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An Improved Adaptive Kalman Filter based on Auxiliary Model for State of Charge Estimation with Random Missing Outputs

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2023年第2期170卷 020512-020512页

作者： Zhang, Zili Pu, Yan Xu, Fei Zhong, Hongxiu Chen, Jing Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China

In this study, an improved adaptive Kalman filter based on auxiliary model (IAKF-AM) is proposed for estimating the state of charge (SOC) with random missing outputs. Since the traditional auxiliary model (AM) method is inefficient for systems with scarce measurements, this paper provides an IAKF-AM method. Compared with the AM method, the proposed method uses the measurable data to adjust missing outputs in each interval, thus has higher estimation accuracy. In addition, a recursive least squares (RLS) algorithm is introduced, which can combine the IAKF-AM method to iteratively estimate the SOC and outputs. In the simulation part, the mean absolute errors (MAE) and the root mean squared error (RMSE) is used to evaluate the model performance under different cases. Simulation example verify the effectiveness of the proposed IAKF-AM algorithm.

关键词： Battery management system State of charge recursive least squares algorithm Second-order RC model Kalman filter Auxiliary model

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Research on Adaptive Hybrid Energy Consumption Model Based on Data Driven under Variable Working Conditions

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Su, Yujun Zou, Mingyao Jiang, Cheng Qian, Hong State Key Lab Nucl Power Safety Monitoring Techno Shenzhen Peoples R China Shanghai Univ Elect Power Sch Automat Engn Shanghai Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China

As to the nonlinear and time-varying problems of the energy consumption model, this paper proposes an adaptive hybrid modeling method. Firstly, the recursive least squares algorithm with adaptive forgetting factor based on fuzzy algorithm and recursive least squares algorithm is used to identify the simplified mechanism energy consumption model, which solves the data saturation phenomenon and the weights of the "old and new" data during the online identification process and guarantees the adaptability of the mechanism model. Secondly, because there is a deviation between the identified model and the simplified mechanism energy consumption model, the deviation compensation model of mechanism model is established through kernel partial least squares algorithm and the model updating strategy with sliding window, which is used to update the deviation compensation model, and then the adaptive hybrid model is established by combining with the mechanism model identified online and updated deviation compensation model. Finally, the effectiveness, generalization and adaptability of the model are verified by the actual operating data of a single working condition and variable working conditions. And comparing with the mechanism model and the data model, The comparison results show that the adaptive hybrid model has higher calculation accuracy with adaptation.

关键词： fuzzy algorithm recursive least squares algorithm kernel partial least squares algorithm sliding window adaptive forgetting factor adaptive hybrid model

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An Identification and Control algorithm for Nonlinear Systems

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IFAC Proceedings Volumes 1997年第11期30卷 909-913页

作者： Wang Jin Wang Fuli Gao Wenzhong Dept. of Automatic Control Northeastern University Shenyang 110006 Liaoning P.R.China

A new adaptive control algorithm for unknown nonlinear plants is presented. The paper first describes a modified neural network(MNN) as well as the associated learning algorithm. The learning algorithm converges considerably faster because of the introduction of recursive least squares(RLS) algorithm. And then designs adaptive pole placement controller based on the modified neural network. Simulation results show that the proposed control algorithm can effectively control a class of highly nonlinear plants.

关键词： adaptive control nonlinear plants neural networks recursive least squares algorithm

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Suppression of Servo System Mechanical Resonance Based on Adaptive IIR Notch Filter 27

Suppression of Servo System Mechanical Resonance Based on Ad...

