE Transactions on Transportation Electrification

Published Date

2024/2/6

Electrochemical impedance spectroscopy (EIS) has received extensive attention because it can reflect the electrochemical mechanism of a battery and is one of the important references for evaluating the health status of a battery. EIS is usually obtained by sinusoidal scanning in the laboratory through professional equipment, making it hardly accessible in the actual vehicle application. This study presents a high-power impedance test system based on a full-bridge converter capable of being compatible with a battery management system. The experimental results show that the developed test system can accurately measure the EIS of a cylindrical 18650 battery with a capacity of 3.5 Ah, a pouch battery with a capacity of 10 Ah and a prismatic battery with a capacity of 50 Ah over the frequency range of 0.1-800 Hz. Their overall measurement error is less than 3%. The hardware implementation of the test system is …

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

Authors

Rui Xiong,Zian Zhao,Cheng Chen,Xinggang Li,WX Shen

Journal

Chinese Journal of Mechanical Engineering

Published Date

2024/4

Battery remaining charging time (RCT) prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles (EVs). Also, it is of great significance to improve EV users' experience. However, the RCT for a lithium-ion battery pack in EVs changes with temperature and other battery parameters. This study proposes an electrothermal model-based method to accurately predict battery RCT. Firstly, a characteristic battery cell is adopted to represent the battery pack, thus an equivalent circuit model (ECM) of the characteristic battery cell is established to describe the electrical behaviors of a battery pack. Secondly, an equivalent thermal model (ETM) of the battery pack is developed by considering the influence of ambient temperature, thermal management, and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling. Finally, the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from − 20 ℃ to 45 ℃. The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

Advancing fault diagnosis in next-generation smart battery with multidimensional sensors

Authors

Rui Xiong,Xinjie Sun,Xiangfeng Meng,Weixiang Shen,Fengchun Sun

Journal

Applied Energy

Published Date

2024/6/15

With the increasing installation of battery energy storage systems, the safety of high-energy-density battery systems has become a growing concern. Developing reliable battery fault diagnosis and fault warning algorithms is essential to ensure the safety of battery systems. After years of development, traditional fault diagnosis techniques based on three-dimensional information of voltage, current and temperature have gradually encountered bottlenecks. It is necessary to adopt a proactive approach by using mulitidimensional information to advance fault diagnosis techniques. This involves integrating advanced sensing technologies, collecting multidimensional data and uncovering subtle changes in battery behavior. This paper delves into the mechanisms and evolutionary paths of battery faults, with a specific focus on the multidimensional observable signals associated with different faults for enhanced safety …

Managing the surge: a comprehensive review of the entire disposal framework for retired Lithium-ion batteries from electric vehicles

Authors

Ruohan Guo,Feng Wang,M Akbar Rhamdhani,Yiming Xu,Weixiang Shen

Published Date

2024/2/2

Anticipating the imminent surge of retired lithium-ion batteries (R-LIBs) from electric vehicles, the need for safe, cost-effective and environmentally friendly disposal technologies has escalated. This paper seeks to offer a comprehensive overview of the entire disposal framework for R-LIBs, encompassing a broad spectrum of activities, including screening, repurposing and recycling. Firstly, we delve deeply into a thorough examination of current screening technologies, shifting the focus from a mere enumeration of screening methods to the exploration of the strategies for enhancing screening efficiency. Secondly, we outline battery repurposing with associated key factors, summarizing stationary applications and sizing methods for R-LIBs in their second life. A particular light is shed on available reconditioning solutions, demonstrating their great potential in facilitating battery safety and lifetime in repurposing …

An electric vehicle-oriented approach for battery multi-constraint state of power estimation under constant power operations

Authors

Ruohan Guo,Cungang Hu,WX Shen

Journal

IEEE Transactions on Vehicular Technology

Published Date

2024/3/15

State of power (SOP) reflects the peak power capability of a lithium-ion battery (LIB). Constant power (CP) operation (e.g., discharge or charge) is more representative of actual battery loadings in electric vehicle (EV) applications (e.g., EV acceleration, gradient climbing and regenerative braking) than constant current or constant voltage operation. However, relevant research on CP-based SOP estimation for LIBs in EVs is still rare. In this paper, a novel model switching-based iterative algorithm is proposed for multi-constraint SOP estimation under a CP operation scenario. Two state-space models with implicit representations are constructed to describe the indirect relationships between a given CP over a prediction window and the maximum look-ahead current/voltage at the end of this window. An unscented Kalman filter-based correction strategy is applied to determine the dominant limitation factor so as to …

