Article
Automation & Control Systems
Zhongbao Wei, Hongwen He, Josep Pou, Kwok-Leung Tsui, Zhongyi Quan, Yunwei Li
Summary: The article focuses on noise effect compensation and online parameter identification for the widely used equivalent circuit model of lithium-ion batteries. A novel degree of freedom (DOF) eliminator is proposed to coestimate noise statistics and unbiased model parameters in a recursive fashion. The proposed method effectively mitigates noise-induced biases and outperforms existing methods in terms of accuracy and robustness to noise corruption.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Wenjie Zhang, Liye Wang, Lifang Wang, Chenglin Liao, Yuwang Zhang
Summary: This article presents a joint state-of-charge (SOC) and state-of-available-power (SOAP) estimation method based on online battery model parameter identification. The improved ABSE achieves higher accuracy than the ABSE at different battery aging states, and it can accurately estimate the SOC and SOAB-based SOAP.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Review
Energy & Fuels
Md Ohirul Qays, Yonis Buswig, Md Liton Hossain, Ahmed Abu-Siada
Summary: This paper presents a comprehensive review on the most recent classifications and mathematical models for SOC estimation and also discusses future trends for SOC estimation methods.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2022)
Article
Energy & Fuels
Yongjie Zhu, Jiajun Chen, Ling Mao, Jinbin Zhao
Summary: This paper presents a model identification method based on two-swarm cooperative particle swarm optimization, which designs an adaptive dynamic sliding window to improve identification robustness and effectively enhances the accuracy and speed of parameter identification through optimization of data fragments and particle update rules.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Thermodynamics
Emanuele Buchicchio, Alessio De Angelis, Francesco Santoni, Paolo Carbone, Francesco Bianconi, Fabrizio Smeraldi
Summary: Estimating the state of charge (SOC) of batteries is crucial for the proper functioning and safety of various systems. This study proposes a SOC estimation approach based on electrochemical impedance spectroscopy (EIS) and an equivalent circuit model. Through experimental validation, the approach achieves over 93% accuracy and has the advantage of efficient model training. The resulting low-dimensional classification model can be embedded into battery-powered systems for online SOC estimation.
Article
Thermodynamics
Tiancheng Ouyang, Peihang Xu, Jingxian Chen, Zixiang Su, Guicong Huang, Nan Chen
Summary: A new adaptive H-infinity filter algorithm is proposed for accurate and efficient state-of-charge estimation of lithium-ion batteries. Experiments show that the proposed algorithm outperforms other joint estimation algorithms in terms of estimation accuracy and computational efficiency under various temperatures.
Article
Energy & Fuels
Lluis Trilla, Lluc Canals Casals, Jordi Jacas, Pol Paradell
Summary: Lithium-Sulfur is a promising technology for the next generation of batteries. However, the behavior of lithium-sulfur batteries has a large non-observable region that can make the convergence of state estimation algorithms difficult. To overcome this issue, a dual Extended Kalman Filter is proposed, combining parameter estimation and a cell model with non-linear behavior. This filter is applied to estimate the state of charge of lithium-sulfur batteries, taking into account degradation, temperature, and self-discharge effects.
Article
Automation & Control Systems
Kandasamy Varatharajalu, Mathankumar Manoharan, Thamil Selvi C. Palanichamy, Sivaranjani Subramani
Summary: This manuscript proposes a hybrid method, WSO-HDLNN, for measuring the battery's dynamic electrical response as it is compressed by an external force. The proposed method aims to reduce the battery-voltage error by combining the War Strategy Optimization algorithm and Hierarchical Deep Learning Neural Network. The results show that the proposed method outperforms existing approaches in terms of computation time and error.
Article
Energy & Fuels
F. Hipolito, C. A. Vandet, J. Rich
Summary: The increasing interest in electric vehicle (EV) charging situation has led to the development of models that approximate the distribution of State-of-Charge (SoC) levels for EVs. These models help understand charging needs and provide crucial information for vehicle-to-grid (V2G) applications. The study suggests that a full electrification of the vehicle fleet could stress the power grid but also offers potential for deploying V2G as a mechanism to dampen high-demand effects.