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27th Chinese Control and Decision Conference (CCDC)

作者： Li, Na Li, Shi-hua Southeast Univ Sch Automat Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781479970162

In actual servo system, the transmission mechanism is not an ideal rigid body, and mechanical resonance easily occurs. In this paper, the transmission mechanism of servo system is equivalent to a torsion spring, and the entire flexible connection servo system is simplified into a motor-spring-load two-mass system, then the mathematical model of the flexible connection servo system is established through theoretical analysis. Since the mechanical resonant frequency of the system will change with time and environment, the parameters of notch filter must be adjusted online with the resonant frequency. In this paper, according to the application requirements of mechanical resonance online suppressing, the mechanical resonance online suppressing algorithm based on adaptive IIR notch filter is proposed. Using a recursive least squares algorithm, resonant frequency of the system is online estimated, and then the frequency parameters of notch filter are adjusted online. The simulation results show the effectiveness of this method.

关键词： Mechanical Resonance Adaptive IIR Notch Filter recursive least squares algorithm Online suppression

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Online Broadband Impedance Identification for Lithium-Ion Batteries Based on a Nonlinear Equivalent Circuit Model

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WORLD ELECTRIC VEHICLE JOURNAL 2023年第7期14卷 168页

作者： Pan, Hongyu Wang, Xueyuan Zhang, Luning Wang, Rong Dai, Haifeng Wei, Xuezhe Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China Tongji Univ Clean Energy Automot Engn Ctr Shanghai 201804 Peoples R China Jiangsu Donghua Analyt Instrument Co Ltd Taizhou 214500 Peoples R China

Models play a crucial role in explaining internal processes, estimating states, and managing lithium-ion batteries. Electrochemical models can effectively illustrate the battery's mechanism;however, their complexity renders them unsuitable for onboard use in electric vehicles. On the other hand, equivalent circuit models (ECMs) utilize a simple set of circuit elements to simulate voltage-current characteristics. This approach is less complex and easier to implement. However, most ECMs do not currently account for the nonlinear impact of operating conditions on battery impedance, making it difficult to obtain accurate wideband impedance characteristics of the battery when used in online applications. This article delves into the intrinsic mechanism of batteries and discusses the influence of nonstationary conditions on impedance. An ECM designed for non-steady state conditions is presented. Online adaptive adjustment of model parameters is achieved using the forgetting factor recursive least squares (FFRLS) algorithm and varied parameters approach (VPA) algorithm. Experimental results demonstrate the impressive performance of the model and parameter identification method, enabling the accurate acquisition of online impedance.

关键词： lithium-ion batteries impedance equivalent circuit model nonlinear recursive least squares algorithm

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Fractional-order Polynomial Wiener System Identification

Fractional-order Polynomial Wiener System Identification

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Signal, Control and Communication (SCC), International Conference on

作者： Soumaya Marzougui Asma Atitallah Saida Bedoui Kamel Abderrahim Research Laboratory: Numerical Control of Industrial Processes National School of Engineers of Gabes University of Gabes Tunisia

ISBN: (数字)9781728135960

ISBN: (纸本)9781728135977

This paper deals with the problem of identification of fractional-order Wiener systems. This model consists of a linear fractional-order state space system in series with a static nonlinear block. This problem poses different difficulties, because, it consists of estimating the system parameters and the fractional order. In this work, a new identification algorithm is performed: firstly, the parameters of both the linear and the nonlinear subsystems are estimated based on the least squares algorithm and the states are updated based on the auxiliary model principle using the estimated parameters, then, the system order will be estimated using the Levenberg-Marquardt algorithm. Finally, a numerical simulation is offered in the extent to evaluate the effectiveness of the presented method.

关键词： Fractional-order Wiener system Identification Levenberg-Marquardt algorithm Order estimation Parameter estimation recursive least squares algorithm State estimation l-m algorithm State estimation fractional order Parameter estimation system parameter bockwurst System identification

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Research of Discrete Wavelet Transform Domain Adaptive Equalization algorithm

Research of Discrete Wavelet Transform Domain Adaptive Equal...

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2006 8th International Conference on Signal Processing

作者： Xueyu NIU Institute of Communications Engineering PLA Univ.of Sci.& Tech.

