Article
Energy & Fuels
Omid Rezaei, Ali Rahdan, Sohrab Sardari, Masoud Dahmardeh, Zhanle Wang
Summary: This paper proposes a novel fuzzy robust two-stage unscented Kalman filter (FRTSUKF) method for the practical state of charge (SoC) estimation of lithium-ion batteries. The proposed estimator is able to estimate the model uncertainties without requiring the statistical characteristics of the uncertainties. Using the estimated uncertainties, the SoC estimation is corrected, eliminating the destructive effect of model inaccuracy on the estimation accuracy.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Muyao Wu, Linlin Qin, Gang Wu, Yusha Huang, Chun Shi
Summary: This paper proposes a linear model with the Variable Forgetting Factor Adaptive Kalman Filter for accurate estimation of the State of Charge (SoC) in battery management systems. The proposed model outperforms the traditional Rint and Thevenin models in SoC estimation, even when the exact terminal current is unknown. The effectiveness of the linear model is demonstrated through numerical experiments, showing a significant improvement in estimation accuracy compared to traditional models.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Energy & Fuels
Jieyu Xu, Dongqing Wang
Summary: This study addresses issues with single-rate sampled state space models facing resistor-capacity couples with different time scales by using a dual-rate sampled method. It incorporates recursive noise estimation and exponential weighted multiple innovation, demonstrating superior precision compared to classical single-rate sampled methods.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Automation & Control Systems
Wenquan Ou, Chun Wang, Aihua Tang, Bo Huang, Kang Liu
Summary: This paper presents a multistate joint estimation method of ultracapacitor based on trans-scale dual extended Kalman filter to predict SOC and model parameters in real-time. The method combines an ultracapacitor model with two Kalman filters, one for SOC estimation and another for trans-scale updating of model parameters. Validation results show that the proposed algorithm has higher estimation accuracy and more stable convergence ability compared to other two traditional SOC estimation methods.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Energy & Fuels
Jiabo Li, Min Ye, Xiaokang Ma, Qiao Wang, Yan Wang
Summary: This paper proposes a new method for state of charge (SOC) estimation and fault diagnosis based on multiple equivalent circuit models (ECMs) fusion approach to ensure the safe driving of electric vehicles (EVs). The accuracy of SOC estimation and fault diagnosis is improved from four aspects. The SOC is estimated using the Thevenin model and second-order ECM, and the model parameters are determined with the least square method. The unscented Kalman filter (UKF) is employed for SOC estimation, and the Bayesian theorem is used to determine the optimal weights for synthesizing the estimated SOCs from the two models. An adaptive fault diagnosis framework based on multiple ECMs fusion is constructed to determine the current battery operation status, achieving accurate early warning of the current sensor fault.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Yuanmao Ye, Zhenpeng Li, Jingxiong Lin, Xiaolin Wang
Summary: This paper proposes a new model-based SOC estimation method for lithium-ion batteries, which integrates parameter identification and state estimation into one closed-loop algorithm. The algorithm utilizes extended stochastic gradient algorithm and adaptive extended Kalman filter for parameter identification and state estimation respectively. Experimental results demonstrate the good performance of the proposed method in terms of estimation accuracy and robustness under different test conditions, making it more suitable for online SOC estimation of lithium-ion batteries.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Ali Wadi, Mamoun Abdel-Hafez, Ala A. Hussein
Summary: This paper proposes a state-of-charge estimation technique for electric vehicles that integrates adaptive noise identification with the dual-Kalman filter to achieve robust and computationally-efficient estimation. The proposed technique is validated through standardized electric vehicle tests, demonstrating its effectiveness and robustness.
Article
Energy & Fuels
Jiaming Dou, Hongyan Ma, Yingda Zhang, Shuai Wang, Yongxue Ye, Shengyan Li, Lujin Hu
Summary: This research proposes an improved extreme learning machine (ELM) approach to accurately estimate the capacity of lithium-ion batteries, and optimizes the robustness and computational cost of the model by using a salp swarm algorithm (SSA) and a sine cosine algorithm (SCA). The experimental results demonstrate that the proposed model outperforms other popular models and exhibits good traceability and generality.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Automation & Control Systems
Hao Lei, Boyi Chen, Yanbin Liu, Yuping Lv
Summary: This study introduces a modified Kalman particle swarm optimization (MKPSO) algorithm, which enhances the prediction accuracy of the global optimum by incorporating the Kalman filter principle, effectively addressing the advantages of mining capabilities for high-dimensional problems and solving hybrid optimization problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Automation & Control Systems
Zhaoxia Peng, Chenyang Pan, Shichun Yang, Guoguang Wen, Tingwen Huang
Summary: This article proposes a dual Kalman filter-type resilient filter to estimate SOC and parameter jointly with the random missing measurement phenomenon. The filter gains are optimized based on the minimum-variance principle to minimize the effects of missing measurement and gain variations on the estimation performance. Extensive simulations and experiments are conducted to validate the effectiveness and resilience of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Energy & Fuels
Xiaobo Zhao, Kangsan Kim, Seunghun Jung
Summary: This paper proposed a new method to improve the accuracy of SOC estimation for VRFBs through data fusion, and experimental results demonstrated the accuracy and reliability of this method.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Telecommunications
G. Ananthi
Summary: The battery management system in electric vehicles monitors the state of charge of the Lithium Ion battery by controlling parameters such as voltage, current and temperature, in order to prevent overcharging and overdischarging. Accurate estimation of the battery state of charge is crucial for the safety of automotive batteries.
