Xin Sui

Scopus Publications

Identification of Lithium-Ion Battery Degradation Mechanisms During Pulsed Current Charging Through Non-invasive and Post-Mortem Analysis
Siyu Jin, Jia Guo, Xin Sui, Xinrong Huang, Shunli Wang, Xiangming He, Daniel-Ioan Stroe
Automotive Innovation, 2026
Battery Time-Series Data Augmentation Using a GAF-Based Generative Framework
Opy Das, Xin Sui, Filippo Sanfilippo, Souman Rudra
Proceedings Eurocon 2025 21st International Conference on Smart Technologies, 2025
Generative models have gained significant attention in computer vision and natural language processing for their ability to generate realistic samples from complex data distributions. Inspired by these advancements and the persistent challenge of data scarcity in time-series domains, this work proposes a pioneering framework that transforms time-series data (e.g., voltage, current, temperature of lithium-ion batteries) into Gramian Angular Field (GAF) images for synthetic data generation using deep convolutional generative adversarial network (DCGAN) architecture. The proposed GAF-based GAN framework, validated on the Massachusetts Institute of Technology (MIT) battery dataset, generates samples that closely resemble real data and enables accurate signal reconstruction through inverse GAF transformation, effectively overcoming scale invariance and invertibility issues. To further demonstrate the quality of the synthetic signals, state of charge estimation (SOC) is performed using both original and augmented datasets. The results present that using augmented datasets help the models perform better than when trained only on the original data, highlighting the effectiveness of the proposed method for improving time-series data augmentation and state estimation.
Sensorless Temperature Monitoring of Lithium-Ion Batteries by Integrating Physics With Machine Learning
Yusheng Zheng, Yunhong Che, Xiaosong Hu, Xin Sui, Remus Teodorescu
IEEE Transactions on Transportation Electrification, 2024
The large-scale application of lithium-ion batteries in electric vehicles requires meticulous battery management to guarantee vehicular safety and performance. Temperatures play a significant role in the safety, performance, and lifetime of lithium-ion batteries. Therefore, the state of temperature (SOT) of batteries should be monitored timely by the battery management system. Due to limited onboard temperature sensors in electric vehicles, the SOT of most batteries must be estimated through other measured signals such as current and voltage. To this end, this paper develops an accurate method to estimate the surface temperature of batteries by combing the physics-based thermal model with machine learning. A lumped-mass thermal model is applied to provide prior knowledge of battery temperatures for machine learning. Temperature-related feature, such as internal resistance, is extracted in real-time and fed into the machine learning framework as supplementary inputs to enhance the accuracy of the estimation. A machine learning model, which combines a convolutional neural network with a long short-term memory neural network, is sequentially integrated with the thermal model to learn the mismatch between the model outputs and the real temperature values. The proposed method has been verified against experimental results, with accuracy improvement of 79.37% and 86.24% compared to conventional pure thermal model-based and pure data-driven approaches respectively.
Small-Sample-Learning-Based Lithium-Ion Batteries Health Assessment: An Optimized Ensemble Framework
Xin Sui, Shan He, Remus Teodorescu
IEEE Transactions on Industry Applications, 2024
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 the conventional BaggELM. Compared to other classic machine learning methods, the proposed method reduces the required data size, maintains strong robustness and high estimation accuracy, making it a promising solution for small-sample-based SOH estimation. Finally, the effectiveness of the proposed estimation framework is verified using the accelerated aging dataset from Lithium Iron Phosphate (LFP) and Nickel Manganese Cobalt (NMC) batteries.
Battery state-of-health estimation using machine learning
Daniel-Ioan Stroe, Xin Sui
Control of Power Electronic Converters and Systems Volume 4, 2024
Combing physics-based thermal model and machine learning for battery temperature estimation: The impact of model accuracy
Yusheng Zheng, Yunhong Che, Xin Sui, Remus Teodorescu
2024 IEEE 10th International Power Electronics and Motion Control Conference Ipemc 2024 Ecce Asia, 2024
Temperature significantly impacts the safety, performance, and degradation of lithium-ion batteries (LIBs), and therefore should be monitored properly by the battery management system (BMS). Hybrid estimation methods by combining physics-based thermal models and machine learning (ML) algorithms, become very promising for sensorless temperature estimation given the limited number of onboard temperature sensors. In this hybrid estimation framework, the physics-based thermal model provides prior knowledge for the ML algorithm to help achieve an accurate final estimation. Therefore, the impact of model accuracy on the overall estimation performance needs to be investigated comprehensively. To this end, this paper investigated the performance of the hybrid estimation framework under different model accuracies, which stem from parameter uncertainties and unmodeled dynamics. Results suggest that the hybrid estimation model can still achieve high accuracy even though trained with inaccurate prior knowledge, demonstrating its robustness to different uncertainties.
