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Online power and efficiency estimation of a fuel cell system for adaptive energy management designs

ENERGY CONVERSION AND MANAGEMENT 2022年 255卷 115324-115324页

作者： Kandidayeni, Mohsen Soleymani, Mehdi P. Macias, Alvaro Boulon, Loic Travao, Joao P. Arak Univ Dept Mech Engn Fac Engn Arak *** Iran Univ Sherbrooke Dept Elect & Comp Engn eTESC Lab Sherbrooke PQ J1K 2R1 Canada Univ Quebec Trois Rivieres Hydrogen Res Inst Dept Elect & Comp Engn Quebec City PQ G8Z 4M3 Canada Polytech Coimbra Coimbra Inst Engn P-3030199 Coimbra Portugal

The temporal changes of power and efficiency in a fuel cell (FC) stack can cause malperformance in the energy management strategy (EMS) of a FC hybrid electric vehicle. Therefore, the online estimation of these physical attributes is becoming an integral part of any EMS. This paper aims to utilize a two-step method to extract the maximum power and efficiency points of a FC system online. In this respect, an online parameter estimation technique, composed of smooth variable structure filter (SVSF) and Kalman filter (KF), is utilized in the first step to estimate the parameters of a FC semi-empirical voltage model. KF generates statistically optimal estimates for a linear, well-designed system model in the existence of Gaussian noise. However, these assumptions do not always hold in real applications and can lead to unstable estimation. A practical solution to deal with these instabilities is to enforce boundaries on the state estimates through SVSF which is based on sliding mode estimation concept. Hence, unlike the other similar studies, this paper synthesizes the robustness of SVSF with the precision of KF to enhance the characteristics estimation process of a FC stack. In the second step, the updated voltage model is utilized to extract the efficiency and power curves of the real FC system. To corroborate the potential of the proposed approach, a thorough comparison with KF, as an attested estimation method, is performed. The experimental tests on a 500-W FC stack indicate the superior performance of the SVSF-KF compared to that of KF.

关键词： Energy management strategy Kalman filter Parameter estimation Proton exchange membrane fuel cell smooth variable structure filter

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Image-Based Visual Servoing Control of Robot Manipulators Using Hybrid Algorithm With Feature Constraints

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IEEE ACCESS 2020年 8卷 223495-223508页

作者： Ren, Xiaolin Li, Hongwen Li, Yuanchun Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China

The challenge in addressing uncalibrated visual servoing (VS) control of robot manipulators with unstructured environments is to obtain appropriate interaction matrix and keep the image features in the field of view (FOV), especially when the non-Gaussian noise disturbance exists in the VS process. In this article, a hybrid control algorithm which combines bidirectional extreme learning machine (B-ELM) with smooth variable structure filter (SVSF) is proposed to estimate interaction matrix and tackle visibility constraints. For VS, the nonlinear mapping between image features and interaction matrix is approximated using the B-ELM learning. To increase the capability of anti-interference, the SVSF is employed to re-estimate interaction matrix. A constraint function presenting feature coordinates and region boundaries is given and added to the velocity controller, which drags image features away from the restricted region and ensures the smoothness of the velocities. Since the camera and robot model parameters are not required in developing the control strategy, the servoing task can be fulfilled flexibly and simply. Simulation and experimental results on a conventional 6-degree-of-freedom manipulator verify the effectiveness of the proposed method.

关键词： Jacobian matrices Robots Cameras Robot kinematics Visualization Visual servoing Calibration Bidirectional extreme learning machine smooth variable structure filter constraint function restricted region visual servoing

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A new dynamic state estimation method for distribution networks based on modified SVSF considering photovoltaic power prediction

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Zhi, Huiqiang Chang, Xiao Wang, Jinhao Mao, Rui Fan, Rui Wang, Tengxin Song, Jinge Xiao, Guisheng State Grid Shanxi Elect Power Co Elect Power Res Inst Taiyuan Peoples R China Shanghai Univ Elect Power Sch Elect Engn Shanghai Peoples R China

The fluctuations brought by the renewable energy access to the distribution network make it difficult to accurately describe the state space model of the distribution network's dynamic process, which is the basis of the existing dynamic state estimation methods such as the Kalman filter. The inaccurate state space model directly causes an error of dynamic state estimation results. This paper proposed a new dynamic state estimation method which can mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Firstly, the proposed method mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Secondly, SVSF filter is introduced to achieve more accurate estimation under noise. The case study and evaluations are carried out based on MATLAB simulation. The results prove that the smooth variable structure filter with photovoltaic power prediction has a better dynamic state estimation effect under the fluctuation of the distribution network compared with the existing Kalman filter.