WIRELESS PERSONAL COMMUNICATIONS
(2023)
Article
Automation & Control Systems
Henrik Beelen, Henk Jan Bergveld, M. C. F. Donkers
Summary: This article discusses a method that combines a nonlinear observer with a structured representation of model uncertainty to address the issue of tedious tuning in joint EKF. Experimental results show that the proposed method performs similarly to the regular EKF, demonstrating intuitive and effective characteristics in estimating SoC.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2021)
Article
Energy & Fuels
Ce Huang, Xiaoyang Yu, Yongchao Wang, Yongqin Zhou, Ran Li
Summary: The paper introduces a noise-adaptive interacting multiple model algorithm combined with an unscented Kalman filter to address filtering issues caused by noise statistical properties. This algorithm allows for accurate estimation of SOC even when model parameters change dynamically and noise statistical properties are unknown.
Article
Energy & Fuels
Dongqing Wang, Yan Yang, Tianyu Gu
Summary: This paper introduces a hierarchical adaptive extended Kalman filter (HAEKF) algorithm for state of charge (SOC) estimation in battery management system (BMS). The algorithm utilizes an adaptive EKF algorithm with online updating of the Sage-Husa estimator for improved SOC estimation. The hierarchical identification principle is used to decompose the circuit state equation model into two fictitious submodels with different sampling rates, allowing for fast and slow dynamic estimation using the HAEKF algorithm. Experimental results under the urban dynamometer driving schedule (UDDS) test condition confirm the high accuracy, low computational cost, and strong robustness of the HAEKF algorithm under left biased measurement noise variance.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Zhongbao Wei, Jian Hu, Yang Li, Hongwen He, Weihan Li, Dirk Uwe Sauer
Summary: This paper proposes a hierarchical soft measurement framework for accurate estimation of SOC and load current in electric vehicles, even without using current measurements. Simulation and experimental results show that the framework can achieve high-fidelity co-estimation even in scenarios of noise corruption and current sensor malfunction.
Article
Automation & Control Systems
Zhongbao Wei, Jian Hu, Hongwen He, Yifei Yu, James Marco
Summary: This article proposes a thermal-model-based method for multistate joint observation in lithium-ion battery management. By embedding distributed temperature sensors, a novel smart battery design enables real-time monitoring of internal and surface temperatures, as well as state parameters with high space resolution.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Jiangtao He, Shujuan Meng, Xiaoyu Li, Fengjun Yan
Summary: This article proposes a new method for state-of-health (SOH) estimation using partial constant current (CC) charging data. A linear model is calibrated using full-range CC charging data, and the corresponding features of interest (FOI) are extracted from the partial CC data. The SOH can be directly interpolated based on the calibrated FOI-SOH linear model. Experimental results show that the estimated SOH accuracy is within 5%, demonstrating the reliability of the proposed SOH estimator for practical applications.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Review
Engineering, Electrical & Electronic
Cuili Chen, Zhongbao Wei, Alois Christian Knoll
Summary: This article provides a comprehensive review of existing charging optimization techniques for lithium-ion batteries, addressing the challenges of long charging time and degradation caused by fast charging. It discusses the operation and models of lithium-ion batteries and scrutinizes the side effects of unregulated fast charging on battery aging mechanism. The state-of-the-art open- and close-loop charging optimization techniques are systematically reviewed, highlighting their respective merits and shortcomings. The future development of a charging control protocol with real-time affordability and degradation consciousness is also discussed.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Thermodynamics
Jiaqiang Tian, Xinghua Liu, Siqi Li, Zhongbao Wei, Xu Zhang, Gaoxi Xiao, Peng Wang
Summary: This study proposes a state-of-health (SOH) attenuation model considering driving mileage and seasonal temperature for battery health estimation, which is significant for battery pack management and maintenance. The variable forgetting factor recursive least square (VFFRLS) algorithm is used for battery model parameter identification and the extended Kalman-particle filter (EPF) algorithm is proposed for online capacity estimation. The proposed model and algorithm are verified using actual vehicle data over nine months. The experimental results demonstrate the accurate estimation of model parameters and capacity through the proposed algorithm, and the decrease in average capacity of the battery module with total mileage. The compensation of monthly driving mileage and ambient temperature factors effectively improves the accuracy of the SOH model.