Online Sensorless Temperature Estimation of Lithium-Ion Batteries Through Electro-Thermal Coupling
Yusheng Zheng, Yunhong Che, Xiaosong Hu, Xin Sui, Remus Teodorescu
IEEE ASME Transactions on Mechatronics, 2024
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 and the extended Kalman filter to estimate the battery temperature by merely using the voltage measurement as feedback. The electrothermal coupling effect enables the full observability of batteries’ internal states from their voltage, and contributes to an accurate and robust temperature estimation. The capability of the proposed estimation method has been demonstrated via experiments, with root-mean-square error less than 0.7 °C in various scenarios.
AI for Smart Battery State Estimation: A Perspective
X. Sui, Y. Che, Y. Zheng, N. André Weinreich, S. He, R. Teodorescu
2024 IEEE 10th International Power Electronics and Motion Control Conference Ipemc 2024 Ecce Asia, 2024
In battery management systems (BMSs), state estimation stands as a pivotal element yet encounters significant challenges. These include the poor observability inherent in fixed configuration battery packs, limited generalizability of pre-trained machine learning models, and the deficiency of higher-level management strategies. To address these obstacles, we propose a forward-looking perspective on the future BMS state estimation, introducing the concept of a "Smart Battery". Battery digital twin enables synthetic data generation and physics-informed AI development. This approach integrates battery digital twin to generate synthetic data, which is then used for data augmentation and physics-informed AI development. Additionally, it incorporates advanced data cleaning and selection techniques to preserve essential information and augment data management efficiency. Leveraging cutting-edge AI algorithms, such as transfer learning and meta-learning, aims to mitigate issues of model generalization and feature invalidation under various operating conditions. Furthermore, this paper emphasizes the importance of multi-task learning for batteries, enabling comprehensive health assessments. By fully utilizing both short-term estimations and long-term predictions, the proposed framework contributes to the advancement of higher-level health and thermal management designs. We aim to furnish pioneering insights for state estimation in future intelligent BMSs.
Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection
Yunhong Che, Yusheng Zheng, Florent Evariste Forest, Xin Sui, Xiaosong Hu, Remus Teodorescu
Reliability Engineering and System Safety, 2024
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 the slope is predicted together with the state of health via multi-task learning. A cloud-edge framework is considered where fine-tuning is adopted for performance improvement during cycling. The proposed general framework is flexible for adjustment to different practical requirements and can be extrapolated to other batteries aged under different conditions. The results indicate that the early predictions are improved using the proposed method compared to multiple single feature-based benchmarks, and that integration of the algorithm is improved. The sequence prediction is reliable for different predicted lengths with root mean square errors of less than 1.41%, and the detection of accelerating aging can guide reliable predictive health management.
Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities
Yusheng Zheng, Yunhong Che, Xiaosong Hu, Xin Sui, Daniel-Ioan Stroe, Remus Teodorescu
Progress in Energy and Combustion Science, 2024
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 this end, this paper provides a comprehensive review of temperature estimation techniques in battery systems regarding their mechanism, framework, and representative studies. The potential metrics used to characterize battery thermal states are discussed in detail at first considering the spatiotemporal attributes of battery temperature, and the strengths and weaknesses of applying such metrics in battery management are also analyzed. Afterward, various temperature estimation methods, including impedance/resistance-based, thermal model-based, and data-driven estimations, are elucidated, analyzed, and compared in terms of their strengths, limitations, and potential improvements. Finally, the key challenges to battery thermal state monitoring in real applications are identified, and future opportunities for removing these barriers are presented and discussed.
