Article
Energy & Fuels
Fan Yang, Dongliang Shi, Kwok-ho Lam
Summary: With the popularization of electric vehicles, accurate estimation of voltage and state-of-charge (SOC) for rechargeable batteries becomes crucial. Traditional extended Kalman Filtering algorithms suffer from limitations in SOC and voltage estimations. This study proposes a modified extended Kalman filtering (MEKF) algorithm to improve the estimation accuracy of voltage and SOC through real-time parameter adjustment and error reduction.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Wenhua Xu, Shunli Wang, Cong Jiang, Carlos Fernandez, Chunmei Yu, Yongcun Fan, Wen Cao
Summary: The capacity attenuation characteristics of Li-ion batteries were analyzed by aging experiment, and a novel adaptive dual extended Kalman filter algorithm was proposed to collaboratively estimate SOC and SOH, which was verified in experiments for feasibility and accuracy.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Energy & Fuels
Xingtao Liu, Kun Li, Ji Wu, Yao He, Xintian Liu
Summary: A data-driven SOC estimation method based on EKF and XGBoost is proposed in this study, achieving high-precision prediction of battery SOC through machine learning technology, with good robustness in the low SOC range.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Energy & Fuels
Menghan Li, Chaoran Li, Qiang Zhang, Wei Liao, Zhonghao Rao
Summary: In this paper, a hybrid method of deep learning method and Kalman filter was proposed for the estimation of SOC in Li-ion batteries. The deep learning method, including convolutional neural network or temporal convolutional network, was used to capture the spatial and temporal characteristics of input signals, while the Kalman filter was integrated to eliminate the effects of transient signal oscillations. The results showed significant improvements in estimation accuracy and minimal sacrifices in estimation time.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Himadri Sekhar Bhattacharyya, Amalendu Bikash Choudhury, Chandan Kumar Chanda
Summary: This paper focuses on the battery management system (BMS) and the calculation of state of charge (SOC) in lithium-ion batteries. By using the electrical equivalent circuit model (EECM) and algorithms such as extended Kalman filter (EKF) and dual extended Kalman filter (DEKF), a fairly accurate estimate of SOC can be obtained. The impact of voltage and current sensor bias on SOC is also investigated, and the effectiveness of the algorithms is validated under different conditions.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Ishaq Hafez, Ali Wadi, Mamoun F. Abdel-Hafez, Ala A. Hussein
Summary: This research proposes a state-of-charge estimation method to improve the accuracy of battery chemical processes and prevent undesired consequences such as premature end of life or fire hazards.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Thermodynamics
Jiang Zhengxin, Shi Qin, Wei Yujiang, Wei Hanlin, Gao Bingzhao, He Lin
Summary: This paper proposes a method using Extended Kalman Particle Filter to estimate the state of charge of lithium-ion battery by identifying parameters through Immune Genetic Algorithm, showing good adaptability and accuracy in experimental scenarios.
Article
Energy & Fuels
Mostafa Al-Gabalawy, Nesreen S. Hosny, James A. Dawson, Ahmed Omar
Summary: A study developed a SOC estimation algorithm using extended Kalman filter (EKF) and found that the dual EKF algorithm provided the most accurate estimation for battery parameters through comparative analysis.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Automation & Control Systems
Mahroo Sajid, Ala A. Hussein, Ali Wadi, Mamoun F. Abdel-Hafez
Summary: This article presents an enhanced dual extended Kalman filter method for estimating and tracking the state-of-temperature of lithium-ion battery cells. The method utilizes simple but effective dynamic and measurement empirical fit models to estimate the temperature concurrently with the state-of-charge. The dual estimator improves the estimation accuracy by accounting for variations in the state-of-charge.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Thermodynamics
Zheng Liu, Zhenhua Zhao, Yuan Qiu, Benqin Jing, Chunshan Yang, Huifeng Wu
Summary: This paper proposes a novel extended Kalman filter algorithm based on the adaptive maximum correntropy criterion (AMCCEKF) to improve the robustness of state of charge (SOC) estimation in Li-ion battery management systems. The algorithm uses the Gaussian kernel function as the cost function to reconstruct the state error variance and the measurement noise variance. A kernel width adaptive update strategy is designed to address the constraints of fixed kernel width on SOC estimation performance. In addition, an open circuit voltage (OCV) correction strategy based on terminal voltage innovation and OCV-SOC curve gradient is designed to reduce the impact of non-Gaussian noise on SOC estimation.