Article
Automation & Control Systems
Haoyong Cui, Zhongbao Wei, Hongwen He, Jianwei Li
Summary: This article proposes a dual-scale hierarchical equalization scheme enabled by a novel four-switch reconfigurable topology, aiming to address the issue of cell imbalance in lithium-ion batteries. The proposed scheme offers flexible reconfigurability, moderate complexity, and high fault tolerance, ensuring all-cell flexibility and maximum capacity utilization.
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
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
Automation & Control Systems
Jianwei Li, Shucheng He, Qingqing Yang, Tianyi Ma, Zhongbao Wei
Summary: This article proposes a multi-objective sizing method that integrates retired batteries and photovoltaic solar energy in electric vehicle charging stations to address uncertainty in charging demand. The method utilizes the non-dominated sorting genetic algorithm II (NSGA-II) to minimize renewable energy waste, energy purchased from the grid, and the cost over a 20-year period. The article also predicts the remaining life of retired batteries using a calendar-life degradation model and incorporates different charging patterns to model charging demand uncertainty in different EVCS scenarios. Case studies using real-world data demonstrate a 29.4% cost reduction in long-term operation with the proposed sizing method and retired batteries.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Qingqing Yang, Jun Shen, Jianwei Li, Hongwen He, Zhongbao Wei, Petar Igic
Summary: This article proposes an improved adaptive predictive control method for multiterminal HVdc systems, which addresses the challenges of system stability caused by the decreasing system inertia due to the increasing penetration of renewables and integration of power electronic devices. The proposed method coordinates the key parameters, including dc voltage, ac frequency, and power-sharing among terminals, by optimizing a multiobjective fitness function. The method achieves adaptive control by combining trust-region and particle swarm optimization. The proposed method is validated on a four-terminal HVdc system within the IEEE 30-bus ac system, demonstrating its robustness and efficiency.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Wanke Cao, Mengchao Yang, Zhongbao Wei, Jun Wang, Xiaoguang Yang
Summary: A new method for analyzing multi-hop loop delay and a hierarchical cyber-physical control scheme for the autonomous emergency braking (AEB) system are proposed to mitigate the adverse effects of road adhesion saturation and communication delays. The upper layer adopts a mu-adaptive TTC planning strategy to consider road adhesion saturation and generate desired acceleration for collision risk avoidance. The lower layer designs an H-infinity-based LQR for acceleration tracking with strong robustness to cyber system uncertainties.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Rui Wang, Junda Li, Qiuye Sun, Huaguang Zhang, Zhongbao Wei, Peng Wang
Summary: This paper proposes a DC-DC converter using virtual power based model predictive control (VP-MPC), which can provide energy mutual aid function between two electric vehicles. The bidirectional full bridge series resonant DC-DC converter (BDB-SRC) is applied to meet the demand for high voltage gain and high power density. VP-MPC is proposed to solve the problem of variable parameters in different types of electric vehicles. Experimental results verify the high performance of the proposed energy transfer converter between two electric vehicles.
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
(2023)
Article
Automation & Control Systems
Xuyang Zhao, Hongwen He, Jianwei Li, Zhongbao Wei, Ruchen Huang, Man Shi
Summary: This article proposes a computer vision-based method to accurately estimate battery capacity degradation. The method constructs battery multidimensional aging features as key images and establishes a mapping relationship between the images and capacity degradation using specific charging data segments. The proposed method greatly simplifies the network structure in computer vision models and improves estimation accuracy and efficiency.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Haokai Ruan, Hongwen He, Zhongbao Wei, Zhongyi Quan, Yunwei Li
Summary: This article proposes a novel state of health (SOH) estimator using partial constant-voltage (CV) charging data. Thorough analysis is performed to determine the most informative and robust health indicators (HIs), with CV capacity found to be the most suitable for SOH estimation. To address the challenge of partial CV charging, a novel CV phase reconstruction method combining Q - V modeling and open-circuit voltage (OCV) estimation is proposed to accurately predict the CV capacity based on available partial CV data. The proposed method is validated with long-term degradation experiments, demonstrating high accuracy, low charging completeness requirement, and robustness to cell inconsistency.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Xiaolei Bian, Zhongbao Gae Wei, Weihan Li, Josep Pou, Dirk Uwe Sauer, Longcheng Liu
Summary: A novel fusion-based method combining OCV model and incremental capacity analysis is proposed for state of health (SOH) estimation of lithium-ion batteries. The extracted features from OCV and IC curves are fused using an artificial neural network, resulting in high estimation accuracy and robustness according to experimental validation.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2022)