Multi-objective nonlinear observer design for multi-fault detection of lithium-ion battery in electric vehicles

Authors

Yiming Xu,Xiaohua Ge,Weixiang Shen

Journal

Applied Energy

Published Date

2024/5

Accurate and rapid fault detection is essential for the safe operation of lithium-ion batteries in electric vehicles. However, conventional fault detection methods dependent on constant thresholds may have false alarms or missing alarms due to the inevitable disturbances resulted from the battery system modeling errors and measurement noises. In this paper, we design a multi-objective nonlinear fault detection observer for lithium-ion batteries, which is robust against disturbances but sensitive to battery multi-fault. We then perform formal stability and L∞/H _ performance analysis for the resultant estimation error system. Furthermore, tractable design procedures for the observer gain parameter and an adaptive threshold are derived. Then, via adaptive thresholding, a delicate three-step multi-fault detection scheme is developed to detect the occurrence of battery various faults, including short-circuit faults, current and …

Recent Advances in Model-Based Fault Diagnosis for Lithium-Ion Batteries: A Comprehensive Review

Authors

Yiming Xu,Xiaohua Ge,Ruohan Guo,Weixiang Shen

Published Date

2024/1

Lithium-ion batteries (LIBs) have found wide applications in a variety of fields such as electrified transportation, stationary storage and portable electronics devices. A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault diagnosis methods in advanced BMSs. This paper provides a comprehensive review on the model-based fault diagnosis methods for LIBs. First, the widely explored battery models in the existing literature are classified into physics-based electrochemical models and electrical equivalent circuit models. Second, a general state-space representation that describes electrical dynamics of a faulty battery is presented. The formulation of the state vectors and the identification of the parameter matrices are then elaborated. Third, the fault mechanisms of both battery faults (incl. overcharege/overdischarge faults, connection faults, short circuit faults) and sensor faults (incl. voltage sensor faults and current sensor faults) are discussed. Furthermore, different types of modeling uncertainties, such as modeling errors and measurement noises, aging effects, measurement outliers, are elaborated. An emphasis is then placed on the observer design (incl. online state observers and offline state observers). The algorithm implementation of typical state observers for battery fault diagnosis is also put forward. Finally, discussion and outlook are offered to envision some possible future research directions.

Battery Peak Power Assessment under Various Operational Scenarios: A Comparative Study

Authors

Ruohan Guo,Cungang Hu,WX Shen

Journal

Authorea Preprints

Published Date

2024/3/14

The peak power capability of lithium-ion batteries (LIBs), or so-called state of power (SOP), plays a decisive role for electric vehicles to fulfill a specific power-intensive task. Generally, battery SOP can be achieved based on different peak operation modes (POMs), including constant current, constant voltage, constant current and constant voltage or constant power, throughout a prediction window. However, the impact of these POMs on battery peak performance and their interrelationship remain unclear by far. In light of this, we conduct a comparative study to fill this blank. Four key indices, including maximum and minimum instant magnitudes, time-averaged magnitude and falling/rising rate, are adopted to evaluate battery peak performance under each POM. Potential factors, such as load profile, length of the prediction window and battery chemistry, are considered in the comparisons. The results offer valuable insights into the distinct attributes of these POMs and their regiondependent interrelationship.

Towards Accurate and Efficient Sorting of Retired Lithium-ion Batteries: A Data Driven Based Electrode Aging Assessment Approach

Authors

Ruohan Guo,Feng Wang,Cungang Hu,Weixiang Shen

Journal

arXiv preprint arXiv:2404.12769

Published Date

2024/4/19

Retired batteries (RBs) for second-life applications offer promising economic and environmental benefits. However, accurate and efficient sorting of RBs with discrepant characteristics persists as a pressing challenge. In this study, we introduce a data driven based electrode aging assessment approach to address this concern. To this end, a number of 15 feature points are extracted from battery open circuit voltage (OCV) curves to capture their characteristics at different levels of aging, and a convolutional neural network with an optimized structure and minimized input size is established to relocate the relative positions of these OCV feature points. Next, a rapid estimation algorithm is proposed to identify the three electrode aging parameters (EAPs) which best reconstruct the 15 OCV feature points over the entire usable capacity range. Utilizing the three EAPs as sorting indices, we employ an adaptive affinity propagation algorithm to cluster RBs without the need for pre-determining the clustering number. Unlike conventional sorting methods based solely on battery capacity, the proposed method provides profound insights into electrode aging behaviors, minimizes the need for constant-current charging data, and supports module/pack-level tests for the simultaneous processing of high volumes of RBs.