Article
Energy & Fuels
Peng Nian, Zhang Shuzhi, Zhang Xiongwen
Summary: An improved adaptive extended Kalman filter (IAEKF) is proposed for co-estimation of battery capacity and SOC, with enhanced temperature adaptability through polynomial relationships and forgetting factor. Verification results demonstrate high accuracy and anti-interference capability of the algorithm in FUDS testing.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Energy & Fuels
Mohammad Reza Ramezani-al, Mohammad Moodi
Summary: The estimation of the state of charge (SOC) in battery management systems (BMS) is crucial but suffers from inaccuracy and high computational burden. This paper presents a novel combined SOC estimation approach with high accuracy and simplicity, suitable for low-cost microcontrollers. The method identifies the open circuit voltage (OCV) simultaneously with other parameters and extracts SOC directly from the OCV-SOC curve. A combined method is proposed to overcome the accuracy limitation, utilizing linear and nonlinear parts of the curve with different algorithms. Experimental results demonstrate a 15% reduction in computational burden and high accuracy SOC estimation with a maximum error of <2.5%.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Computer Science, Information Systems
Jaejung Yun, Yeonho Choi, Jaehyung Lee, Seonggon Choi, Changseop Shin
Summary: Accurate SOC estimation is crucial for the efficient operation of battery systems. This study proposes an improved EKF algorithm that applies battery parameters that change according to the SOC, and experimental results confirm its higher accuracy compared to the conventional EKF in SOC estimation.
Article
Computer Science, Artificial Intelligence
Likun Xing, Liuyi Ling, Xianyuan Wu
Summary: This paper proposes a novel model-based method using Dual Extended Kalman Filtering algorithm and Back Propagation Neural Network to estimate and correct the State-Of-Charge (SOC) of lithium-ion batteries. Experimental results show that the SOC estimation error reduces significantly after correcting the estimated SOC.
CONNECTION SCIENCE
(2022)
Article
Thermodynamics
Cong Jiang, Shunli Wang, Bin Wu, Carlos Fernandez, Xin Xiong, James Coffie-Ken
Summary: An adaptive square root extended Kalman filter combined with Thevenin equivalent circuit model was proposed in this study to address filtering divergence caused by computer rounding errors in power lithium-ion battery state-of-charge estimation. The algorithm achieved robust and accurate SOC estimation results using the Sage-Husa adaptive filter and square root decomposition of the covariance matrix. Additionally, a multi-scale dual Kalman filter algorithm and a forgetting factor recursive least-square method were utilized for joint estimation of SOC and capacity, and parameter identification, respectively.
Article
Engineering, Electrical & Electronic
Guozhou Zhang, Junbo Zhao, Weihao Hu, Di Cao, Bendong Tan, Qi Huang, Zhe Chen
Summary: This paper proposes a novel data-driven self-tuning additional sliding mode controller for enhancing transient voltage stability in power systems with wind integration. The controller parameters are tuned using a Markov decision process and deep reinforcement learning algorithm. A data-driven estimation method is also introduced to calculate rewards during the training process. Comparative results demonstrate the effectiveness of the proposed method in suppressing chattering issues and ensuring transient voltage stability under various operating conditions.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Runsheng Zheng, Qunying Liu, Yazhou Jiang, Shuheng Chen, Weihao Hu
Summary: This paper proposes a method based on stochastic transient energy function model to evaluate the stability of power system with DFIG, and analyzes it by constructing stable region and evaluating the stability degree of each bus. The case study validates the reliability and potential applications of this method.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Jiaxiang Hu, Zhou Liu, Jianjun Chen, Weihao Hu, Zhenyuan Zhang, Zhe Chen
Summary: A novel fault diagnosis framework based on deep learning with anti-disturbance ability is proposed in this study to identify the fault state and fault type information, even under the influence of system disturbance.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Chemistry, Physical
Jun Wang, Xiao Xu, Lan Wu, Qi Huang, Zhe Chen, Weihao Hu
Summary: This paper proposes a risk-averse optimal operational strategy for a grid-connected photovoltaic/wind/battery/diesel hybrid energy system (HES) to participate in electricity and hydrogen markets. The proposed strategy maximizes profits through power trading based on price arbitrage. The impact of uncertainties in photovoltaic/wind generation and electricity prices on expected revenue is evaluated using the CVaR model. The results show that considering the hydrogen market can significantly increase the overall expected revenue of the HES.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Energy & Fuels
Bin Zhang, Weihao Hu, Di Cao, Amer M. Y. M. Ghias, Zhe Chen
Summary: This paper proposes a novel model-free multi-agent deep reinforcement learning (MADRL)-based decentralized coordination model to minimize the energy costs of Energy Hub (EH) entities and maximize profits of Electric Vehicle Aggregators (EVAGGs). Simulation results show that the proposed method outperforms traditional methods in terms of computational performance and energy costs.
