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Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

Chinese Journal of Mechanical Engineering 2023年第1期36卷 305-314页

作者： Xinghao Du Jinhao Meng Kailong Liu Yingmin Zhang Shunli Wang Jichang Peng Tianqi Liu College of Electrical Engineering Sichuan UniversityChengdu610044China School of Electrical Engineering Xi’an Jiaotong University710049Xi’anChina Warwick Manufacturing Group University of WarwickCoventryUK Southwest University of Science and Technology Mianyang621010China Nanjing Institute of Technology Nanjing211103China

Online parameter identification is essential for the accuracy of the battery equivalent circuit model(ECM).The traditional recursive least squares(RLS)method is easily biased with the noise disturbances from sensors,which degrades the modeling accuracy in practice.Meanwhile,the recursive total least squares(RTLS)method can deal with the noise interferences,but the parameter slowly converges to the reference with initial value uncertainty.To alleviate the above issues,this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM.RLS converges quickly by updating the parameters along the gradient of the cost function.RTLS is applied to attenuate the noise effect once the parameters have converged.Both simulation and experimental results prove that the proposed method has good accuracy,a fast convergence rate,and also robustness against noise corruption.

关键词： Li-ion battery Equivalent circuit model recursive least squares recursive total least squares

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An Online Adaptive Internal Short Circuit Detection Method of Lithium-Ion Battery

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Automotive Innovation 2021年第1期4卷 93-102页

作者： Jian Hu Zhongbao Wei Hongwen He National Engineering Laboratory of Electric Vehicles School of Mechanical EngineeringBeijing Institute of Technology5 ZhongGuanCun South StreetHaidian DistrictBeijing 100181China

Internal short circuit(ISC)is a critical cause for the dangerous thermal runaway of lithium-ion battery(LIB);thus,the accurate early-stage detection of the ISC failure is critical to improving the safety of electric vehicles.In this paper,a model-based and self-diagnostic method for online ISC detection of LIB is proposed using the measured load current and terminal voltage.An equivalent circuit model is built to describe the characteristics of ISC cell.A discrete-time regression model is formulated for the faulty cell model through the system transfer function,based on which the electrical model parameters are adapted online to keep the model accurate.Furthermore,an online ISC detection method is exploited by incorporating an extended Kalman filter-based state of charge estimator,an abnormal charge depletion-based ISC current estimator,and an ISC resistance estimator based on the recursive least squares method with variant forgetting factor.The proposed method shows a self-diagnostic merit relying on the single-cell measurements,which makes it free from the extra uncertainty caused by other cells in the system.Experimental results suggest that the online parameterized model can accurately predict the voltage dynamics of LIB.The proposed diagnostic method can accurately identify the ISC resistance online,thereby contributing to the early-stage detection of ISC fault in the LIB.

关键词： Lithium-ion battery Internal short circuit recursive least squares Extended Kalman filter

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A Residual Carrier Tolerant QAM Receiver Architecture Based on Time-Varying Blind Source Separation

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Chinese Journal of Electronics 2016年第6期25卷 1040-1044页

作者： WANG Dan LIU Hailin YANG Junjie Guangdong University of Technology

In case of Quadrature amplitude modulated(QAM) transmission, the residual carrier affects the quality of the received signal. We show that the carrier tracking problem is equivalent to a time-varying Blind source separation(BSS) problem if there exists a residual carrier. This motivates us to propose a new receiver architecture, that is tolerant to residual carrier, based on a recursive least squares(RLS) BSS algorithm. Simulations are provided to show the promising performance of the proposed algorithm for tracking the residual carrier and for improving the quality of the received signal.

