Article
Automation & Control Systems
Xinghao Du, Jinhao Meng, Yingmin Zhang, Xinrong Huang, Shunliang Wang, Ping Liu, Tianqi Liu
Summary: This article proposes a reliable online parameter identification method for battery ECM, which utilizes a well-designed information appraisal procedure based on the Fisher-information-based Cramer-Rao lower bound (CRLB). The method achieves online parameter updating through a comprehensive appraisal indicator derived recursively from CRLB. The simulation and experimental results confirm the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Review
Chemistry, Analytical
Si Won Park, Sang Jun Kim, Seong Hyun Park, Juyeon Lee, Hyungjun Kim, Min Ku Kim
Summary: Electroactive polymer (EAP) reacts to electrical stimuli. Ionic EAP, such as IPMC, is composed of a metal electrode and a polymer membrane, with advantages such as low voltage requirements, large bending displacement, and bidirectional actuation. Manufacturing of IPMC involves preparing the polymer membrane and plating electrode. IPMC is widely used in various applications.
Article
Energy & Fuels
Xiaobo Zhao, Xiao Qian, Dongji Xuan, Seunghun Jung
Summary: This study proposed a multi-input extreme learning machine (MI-ELM) method based on online model parameter identification technique for SOC estimation of lithium-ion batteries (LiBs). Experimental results under various operating conditions demonstrated that the method exhibited superior accuracy and outperformed other estimation methods.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Computer Science, Interdisciplinary Applications
Qinghe Shi, Hao Wang, Lei Wang, Zhenxian Luo, Xiaojun Wang, Wenqin Han
Summary: This research proposes a bilayer optimization strategy for optimal sensor placement to decrease uncertainty in parameter identification. A surrogate model and a coordinate-based particle swarm optimization algorithm are established to improve solution efficiency. The proposed strategy shows significant advantages in terms of optimization efficiency, computational efficiency, and optimization results.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Automation & Control Systems
Kesen Fan, Yiming Wan, Benben Jiang
Summary: In this paper, a non-parametric sparse Gaussian process regression (GPR) method is proposed to estimate the parameters of an equivalent circuit model (ECM) for batteries. By transforming the non-linear state-of-charge (SOC) dependent ECM into a linear parameter varying (LPV) model, the proposed method provides parameter estimates and uncertainties, while reducing computational cost through sparse regression. Experimental results demonstrate the effectiveness of the proposed approach.
JOURNAL OF PROCESS CONTROL
(2022)
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
Electrochemistry
Xueyi Hao, Shunli Wang, Yongcun Fan, Yawen Liang, Yangtao Wang, Carlos Fernandez
Summary: Accurate prediction of the remaining range for electric vehicles is still a challenge. The state of energy (SOE), which represents the remaining mileage and charge of a lithium-ion battery, is crucial for predicting the remaining range. To achieve high accuracy in describing and identifying SOE parameters, a parameter identification method using an improved particle swarm optimization algorithm with a compression factor is proposed. A particle filter (PF) is employed to estimate the energy state, using the unscented particle filtering (UPF) algorithm with particle swarm optimization (PSO) to overcome the limitations of particle degradation and insufficient diversity in particle filtering. Experimental results demonstrate that the proposed algorithm achieves an estimation error within 0.97% at 25 degrees and within 1.29% at 5 degrees for all three operating conditions. This indicates that the algorithm exhibits high accuracy and robustness across different temperatures and working conditions, and validates the effectiveness of energy state estimation.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2023)
Article
Thermodynamics
Kesen Fan, Yiming Wan, Zhuo Wang, Kai Jiang
Summary: In this paper, a time-efficient method is proposed to identify the temperature-dependent OCV-SOC curve from current-voltage data by fusing existing OCV-SOC curve data at different temperatures. Experimental results show that our approach significantly reduces the RMSE of OCV predictions by at least 29.4% compared to existing methods, and also improves the accuracy of SOC estimates by at least 14.0% using the updated OCV-SOC curve.
Article
Energy & Fuels
Peng Lin, Peng Jin, Aixiao Zou, Zhenpo Wang
Summary: The study proposes a real-time PNGV model parameter identification method, which can provide a solid foundation for various battery state estimation. Real-time identification of PNGV model parameters via MRAS enables high-precision terminal voltage estimation for hybrid pulse power characterization. The real-time SOC estimation based on identified OCV also shows promising results.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Engineering, Electrical & Electronic
Gautam Sethia, Sisir Kumar Nayak, Somanath Majhi
Summary: This paper presents a new adaptive observer based approach for precise estimation of battery SOC using super twisting algorithm, which avoids overestimation of gains, ensures finite-time convergence of states, provides continuous control injection to reduce chattering, and eliminates the need for a low pass filter. The proposed method does not require information on upper bounds of uncertainty and achieves improved accuracy, robustness, computational complexity, and convergence time compared to existing approaches.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2021)
Article
Computer Science, Information Systems
Shehla Amir, Moneeba Gulzar, Muhammad O. Tarar, Ijaz H. Naqvi, Nauman A. Zaffar, Michael G. Pecht
Summary: This paper proposes a method to accurately estimate the state of health (SOH) of lithium-ion batteries using a 2-RC model, considering factors such as time and temperature. Compared to the traditional 1-RC model, this method has significant advantages in capturing battery degradation and reducing computational costs.
Article
Automation & Control Systems
Ruohan Guo, Weixiang Shen
Summary: This article proposes an IA-MAFF-RLS method to identify model parameters of lithium-ion batteries in electric vehicles. The method utilizes information analysis and adaptive strategies to accurately identify the parameters and avoid accuracy loss in model transformation.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Qiwei Wang, Gaolin Wang, Nannan Zhao, Guoqiang Zhang, Qingwen Cui, Dianguo Xu
Summary: This article proposes a parameter identification method for permanent magnet synchronous motors based on the high frequency equivalent impedance model. By injecting HF signals at both the dq-axes, the method is able to identify motor parameters offline and online, showing good identification results in various operating conditions through experimental validation.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2021)
Article
Energy & Fuels
Lili Xia, Shunli Wang, Chunmei Yu, Yongcun Fan, Bowen Li, YanXin Xie
Summary: Accurate prediction of remaining mileage for electric vehicles is still a challenge. This paper proposes a novel fusion equivalent-circuit model of lithium-ion batteries considering the influence of temperature to obtain a high precision mathematical description and state parameters. An adaptive noise correction-dual extended Kalman filtering algorithm is adopted for the estimation of state-of-energy and state-of-charge, solving the noise influence of Kalman filtering. Experimental results show that the proposed method achieves estimation errors within 1.83% and 1.92% at different working temperatures and conditions, proving the efficiency of the co-estimation method.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Computer Science, Information Systems
Sergio Marin-Coca, Amir Ostadrahimi, Stefano Bifaretti, Elena Roibas-Millan, Santiago Pindado
Summary: This paper presents a simple and direct method for estimating the electrical parameters of a supercapacitor (SC). The equivalent electrical circuit model considers the voltage and frequency dependence of the SC's capacitance, neglecting self-discharge phenomenon, making it suitable for short and mid-term simulations in industrial applications. The estimation procedure starts by analyzing experimental data from a constant-current discharge test and building a fitting function. Initial estimated values of the electrical parameters are obtained through simple relations and optimized using the least squares method. The procedure is validated by extracting the optimal parameters of the two-branches model and comparing the results with experimental data, showing good accuracy in a wide range of constant-current charging/discharging cycles.