Article
Engineering, Electrical & Electronic
Bingbing Shao, Qi Xiao, Xiaoxiao Meng, Pingping Han, Wei Ma, Zilong Miao, Frede Blaabjerg, Zhe Chen
Summary: Parallel compensation is commonly used for reactive power compensations to improve the voltage quality of power grids, but direct-drive wind farms connected to the parallel-compensated AC grid may face oscillation risks. To address this issue, a small-signal model of multiple direct-drive wind turbines connected to the parallel-compensated AC grid is developed, and the oscillation characteristics are analyzed using the eigenvalue-based method. The results reveal that the medium-frequency oscillation within the wind farm is primarily influenced by the DC voltage outer loop control, while the sub-synchronous oscillation between the wind farm and the grid is mainly affected by the phase-locked loop control and parallel-compensated AC grid. Furthermore, the oscillation interactions between wind turbine generators in homogeneous and heterogeneous wind farms are investigated, indicating strong interactions in homogeneous wind farms and weak interactions in heterogeneous wind farms. The theoretical analysis is validated through PSCAD/EMTDC simulations.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Thermodynamics
Bin Zhang, Xuewei Wu, Amer M. Y. M. Ghias, Zhe Chen
Summary: In this study, a novel data-driven deep reinforcement learning method is proposed to solve the integrated electricity-gas system (IEGS) dynamic dispatch problem with the targets of minimizing carbon emission and operating cost. The proposed method shows fast and stable learning performance compared to other algorithms, and can reduce the target cost by 11.62% when the prediction error exceeds 10%. Simulation results demonstrate satisfactory generalization and adaptability of the proposed dispatch strategy.
Article
Thermodynamics
Bin Zhang, Weihao Hu, Xiao Xu, Zhenyuan Zhang, Zhe Chen
Summary: In this study, a model-free deep-reinforcement-learning method is integrated into the low-carbon economic autonomous energy management system of an electricity-gas coupled energy system (EGCES) to minimize carbon trading and generation costs. The proposed method, called transformer-deep deterministic policy gradient (TDDPG), combines the feature extraction ability of the transformer network with the decision-making ability of TDDPG. Simulation results show that TDDPG outperforms other examined deep-reinforcement-learning approaches in optimizing low-carbon and economy targets, computation efficiency, and optimization of the results.
Article
Engineering, Electrical & Electronic
Di Cao, Junbo Zhao, Weihao Hu, Qishu Liao, Qi Huang, Zhe Chen
Summary: This paper investigates the distribution system state estimation (DSSE) with unknown topology change. A specific kernel that can transfer across different topologies is used to find relevant patterns and induce knowledge transfer. Bayesian inference is employed to quantify the uncertainties of the DSSE results. Comparative results with other methods demonstrate the improved accuracy and robustness against topology change.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Energy & Fuels
Ozgur Celik, Jalal Sahebkar Farkhani, Abderezak Lashab, Josep M. Guerrero, Juan C. Vasquez, Zhe Chen, Claus Leth Bak
Summary: The increasing penetration of distributed generation to power distribution networks has weaknesses in the protection systems. Machine learning techniques can be used to generate reliable and robust fault detection solutions. This paper proposes a non-pilot protection method based on a GMDH neural network for accurate fault detection.