关键词： Quadrature amplitude modulated receiver Residual carrier Blind source separation recursive least squares

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Prediction of Time Series Empowered with a Novel SREKRLS Algorithm

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Computers, Materials & Continua 2021年第5期67卷 1413-1427页

作者： Bilal Shoaib Yasir Javed Muhammad Adnan Khan Fahad Ahmad Rizwan Majeed Muhammad Saqib Nawaz Muhammad Adeel Ashraf Abid Iqbal Muhammad Idrees School of Computer Science Minhaj University LahoreLahore54000Pakistan Prince Sultan University Riyadh11586Saudi Arabia Department of Computer Science Riphah International University Lahore CampusLahore54000Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia Directorate of IT The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Science University of Management and TechnologyLahore54770Pakistan PUCIT University of the PunjabLahore54000Pakistan

For the unforced dynamical non-linear state–space model,a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article.The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems.With the help of an ortho-normal triangularization method,which relies on numerically stable givens rotation,matrix inversion causes a computational burden,is reduced.Matrix computation possesses many excellent numerical properties such as singularity,symmetry,skew symmetry,and triangularity is achieved by using this algorithm.The proposed method is validated for the prediction of stationary and non-stationary Mackey–Glass Time Series,along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness.By the learning curves regarding mean square error(MSE)are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS.This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays,which is efficient in developing very-large-scale integration(VLSI)applications with non-linear input data.Multiple experiments are carried out to validate the reliability,effectiveness,and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares(EKRLS)algorithm.

关键词： Kernel methods square root adaptive filtering givens rotation mackey glass time series prediction recursive least squares kernel recursive least squares extended kernel recursive least squares square root extended kernel recursive least squares algorithm

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Research of internet worm warning system based on system identification

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控制理论与应用（英文版） 2006年第4期4卷 409-412页

作者： Tao ZHOU Guanzhong DAI Huimin YE College of Automatic Control Northwestern Polytechnical University Xi＇an Shaanxi 710072 China

The frequent explosion of Internet worms has been one of the most serious problems in cyberspace security. In this paper, by analyzing the worm＇s propagation model, we propose a new worm warning system based on the method of system identification, and use recursive least squares algorithm to estimate the worm＇s infection rate. The simulation result shows the method we adopted is an efficient way to conduct Internet worm warning.

关键词： Cyberspace security Internet worm System identification recursive least squares

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Parameter identification to an approximated function of the Weierstrass approximation formula

Parameter identification to an approximated function of the ...

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第24届中国控制与决策会议

作者： Jia Tang, Jing Chen Wuxi Professional College of Science and Technology Xiaoying Zhou School of IoT Engineering, Jiangnan University

In Weierstrass approximation theorem, a continuous function can be approximated by an algebraic polynomial. In this paper, we use the stochastic gradient identification algorithm and the recursive least squares algorithm to estimate the parameters of the algebraic polynomial. In order to improve the convergence rate and the computational effort, two modified stochastic gradient algorithms are given. The proposed approaches are illustrated by a simulation example.

关键词： Parameter estimation Stochastic gradient recursive least squares Weierstrass approximation theorem Identification model.

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Podded Propulsion Unmanned Surface Vehicle Model Identification Based on Field Experiments

Podded Propulsion Unmanned Surface Vehicle Model Identificat...

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第36届中国控制会议

作者： Dongdong Mu Guofeng Wang Yunsheng Fan Yiming Bai School of Information Science and Technology Dalian Maritime University

ISBN: (纸本)9781538629185

Model is the basis of the control,and in order to achieve the autonomous navigation of podded propulsion unmanned surface vehicle,its model is determined to be the beginning of all the work.The research object of this paper is Dalian Maritime University "Lanxin" podded propulsion unmanned surface vehicle.In practical engineering applications,the response model is often used to design ship controller.So first of all,for "Lanxin" unmanned surface vehicle,the MMG model of three DOF is simplified as a response model by force analysis and conditional hypothesis.Subsequently,the field experiments（turning experiment and zig-zag experiment） are executed to collect the experimental data.Then turning experiment data and zig-zag experiment data are used to identify the response model parameters by recursive least square method,respectively.Finally the correctness of the identification results are verified by simulation.Simulation results show that the identification result of zig-zag experiment data is more close to the reality than the identification result of turning experiment data,which it proves the correctness of modeling and the feasibility of identification scheme.

关键词： Unmanned surface vehicle POD response model field experiments recursive least squares

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