Real-Time Sensorless Temperature Estimation of Lithium-Ion Batteries Based on Online Operando Impedance Acquisition
Yusheng Zheng, Yunhong Che, Jia Guo, Nicolai André Weinreich, Abhijit Kulkarni, Ahsan Nadeem, Xin Sui, Remus Teodorescu
IEEE Transactions on Power Electronics, 2024
Boosting battery state of health estimation based on self-supervised learning
Yunhong Che, Yusheng Zheng, Xin Sui, Remus Teodorescu
Journal of Energy Chemistry, 2023
State of Health Estimation for Smart Batteries using Transfer Learning with Data Cleaning
Yunhong Che, Xin Sui, Remus Teodorescu
IFAC Papersonline, 2023
Battery health prognostic with sensor-free differential temperature voltammetry reconstruction and capacity estimation based on multi-domain adaptation
Yunhong Che, Søren Byg Vilsen, Jinhao Meng, Xin Sui, Remus Teodorescu
Etransportation, 2023
Ultrafast Feature Extraction for Lithium-Ion Battery Health Assessment
Xin Sui, Shan He, Remus Teodorescu
2023 25th European Conference on Power Electronics and Applications EPE 2023 Ecce Europe, 2023
Statistical Post-Processing in Ensemble Learning-based State of Health Estimation for Lithium-Ion Batteries
Xin Sui, Shan He, Remus Teodorescu
Icpe 2023 Ecce Asia 11th International Conference on Power Electronics Ecce Asia Green World with Power Electronics, 2023
Hardware Design of High Current Prismatic Smart Battery Packs
A. Kulkarni, R. Teodorescu, X. Sui, A. Oshnoei
Iet Conference Proceedings, 2023
Hyperparameter Optimization in Bagging-Based ELM Algorithm for Lithium-Ion Battery State of Health Estimation
Xin Sui, Shan He, SØren Byg Vilsen, Remus Teodorescu, Daniel-Ioan Stroe
Conference Proceedings IEEE Applied Power Electronics Conference and Exposition APEC, 2023
Early Prediction of Lithium-Ion Batteries Lifetime via Few-Shot Learning
Xin Sui, Shan He, Yusheng Zheng, Yunhong Che, Remus Teodorescu
IECON Proceedings Industrial Electronics Conference, 2023
Sensorless Temperature Estimation for Lithium-ion Batteries via Online Impedance Acquisition
Y. Zheng, N. A. Weinreich, A. Kulkarni, X. Sui, R. Teodorescu
Iet Conference Proceedings, 2023
Battery Aging Behavior Evaluation under Variable and Constant Temperatures with Real Loading Profiles
Yunhong Che, Daniel-Ioan Stroe, Xin Sui, Sϕren Byg Vilsen, Xiaosong Hu, Remus Teodorescu
Conference Proceedings IEEE Applied Power Electronics Conference and Exposition APEC, 2023
Features extraction for battery SOH estimation from battery pulsed charging operation
Siyu Jin, Xinming Yu, Xin Sui, Wendi Guo, Maitane Berecibar, Daniel-Ioan Stroe
2023 25th European Conference on Power Electronics and Applications EPE 2023 Ecce Europe, 2023
Sensorless State of Temperature Estimation for Smart Battery based on Electrochemical Impedance
Yusheng Zheng, Nicolai André Weinreich, Abhijit Kulkarni, Yunhong Che, Hoda Sorouri, Xin Sui, Remus Teodorescu
2023 25th European Conference on Power Electronics and Applications EPE 2023 Ecce Europe, 2023
Li-ion Battery Digital Twin Based on Online Impedance Estimation
Abhijit Kulkarni, Hoda Sorouri, Yusheng Zheng, Xin Sui, Arman Oshnoei, Nicolai André Weinreich, Remus Teodorescu
Cpe Powereng 2023 17th IEEE International Conference on Compatibility Power Electronics and Power Engineering, 2023
Unravelling and quantifying the aging processes of commercial Li(Ni0.5Co0.2Mn0.3)O2/graphite lithium-ion batteries under constant current cycling
Jia Guo, Siyu Jin, Xin Sui, Xinrong Huang, Yaolin Xu, Yaqi Li, Peter Kjær Kristensen, Deyong Wang, Kjeld Pedersen, Leonid Gurevich, Daniel-Ioan Stroe