Article
Chemistry, Physical
Jonas A. Braun, Rene Behmann, David Schmider, Wolfgang G. Bessler
Summary: Accurately diagnosing the SOC and SOH of batteries is crucial for battery users and manufacturers. This study presents a new algorithm that uses battery voltage as input for a voltage-controlled model to accurately estimate SOC and SOH. The algorithm is self-calibrating, robust against cell aging, allows SOH estimation from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.
JOURNAL OF POWER SOURCES
(2022)
Article
Engineering, Electrical & Electronic
Bikash Sah, Praveen Kumar
Summary: Accurate estimation of battery parameters and states is crucial for enhancing safety and reliability. This study proposes charger-side online parameter and state estimation algorithms based on impedance and equivalent circuit parameters determined during charging. The experimental results demonstrate the effectiveness of the proposed algorithms for estimating battery parameters and states for different charging scenarios. The algorithms are also applicable to other types of Li-ion batteries.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Engineering, Aerospace
Min Young Yoo, Jung Heon Lee, Joo-Ho Choi, Jae Sung Huh, Woosuk Sung
Summary: This paper presents a framework for accurately estimating SOC and current sensor bias in a hybrid propulsion urban air mobility (UAM) system. Realistic test profiles reflecting actual operational scenarios for the UAM are used to model the battery and validate the state estimator. The framework ensures reliable state estimation, even during transitions between operational modes, by concurrently estimating and correcting the current sensor bias.
Article
Energy & Fuels
Fazel Mohammadi
Summary: This paper presents a mathematical model to accurately estimate the State-of-Charge (SoC) of a Lithium-ion battery based on an improved Coulomb-Counting algorithm and uncertainty evaluation over a ten-year period. Experimental results indicate a maximum estimation error of 0.3%, demonstrating the high accuracy compared to other methods.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Menaa Nawaz, Jameel Ahmed, Ghulam Abbas
Summary: Increasing demand for low-power consumer electronics and wearable medical devices has led to the need for improved energy efficiency in managing small rechargeable cells. This research proposes an improved battery management system for medical devices, utilizing energy efficient DC-DC converters and cell balancing techniques. The study evaluates different active and passive techniques for balancing the state of charge in lithium-ion batteries and optimizes total pack energy. By using an equivalent circuit model for battery modeling and parameter estimation, the system can accurately identify the battery's state of health and capacity in real-time. Experimental results demonstrate that the proposed energy-efficient battery management system saves energy and improves cell balancing in medical devices.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Electrochemistry
Baohe Yuan, Binger Zhang, Xiang Yuan, Jingyi Wang, Lulu Chen, Lei Bai, Shijun Luo
Summary: Battery management system (BMS) plays a crucial role in battery applications, and accurate estimation of state of charge (SOC) for lithium-ion batteries is of utmost importance. The relationship between open circuit voltage (OCV) and SOC needs to be real-time and accurate. This research focuses on studying the relaxation behavior of battery OCV, with a particular emphasis on the relationship between time constant and polarization resistance with SOC during relaxation.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2022)
Article
Automation & Control Systems
Dongchen Liu, Junzheng Wang, Dawei Shi, Hongwen He, Huaihang Zheng
Summary: This work proposes a force control strategy for a wheel-legged robotic system to adjust its posture on uneven roads. The strategy utilizes a dynamic model and feedback information to calculate desired leg forces, which are used as tracking references. The control scheme is based on the funnel control scheme and incorporates an event-triggering condition to improve system robustness. Experimental results demonstrate the stability and effectiveness of the proposed methodology.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Jianwei Li, Weitao Zou, Qingqing Yang, Zhongbao Wei, Hongwen He
Summary: Fuel cell based combined heat and power (FC-CHP) system is a promising distributed energy solution in south of China to meet the high power demand without central heating. A dynamic heat/power switching strategy and a new energy management strategy are proposed to improve system efficiency and maximize stakeholder benefits. This research reduces fuel cell degradation and achieves energy consumption economy in a practical example in Jiangsu province.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Engineering, Electrical & Electronic
Yu Zeng, Ali I. Maswood, Josep Pou, Xin Zhang, Zhan Li, Changjiang Sun, Suvajit Mukherjee, Amit K. Gupta, Jiaxin Dong
Summary: The dual-active-bridge converter is a popular solution for integrating energy storage systems and dc microgrids. An artificial neural network-based active disturbance rejection control is proposed to regulate constant output voltage quickly and accurately under different operating conditions. The proposed controller improves dynamic performance and reduces the number of current sensors.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Article
Chemistry, Physical
Zhongbao Wei, Xiaofeng Yang, Yang Li, Hongwen He, Weihan Li, Dirk Uwe Sauer
Summary: This paper proposes a machine learning-based fast charging strategy for lithium-ion batteries. By using a reduced-order electrochemical-thermal model in the cloud, the soft actor-critic deep reinforcement learning algorithm is exploited to train the strategy. Hardware-in-Loop tests and experiments show that the proposed strategy effectively mitigates risks and improves the safety and longevity of batteries during fast charging. Compared to the commonly-used empirical protocol, the proposed approach extends the battery cycle life by about 75%.