An Adaptive Neural Observer for Short Circuit Fault Estimation of Lithium-ion Batteries in Electric Vehicles

Authors

Yiming Xu,Xiaohua Ge,Weixiang Shen

Journal

IEEE Transactions on Power Electronics

Published Date

2024/1

Soft short circuit (SC) fault diagnosis is critical for a battery management system to prevent thermal runaway of lithium-ion batteries in electric vehicles. In this article, an adaptive neural network based observer is developed to estimate soft SC faults based on an augmented battery model subject to unknown nonlinear uncertainties. Rigorous theoretical analysis in terms of estimation error convergence is then provided. Leveraging the designed observer, a delicate diagnosis algorithm is presented to timely detect SC fault occurrences and an iterative updating method is further applied to accurately estimate the SC fault resistance via thresholding. Finally, comprehensive experimental tests and comparative studies are elaborated to validate the effectiveness and superiority of the proposed algorithm.

A Curve Relocation Approach for Robust Battery Open Circuit Voltage Reconstruction and Capacity Estimation Based on Partial Charging Data

Authors

Ruohan Guo,Yiming Xu,Cungang Hu,WX Shen

Journal

IEEE Transactions on Power Electronics

Published Date

2024/3

This article proposes a curve relocation approach for robust battery open circuit voltage (OCV) reconstruction and capacity estimation based on partial charging data. First, an electrode-level aging mechanism analysis is conducted to reveal the underlying reasons for battery OCV distortion and capacity decay, and three electrode aging parameters (EAPs) are proposed to account for those aging-induced relative position shifts of electrode OCV curves. Second, a deep long short-term memory recurrent neural network with a many-to-one structure is established to yield battery OCV estimations in high fidelity using accessible daily charging data. Then, a multicoupled optimization algorithm is designed to accurately estimate EAPs, which ensures that the reconstructed OCV curves match well with the estimated OCV segments while satisfying various OCV-related health features at a specific aging level. Obtaining the …

Recent Advancements in Battery State of Power Estimation Technology: A Comprehensive Overview and Error Source Analysis

Authors

Ruohan Guo,Weixiang Shen

Published Date

2024/4/19

Accurate state of power (SOP) estimation is of great importance for lithium-ion batteries in safety-critical and power-intensive applications for electric vehicles. This review article delves deeply into the entire development flow of current SOP estimation technology, offering a systematic breakdown of all key aspects with their recent advancements. First, we review the design of battery safe operation area, summarizing diverse limitation factors and furnishing a profound comprehension of battery safety across a broad operational scale. Second, we illustrate the unique discharge and charge characteristics of various peak operation modes, such as constant current, constant voltage, constant current-constant voltage, and constant power, and explore their impacts on battery peak power performance. Third, we extensively survey the aspects of battery modelling and algorithm development in current SOP estimation technology, highlighting their technical contributions and specific considerations. Fourth, we present an in-depth dissection of all error sources to unveil their propagation pathways, providing insightful analysis into how each type of error impacts the SOP estimation performance. Finally, the technical challenges and complexities inherent in this field of research are addressed, suggesting potential directions for future development. Our goal is to inspire further efforts towards developing more accurate and intelligent SOP estimation technology for next-generation battery management systems.

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

Authors

Ziyou Zhou,Yonggang Liu,Chengming Zhang,WX Shen,Rui Xiong

Journal

Journal of Energy Chemistry

Published Date

2024/3/1

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 …

An integrated cell-to-pack design based on an origami sandwich structure to enhance cooling and mechanical performances of battery packs in electric vehicles