This paper studies an algorithm of discrete wavelet transform domain adaptive *** first,the received signals through the channel are transformed in wavelet domain,then the least Mean squares(LMS) algorithm is used to complete the linear equalization in the same *** simulation results show us that the wavelet transform domain adaptive equalization algorithm may offer better performance and higher convergence rate than the standard LMS linear algorithm.

关键词： Inter-symbol interference discrete wavelet transform least Mean squares algorithm recursive least squares algorithm adaptive equalization

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Model and algorithm to Determine Standard value of Detecting Communication Equipment

Model and Algorithm to Determine Standard value of Detecting...

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2011 3rd International Conference on Computer Research and Development(ICCRD 2011)

作者： WU Dongsheng Department of electronic engineering Anhui university of architecture Hefei,China XIE Minxiang,XU Jiren,GAO Huaihui,WANG Keren Department of information Eletric engineering institute of Hefei Hefei,China

This paper discuss model and algorithm of confirming standard value of communication equipment indected,including confirming methods of two or more detection data,at last we attempt to use least squares algorithm and recursive least squares algorithm to confirm detecion standard value,this have definite action for advancing omputation precision and reducing operation load,analogue compution indiacate this model and algorithm have comparative practicability

关键词： detection,standard value least squares algorithm recursive least squares algorithm

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Adaptive estimation of vehicle dynamics through RLS and Kalman filter approaches

Adaptive estimation of vehicle dynamics through RLS and Kalm...

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International IEEE Conference on Intelligent Transportation Systems

作者： Kun Jiang Alessandro Corrêa Victorino Ali Charara Lab. Heudiasyc Univ. de Technol. de Compiegne Compiegne France

ISBN: (纸本)9781467365970

This article presents a new methodology for estimation of vehicle's vertical forces in order to enhance road safety. Direct measurement of vertical forces requires a complex and expensive experimental set-up, which is not acceptable for ordinary passenger cars. The main contribution of this article is providing a reliable estimator of vertical tire forces by using currently available low-cost sensors. The first advantage of the proposed method is that we modified the vehicle model to take into account the roll and pitch dynamics, which makes our estimator stay robust during sharp turning or at inclined road. The other advantage is that we proposed a process to identify the vehicle parameters, instead of regarding them as known constants. This could enable our estimator to stay reliable even when the parameters are wrongly configured. The parameter identification process is based on recursive least squares (RLS) algorithm. The state observers are based on Kalman filter. The estimation process is applied and compared to real experimental data obtained in real conditions. Experimental results validate and prove the feasibility of this approach.

关键词： Kalman filters adaptive estimation least squares approximations observers parameter estimation road safety road vehicles vehicle dynamics Kalman filter RLS algorithm low-cost sensors parameter identification pitch dynamics recursive least squares algorithm roll dynamics state observers vehicle dynamics adaptive estimation vehicle vertical tire force estimation Estimation Load modeling Mathematical model Suspensions Tires Vehicle dynamics Vehicles

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Blind Equalization under Noisy Environment using Bias-compensated RLS method

Blind Equalization under Noisy Environment using Bias-compen...

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第40届中国控制会议

作者： Zhen Zhang Lijuan Jia Shunshoku Kanae Zi-Jiang Yang School of Information and Electronics Beijing Institute of Technology Department of Medical Engineering Faculty of Health SciencesJunshin Gakuen University Department of Intelligent Systems Engineering Ibaraki University

In this paper,a new blind adaptive equalization algorithm under noisy environment is *** consider a practical case where the noise of each transmission channel is *** oversampling the channel output at twice the symbol rate,a single-input double-output channel can be *** apply the recursive-least-squares（RLS） to tackle the blind equalization *** the noise-induced bias,RLS algorithm is *** order to eliminate the bias,we present a bias-compensated RLS（BCRLS） algorithm that can estimate the unknown additive noise online and the noise-induced bias can be therefore *** unbiased estimate of the channel characteristics obtained can be used for channel *** results are presented to demonstrate the performance of the proposed algorithm.

关键词： Blind Equalization recursive least squares algorithm Variance Estimation Bias-compensated Method

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