ENERGY STORAGE MATERIALS
(2023)
Article
Chemistry, Physical
Ran Li, Binyu Xiong, Shaofeng Zhang, Xinan Zhang, Yulin Liu, Herbert Iu, Tyrone Fernando
Summary: This paper proposes a highly accurate data-driven modelling approach for VRB in power engineering studies. It overcomes the problem of high model dependency encountered by existing VRB modelling methods. By using experimentally trained CNN, it directly learns the behavioural relationship between VRB variables, simplifying the studies of electrical systems that integrate VRB with improved accuracy. The validity of the proposed approach is verified by experimental results, comparing and analyzing the performances of 2D-CNN and 1D-CNN on VRB modelling.
JOURNAL OF POWER SOURCES
(2023)
Article
Chemistry, Physical
Hao Wang, Wen L. Soong, S. Ali Pourmousavi, Xinan Zhang, Nesimi Ertugrul, Bingyu Xiong
Summary: This paper presents a comprehensive thermal model of a 5 kW/60 kWh VRFB system and proposes a room temperature model to analyze the feasibility of using air conditioners for effective thermal management. Two case studies are presented to evaluate the performance of the proposed model and an improved cooling strategy is proposed and validated. The simulation results show significant energy savings and the modeling work is useful for studying the thermal dynamics and guiding real-world applications of VRFBs.
JOURNAL OF POWER SOURCES
(2023)
Article
Chemistry, Analytical
Jianshuai Feng, Tianyu Shi, Yuankai Wu, Xiang Xie, Hongwen He, Huachun Tan
Summary: This paper presents a new approach to tackle the problem of multi-lane differential variable speed limit control in transportation systems using deep reinforcement learning. It proposes using a covariance matrix adaptation evolution strategy (CMA-ES) to optimize the control task. Experimental results show that the proposed method outperforms other deep reinforcement learning-based and traditional evolutionary search methods in terms of average travel time, emissions, and control performance.
Article
Green & Sustainable Science & Technology
Kunang Li, Chunchun Jia, Xuefeng Han, Hongwen He
Summary: In this paper, a novel EMS based on a twin delayed deep deterministic policy gradient algorithm (TD3) is proposed for a fuel cell hybrid electric bus (FCHEB) to optimize the driving cost of the vehicle. The TD3-based EMS is compared and analyzed with the deep deterministic policy gradient algorithm (DDPG)-based EMS using real-world collected driving conditions as training data. The results show that the TD3-based EMS has higher training efficiency, learning ability, and lower overall vehicle operating cost compared to the DDPG-based EMS, validating the effectiveness of the proposed strategy.