Authors

Ruifeng Li,Guoxing Lu,Weixiang Shen,Justin Leontini

Journal

Applied Thermal Engineering

Published Date

2024/4/16

The rapid growth in the energy density of batteries in electric vehicles has led to the need for complex structural components and cooling systems to ensure the safety of the battery pack. However, these inactive materials occupy a significant portion, accounting for at least 35% of the pack's volume. To enhance the volume utilization rate of the battery pack, this paper proposes a novel origami structure with its optimized geometry that enhances thermal and mechanical performances as well as crashworthiness performance. The simulation results of temperature evaluation show that the proposed origami channels for a battery pack can reduce the maximum temperature by more than 2.6 °C at a 5C discharging rate and by over 20 °C under internal short-circuit conditions compared with a flat cooling channel. The experimental results of a quasi-static punch compression test demonstrate the enhanced mechanical …

Semi-supervised estimation of capacity degradation for lithium ion batteries with electrochemical impedance spectroscopy

Authors

Rui Xiong,Jinpeng Tian,Weixiang Shen,Jiahuan Lu,Fengchun Sun

Journal

Journal of Energy Chemistry

Published Date

2023/1/1

Machine learning-based methods have emerged as a promising solution to accurate battery capacity estimation for battery management systems. However, they are generally developed in a supervised manner which requires a considerable number of input features and corresponding capacities, leading to prohibitive costs and efforts for data collection. In response to this issue, this study proposes a convolutional neural network (CNN) based method to perform end-to-end capacity estimation by taking only raw impedance spectra as input. More importantly, an input reconstruction module is devised to effectively exploit impedance spectra without corresponding capacities in the training process, thereby significantly alleviating the cost of collecting training data. Two large battery degradation datasets encompassing over 4700 impedance spectra are developed to validate the proposed method. The results show that …

Guest Editorial: Smart operation and control of energy system for low-carbon applications

Authors

Kailong Liu,Yujie Wang,Weixiang Shen,Zhongbao Wei,Chunhui Zhao,Huazhen Fang

Published Date

2023/6/1

Guest Editorial: Smart operation and control of energy system for low-carbon applications guest Swinburne Research Bank Swinburne Research Bank Help Swinburne Research Bank > Guest Editorial: Smart operation an… Link to this page: http://hdl.handle.net/1959.3/471828 Guest Editorial: Smart operation and control of energy system for low-carbon applications Authors Liu, Kailong ; Wang, Yujie; Shen, Weixiang; Wei, Zhongbao; Zhao, Chunhui; Fang, Huazhen (Search for a current Swinburne researcher) Publisher's website https://doi.org/10.1016/j.conengprac.2023.105512 Publication year 2023 Publication type Journal article Source Control Engineering Practice, Vol. 135 (Jun 2023), 105512 ISSN 0967-0661 Publisher Elsevier BV Copyright Copyright © 2023 Details Collection: Swinburne Research Bank Version: 1 (show all) Status: Live Views: 73 Home Log in Search Browse by Publication Year Browse by …

Prosumer-centric demand side management for minimizing electricity bills in a DC residential PV-battery system: An Australian household case study

Authors

UGK Mulleriyawage,P Wang,T Rui,K Zhang,C Hu,WX Shen

Journal

Renewable Energy

Published Date

2023/3/1

Technological advancements lead to the development of a home energy management system (HEMS) to perform demand side management (DSM) in residential houses. The current study proposes a DSM strategy to minimize electricity bills in a DC residential house considering the predictions of electrical load consumption and solar photovoltaic (PV) power based on long short-term memory networks. A simulation study compares the proposed DSM strategy against four alternate scenarios, the result shows that the proposed DSM strategy has the lowest annual energy cost increment (5.81%) compared to the benchmark scenario where it is assumed to have the ideal predictions of the load consumption and solar PV power. It also shows that the predictions of the load consumption and solar PV power are essential for economically sensible DSM, however further analysis reveals that the prediction errors do not …

A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models

Authors

Aihua Tang,Yukun Huang,Shangmei Liu,Quanqing Yu,WX Shen,Rui Xiong

Journal

Applied Energy

Published Date

2023/10/15

Accurate estimating the state of charge (SOC) can improve battery reliability, safety, and extend battery service life. The existing battery models used for SOC estimation inadequately capture the dynamic characteristics of a battery in a wide temperature over the full SOC range, leading to significant inaccuracies in SOC estimation, especially in low temperature and low SOC. A novel SOC estimation approach is developed based on a fusion of neural network model and equivalent circuit model. Firstly, the weight-SOC-temperature relationship is established by obtaining the weights of the equivalent circuit model and the neural network model offline using the standard deviation weight assignment method. Following that, an online adaptive weight correction approach is implemented to update the weight-SOC-temperature relationship. Finally, a novel multi-algorithm fusion technique is utilized to achieve SOC …