Article
Automation & Control Systems
Zhe Yang, Wenlong Liao, Qi Zhang, Claus Leth Bak, Zhe Chen
Summary: In this article, a new fault coordination control method is proposed to address the misoperation of traditional distance relays caused by the distinctive fault characteristics of converter-interfaced renewable energy sources (CIRESs). The proposed method reveals the relationship between the apparent reactance and the CIRES positive-sequence current angle by considering sequence boundary conditions. By adjusting current references of the controller, a reasonable current angle is generated to ensure correct fault detection by distance relays. Simulation analysis in PSCAD and real-time digital simulator validates the effectiveness of the recommended scheme.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
Yuanhong Tang, Weihao Hu, Di Cao, Nie Hou, Zhuoqiang Li, Yun Wei Li, Zhe Chen, Frede Blaabjerg
Summary: This article proposes an improvement approach for the dual-active-bridge converter using a variable-frequency triple-phase-shift control strategy with the help of deep reinforcement learning. The twin delayed deep deterministic policy gradient algorithm is adopted to train the agent offline, aiming to minimize power losses under varying switching frequencies. The training of the algorithm incorporates zero-voltage switching performance. Based on this, the trained agent acts as a fast surrogate predictor, generating appropriate control strategies in real-time for continuous operation with soft switching and maximum conversion efficiency. The effectiveness and correctness of the proposed scheme are validated through experimental results in a laboratory prototype.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Energy & Fuels
Qinghan Wang, Yanbo Wang, Zhe Chen
Summary: This paper develops an optimal energy bidding mechanism for the regional integrated electricity-hydrogen system (RIEHS) based on Stackelberg game. It introduces the transaction mode and establishes optimization models for the three market game participants. The bidding mechanism is formulated using Stackelberg game, where the electricity-hydrogen operator is the leader and the regional electricity-hydrogen prosumer and load aggregator are the followers. The proposed strategy is validated through a demonstration case and shown to offer additional economic benefits and improved hydrogen utilization.
FRONTIERS IN ENERGY RESEARCH
(2023)
Article
Engineering, Electrical & Electronic
Jianjun Chen, Weihao Hu, Guozhou Zhang, Jiaxiang Hu, Qi Huang, Zhe Chen, Frede Blaabjerg
Summary: Accurate and efficient bearing fault diagnosis method plays a crucial role in modern industrial system. However, the performance of fault diagnosis model varies with the sampling frequency of training data. In this article, a novel knowledge-sharing multi-task (KSMT) model is proposed to address this issue. The experimental results demonstrate that the proposed KSMT model outperforms other state-of-the-art methods and effectively overcomes the limitation of less information on fault diagnosis performance.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Yubo Han, Weihao Hu, Yiping Yuan, Zhenyuan Zhang, Frede Blaabjerg
Summary: A multiratio-multiresonance switched capacitor converter is proposed in this article to improve the voltage regulation capacity. By combining different submodules with different operational modes, various output voltages can be achieved. Experimental results show that the converter has high efficiency and small size.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Energy & Fuels
Mokhtar Benasla, Imane Boukhatem, Tayeb Allaoui, Abderrahmane Berkani, Petr Korba, Felix Rafael Segundo Sevilla, Mohamed Belfedel
Summary: The idea of exporting dispatchable solar electricity from the North African region to Europe is still being discussed. This paper focuses on the potential of Algeria as a supplier and analyzes possible import corridors and barriers.
Article
Energy & Fuels
Hobyung Chae, Sangmu Bae, Jae-Weon Jeong, Yujin Nam
Summary: Thermoelectric generators (TEGs) utilize temperature differences to produce electricity and have potential for various industrial applications. This study introduces an advanced technique that utilizes temperature gradients in water pipes to increase power generation, with efficient modulation of output power through flow control. The feasibility evaluation in residential settings shows that TEGs can generate 10.95 kWh of electricity per unit, and to achieve zero-energy buildings, 64.5 m2 of TEG deployment is required per unit given a zT value of 1.
Review
Energy & Fuels
Lina Patricia Vega, Karen Tatiana Bautista, Heliana Campos, Sebastian Daza, Guillermo Vargas
Summary: This article focuses on the current situation of biofuel production and research development in Latin American countries such as Argentina, Brazil, Mexico, Chile, Costa Rica, and Colombia. Brazil stands out as a leader in the region, making significant advancements in clean energy production through biofuels policy implementation. The review highlights the challenges these countries face in utilizing their comparative advantages for biofuel production.
Article
Energy & Fuels
Iraj Faraji Davoudkhani, Mahmoud Rerza Shakarami, Almoataz Y. Abdelaziz, Adel El-Shahat
Summary: This paper presents an optimization-based method for designing a wide-area damping controller (WADC) based on remote signals to improve the damping of inter-area oscillations by considering the time delays. The grey wolf optimization (GWO) algorithm is utilized to solve the optimization problem, and simulations and statistical results demonstrate the superiority of the proposed method.
Article
Energy & Fuels
Majid Ahmed Mohammed, Bashar Mahmood Ali, Khalil Farhan Yassin, Obed Majeed Ali, Omar Rafae Alomar
Summary: This study compares the effects of different phase change materials on the performance of solar panels. The experiment shows that the use of beeswax can lower the temperature of the panel and increase the efficiency of the photovoltaic system.