Article
Engineering, Electrical & Electronic
Hein Wai Yan, Glen G. Farivar, Neha Beniwal, Hossein Dehghani Tafti, Salvador Ceballos, Josep Pou, Georgios Konstantinou
Summary: In stand-alone dc microgrids, conventional use of battery energy storage systems for voltage regulation leads to continuous battery operation. This study proposes a control strategy to prolong battery lifetime by reducing charging current and maintaining lower state-of-charge (SoC) values if the PV power is sufficient. Experimental tests validate the dynamic performance of the proposed strategy, while simulated case studies evaluate its effectiveness in extending the life of a lithium-ion (Li-ion) battery.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Article
Automation & Control Systems
Huawei Yuan, Hin Sang Lam, Neha Beniwal, Siew-Chong Tan, Josep Pou, Shu-Yuen Ron Hui
Summary: Capacitor voltage imbalance in NPC converters is a well-known problem, and existing solutions are either costly in terms of hardware or involve complex algorithms. This article proposes a new control method called direct-switch duty-cycle control (DSDCC) that addresses the issue and achieves near-optimal performance with simple implementation. By directly considering the duty cycles of switches, DSDCC can generate gating signals with only one carrier and derive near-optimal duty cycles, simplifying the controller design. Experimental results validate the effectiveness of DSDCC with a 5L-NPC converter.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Qingxiang Liu, Ezequiel Rodriguez, Glen Ghias Farivar, Josep Pou, Ramon Leyva, Christopher D. D. Townsend
Summary: This article proposes a discontinuous modulation (DM) scheme for cascaded H-bridge static compensators with star configuration. The scheme ensures zero steady-state active power in each converter phase arm, even in the presence of severe grid imbalance conditions. It also offers a more flexible operation by clamping to the zero-voltage level, resulting in lower zero-sequence-voltage injection requirements and switching losses. The proposed scheme is experimentally verified and compared with traditional modulation schemes under different grid voltage conditions.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Review
Engineering, Electrical & Electronic
Qian Xiao, Yu Jin, Hongjie Jia, Yi Tang, Allan Fagner Cupertino, Yunfei Mu, Remus Teodorescu, Frede Blaabjerg, Josep Pou
Summary: This article provides a comprehensive review of fault diagnosis and fault-tolerant control methods for MMC under submodule failures. A comparison of different fault diagnosis methods is conducted and verification results are provided to analyze the advantages and disadvantages of popular fault-tolerant control methods. The review concludes with a discussion of future trends and research opportunities.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Chemistry, Physical
Yulin Liu, Tianhao Qie, Xinan Zhang, Hao Wang, Zhongbao Wei, Herbert H. C. Iu, Tyrone Fernando
Summary: This paper proposes a novel learning-based data-driven H infinity control approach for enhancing the stability and anti-interference ability of Vanadium redox flow battery (VRB) in microgrids. The proposed method uses a new integral reinforcement learning algorithm to achieve excellent steady-state and dynamic responses solely based on measurements. It is insensitive to model parameter variations and eliminates the need for offline neural network training, unlike most existing artificial intelligent control approaches. Additionally, the proposed control strategy ensures guaranteed closed-loop control stability, a feat not achieved by most control methods relying on offline trained neural networks.
JOURNAL OF POWER SOURCES
(2023)
Article
Engineering, Electrical & Electronic
Bellamkonda Dwiza, Kalaiselvi Jayaraman, Naga Brahmendra Yadav Gorla, Samarjeet Singh, Josep Pou
Summary: In recent years, the dual active bridge (DAB) dc-dc converter with integrated transformers has been explored due to its advantages of low component count, high power density, and high efficiency. This article presents a detailed analysis of the common-mode (CM) noise performance of the DAB converter with an integrated transformer. It proposes a concentric CM choke that occupies less volume compared to its equivalent conventional CM choke and provides design guidelines for it. Experimental validation is conducted on a DAB converter prototype.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Article
Energy & Fuels
Binyu Xiong, Jinrui Tang, Yang Li, Peng Zhou, Shaofeng Zhang, Xinan Zhang, Chaoyu Dong, Hoay Beng Gooi
Summary: This paper presents a novel data-driven approach that incorporates flow rates into VRB modeling, enhancing data processing capabilities and prediction accuracy. The proposed model adopts a GRU neural network as its fundamental framework and demonstrates its superiority through experiments and comparative analyses.
JOURNAL OF ENERGY STORAGE
(2023)