An Information Analysis Based Online Parameter Identification Method for Lithium-ion Batteries in Electric Vehicles

Authors

Ruohan Guo,WX Shen

Journal

IEEE Transactions on Industrial Electronics

Published Date

2023/9/26

This article proposes an information analysis-based multiple adaptive forgetting factors (FFs) recursive least squares (IA-MAFF-RLS) method to identify model parameters of lithium-ion batteries in electric vehicles. First, a Cramer–Rao lower bound (CRLB) based information analysis is implemented for each individual model parameter, and the constant information theory is introduced to update the CRLB for the associated estimation error covariance. Second, a switching-based adaptive strategy is proposed with two rules to fine-tune multiple FFs online by making a trade-off between the information richness in a memory window and the traceability to current-voltage profiles. Third, the IA-MAFF-RLS method is established to separately identify different model parameters with associated FFs. Unlike conventional methods, the proposed element-wise forgetting strategy directly focuses on battery model parameters …

Lithium-ion battery degradation diagnosis and state-of-health estimation with half cell electrode potential

Authors

Chen Zhu,Liqing Sun,Cheng Chen,Jinpeng Tian,WX Shen,Rui Xiong

Published Date

2023/5/13

Lithium-ion batteries (LiBs) have been widely used in electric vehicles and portable electronics. However, the performance and safety of these applications are highly dependent on degradation of LiBs. In this paper, three contributions have been made to achieve reliable degradation diagnosis and State-of-Health (SOH) estimation: (1) Open-circuit voltage is reconstructed to diagnose degradation modes of LiBs by performing scaling and translation transformations on open-circuit potential curves. (2) A degradation diagnosis model is developed to quantify aging characteristics of LiBs. In this model, a segment of charging data is taken to estimate SOH and the degradation modes in a degradation path. (3) An appropriate voltage range of the charging data is selected to improve model estimation accuracy. Experimental results show that the proposed method can achieve reliable degradation diagnosis and accurate …

See List of Professors in Weixiang Shen (沈维祥) University(Swinburne University of Technology)

Weixiang Shen (沈维祥) FAQs

What is Weixiang Shen (沈维祥)'s h-index at Swinburne University of Technology?

The h-index of Weixiang Shen (沈维祥) has been 46 since 2020 and 51 in total.

What are Weixiang Shen (沈维祥)'s top articles?

The articles with the titles of

Electrochemical Impedance Spectroscopy: A Novel High-Power Measurement Technique for Onboard Batteries Using Full-Bridge Conversion

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

Advancing fault diagnosis in next-generation smart battery with multidimensional sensors

Managing the surge: a comprehensive review of the entire disposal framework for retired Lithium-ion batteries from electric vehicles

An electric vehicle-oriented approach for battery multi-constraint state of power estimation under constant power operations

Multi-objective nonlinear observer design for multi-fault detection of lithium-ion battery in electric vehicles

Recent Advances in Model-Based Fault Diagnosis for Lithium-Ion Batteries: A Comprehensive Review

Battery Peak Power Assessment under Various Operational Scenarios: A Comparative Study

...

are the top articles of Weixiang Shen (沈维祥) at Swinburne University of Technology.

What are Weixiang Shen (沈维祥)'s research interests?

The research interests of Weixiang Shen (沈维祥) are: Battery modeling, battery management systems, electric vehicles, renewable energy integration

What is Weixiang Shen (沈维祥)'s total number of citations?

Weixiang Shen (沈维祥) has 9,973 citations in total.

What are the co-authors of Weixiang Shen (沈维祥)?

The co-authors of Weixiang Shen (沈维祥) are Fengchun Sun 孙逢春, Zhihong Man, Yi Ding, N Hosseinzadeh, Udayanka Mulleriyawage.

    Co-Authors

    H-index: 72
    Fengchun Sun 孙逢春

    Fengchun Sun 孙逢春

    Beijing Institute of Technology

    H-index: 53
    Zhihong Man

    Zhihong Man

    Swinburne University of Technology

    H-index: 51
    Yi Ding

    Yi Ding

    Zhejiang University

    H-index: 29
    N Hosseinzadeh

    N Hosseinzadeh

    Deakin University

    H-index: 4
    Udayanka Mulleriyawage

    Udayanka Mulleriyawage

    Swinburne University of Technology

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