Journal of Materials Chemistry A, 2022
A Review of Lithium-Ion Battery Capacity Estimation Methods for Onboard Battery Management Systems: Recent Progress and Perspectives
Jichang Peng, Jinhao Meng, Dan Chen, Haitao Liu, Sipeng Hao, Xin Sui, Xinghao Du
Batteries, 2022
Smart Battery Technology for Lifetime Improvement
Remus Teodorescu, Xin Sui, Søren B. Vilsen, Pallavi Bharadwaj, Abhijit Kulkarni, Daniel-Ioan Stroe
Batteries, 2022
Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network
Yunhong Che, Yusheng Zheng, Yue Wu, Xin Sui, Pallavi Bharadwaj, Daniel-Ioan Stroe, Yalian Yang, Xiaosong Hu, Remus Teodorescu
Applied Energy, 2022
Battery Lifetime Prediction and Degradation Reconstruction based on Probabilistic Convolutional Neural Network
Yunhong Che, Xin Sui, Pallavi Bharadwaj, Daniel-Ioan Stroe, Remus Teodorescu
2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems Pedg 2022, 2022
Robust Fuzzy Entropy-Based SOH Estimation for Different Lithium-Ion Battery Chemistries
Xin Sui, Shan He, Alejandro Gismero, Remus Teodorescu, Daniel-Ioan Stroe
2022 IEEE Energy Conversion Congress and Exposition Ecce 2022, 2022
Lifetime evaluation of lithium-ion batteries under pulsed charging currents
Siyu Jin, Xin Sui, Xinrong Huang, Shunli Wang, Remus Teodores, Daniel-Ioan Stroe
2022 IEEE Energy Conversion Congress and Exposition Ecce 2022, 2022
Overview of machine learning methods for lithium-ion battery remaining useful lifetime prediction
Siyu Jin, Xin Sui, Xinrong Huang, Shunli Wang, Remus Teodorescu, Daniel-Ioan Stroe
Electronics Switzerland, 2021
A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery
Xin Sui, Shan He, Søren B. Vilsen, Jinhao Meng, Remus Teodorescu, Daniel-Ioan Stroe
Applied Energy, 2021
Multidimensional Machine Learning Balancing in Smart Battery Packs
Roberta Di Fonso, Xin Sui, Anirudh Budnar Acharya, Remus Teodorescu, Carlo Cecati
IECON Proceedings Industrial Electronics Conference, 2021
Overview of Methods for Battery Lifetime Extension
2021 23rd European Conference on Power Electronics and Applications EPE 2021 Ecce Europe, 2021
Fuzzy Entropy-Based State of Health Estimation for Li-Ion Batteries
Xin Sui, Shan He, Jinhao Meng, Remus Teodorescu, Daniel-Ioan Stroe
IEEE Journal of Emerging and Selected Topics in Power Electronics, 2021
The degradation behavior of lifepo4/c batteries during long-term calendar aging
Xin Sui, Maciej Świerczyński, Remus Teodorescu, Daniel-Ioan Stroe
Energies, 2021
Fast and Robust Estimation of Lithium-ion Batteries State of Health Using Ensemble Learning
Xin Sui, Shan He, Seren Byg Vilsen, Remus Teodorescu, Daniel-Ioan Stroe
2021 IEEE Energy Conversion Congress and Exposition Ecce 2021 Proceedings, 2021
Smart Battery Concept: A Battery that Can Breathe
Iet Conference Proceedings, 2021
On the feature selection for battery state of health estimation based on charging–discharging profiles
Yuanyuan Li, Daniel-Ioan Stroe, Yuhua Cheng, Hanmin Sheng, Xin Sui, Remus Teodorescu
Journal of Energy Storage, 2021
A Time-Varying Log-linear Model for Predicting the Resistance of Lithium-ion Batteries
Soren B. Vilsen, Xin Sui, Daniel-Ioan Stroe
2020 IEEE 9th International Power Electronics and Motion Control Conference Ipemc 2020 Ecce Asia, 2020
Torque Ripple Suppression of a Five-phase Induction Motor under Single-phase Open
Shan He, Xin Sui, Dao Zhou, Frede Blaabjerg
2020 IEEE 9th International Power Electronics and Motion Control Conference Ipemc 2020 Ecce Asia, 2020
State of Health Estimation for Lithium-ion Battery Using Fuzzy Entropy and Support Vector Machine
Xin Sui, Shan He, Daniel-Ioan Stroe, Remus Teodorescu
2020 IEEE 9th International Power Electronics and Motion Control Conference Ipemc 2020 Ecce Asia, 2020
Data smoothing in Fuzzy Entropy-based Battery State of Health Estimation
Xin Sui, Shan He, Xinrong Huang, Remus Teodorescu, Daniel-Ioan Stroe
IECON Proceedings Industrial Electronics Conference, 2020
Fuzzy Entropy-Based State of Health Estimation of LiFePO4Batteries Considering Temperature Variation
Xin Sui, Shan He, Jinhao Meng, Remus Teodorescu, Daniel-Ioan Stroe
Ecce 2020 IEEE Energy Conversion Congress and Exposition, 2020
The Effect of Pulsed Current on the Performance of Lithium-ion Batteries
Xinrong Huang, Yuanyuan Li, Jinhao Meng, Xin Sui, Remus Teodorescu, Daniel-Ioan Stroe
Ecce 2020 IEEE Energy Conversion Congress and Exposition, 2020
Wireless Smart Battery Management System for Electric Vehicles
Xinrong Huang, Anirudh Budnar Acharya, Jinhao Meng, Xin Sui, Daniel-Ioan Stroe, Remus Teodorescu
Ecce 2020 IEEE Energy Conversion Congress and Exposition, 2020
Torque ripple minimization of seven-phase induction motor under more-than-two-phase fault
Shan He, Xin Sui, Dao Zhou, Frede Blaabjerg
Proceedings 2020 International Conference on Electrical Machines ICEM 2020, 2020
Zero Torque Ripple Operation of Seven-phase Concentrated-full-pitch Winding Induction Motor under Open Circuit faults
Shan He, Xin Sui, Dao Zhou, Frede Blaabjerg
IEEE International Symposium on Industrial Electronics, 2020
A review of pulsed current technique for lithium-ion batteries
Xinrong Huang, Yuanyuan Li, Anirudh Budnar Acharya, Xin Sui, Jinhao Meng, Remus Teodorescu, Daniel-Ioan Stroe
Energies, 2020
Lithium-ion Battery State of Health Estimation Using Empirical Mode Decomposition Sample Entropy and Support Vector Machine
Xin Sui, Shan He, Daniel-Ioan Stroe, Remus Teodorescu
Conference Proceedings IEEE Applied Power Electronics Conference and Exposition APEC, 2020
Torque Ripple Minimization of a Five-Phase Induction Motor under Open-Phase Faults Using Symmetrical Components
Shan He, Xin Sui, Zijian Liu, Min Kang, Dao Zhou, Frede Blaabjerg
IEEE Access, 2020
Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles
Jinhao Meng, Lei Cai, Daniel-Ioan Stroe, Guangzhao Luo, Xin Sui, Remus Teodorescu
Energy, 2019
A Review of Management Architectures and Balancing Strategies in Smart Batteries
Xinrong Huang, Xin Sui, Daniel-Ioan Stroe, Remus Teodorescu
IECON Proceedings Industrial Electronics Conference, 2019
The effect of voltage dataset selection on the accuracy of entropy-based capacity estimation methods for lithium-ion batteries
Xin Sui, Daniel-Ioan Stroe, Shan He, Xinrong Huang, Jinhao Meng, Remus Teodorescu
Applied Sciences Switzerland, 2019
Comparative study of different fault-tolerant control strategies for a five-phase concentrated-full-pitch winding induction motor
Shan He, Xin Sui, Frede Blaabjerg
2019 21st European Conference on Power Electronics and Applications EPE 2019 Ecce Europe, 2019
Optimization of the discharge cut-off voltage in LiFePO4 battery packs
Xin Sui, Shan He, Jinhao Meng, Daniel-Ioan Stroe, Xinrong Huang, Remus Teodorescu
2019 21st European Conference on Power Electronics and Applications EPE 2019 Ecce Europe, 2019
A review of sliding mode observers based on equivalent circuit model for battery SoC estimation
Xin Sui, Shan He, Daniel-Ioan Stroe, Xinrong Huang, Jinhao Meng, Remus Teodorescu
IEEE International Symposium on Industrial Electronics, 2019

Xin Sui

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