关键词： dynamic state estimation smooth variable structure filter distribution networks photovoltaic power prediction voltage magnitude voltage phase angle

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Trapped Unburned Fuel Estimation and Robustness Analysis for a Turbocharged Diesel Engine With Negative Valve Overlap Strategy

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2015年第6期137卷 061004-061004页

作者： Chen, Song Yan, Fengjun McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada

Turbocharger and negative valve overlap (NVO) strategy are widely used among advanced combustion modes for internal combustion engines. In order to achieve well emission performance, the NVO can be as large as 100 crank angle (CA) degrees, such that the residual gas fraction can be up to 40%. With such amount of residual gas in the cylinder, the trapped unburned fuel is not trivial. It has a significant impact on the combustion process. However, the trapped unburned fuel mass is hard to be measured directly. In this paper, a novel method based on the signals of oxygen fraction is proposed to estimate it. By analyzing the combustion process, dynamic equations for the intake/exhaust manifolds and in-cylinder oxygen fractions, as well as actual fuel mass in the cylinder are constructed. A smooth variable structure filter (SVSF) was designed to estimate oxygen fractions and further the trapped unburned fuel. As a comparison, Kalman filter (KF) and linear matrix inequality (LMI) based linear parameter-varying (LPV) filter were also applied. Robustness properties of the three observers are analyzed based on the theory of input-to-state (ISS) stability. The proposed models and methods and theoretical analysis are validated and compared through a set of simulations in high-fidelity GT-Power environment. The simulation results match well with theoretical analysis that the SVSF has good properties of strong robustness (with a root mean square error (RMSE) of 0.24, comparing with 0.4 of LPV filter and 0.49 of KF, for the unburned fuel estimation).

关键词： unburned fuel Kalman filter smooth variable structure filter linear parameter-varying estimation input-to-state stability

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Online estimation of state-of-charge inconsistency for lithium-ion battery based on SVSF-VBL

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JOURNAL OF ENERGY STORAGE 2023年 67卷

作者： Wang, Lu Ma, Jian Zhao, Xuan Li, Xuebo Zhang, Kai Changan Univ Sch Automobile Xian 710064 Peoples R China

It is of great significance to estimate cell inconsistency for improving the service life and safety performance of the power battery pack. To accurately estimate the state-of-charge (SOC) inconsistency of cells with different performance parameters and working states, a method combining adaptive robust unscented Kalman filter and smooth variable structure filter with time-varying smoothing boundary layer (SVSF-VBL) is proposed. The cell mean-difference model is used to simulate the behavior characteristics of the battery module, including the cell mean model expressed by the dual polarization (DP) model and the cell difference model characterized by the hypothetical Rint model. Firstly, the improved forgetting factor recursive least square is applied to identify parameters of the DP model, and the unscented Kalman filter incorporating robust estimation and adaptive filter tuning is employed to estimate the SOC of the battery module. Then, SVSF-VBL is used to estimate the SOC difference between each cell and module based on the Rint model for improving the estimation accuracy and robustness. In addition, the comprehensive inconsistency of the cells can be captured by the secondary performance indicator inherent in SVSF-VBL, which contributes to the in-depth study of cell inconsistency. Finally, a series of tests are carried out to verify the performance of the proposed method, and the results show that the method can improve the estimation accuracy and convergence performance while effectively suppressing the system disturbance.

关键词： Adaptive robust unscented Kalman filter Cell mean -difference model State -of -charge inconsistency smooth variable structure filter

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Model-Based Fault Detection of a Battery System in a Hybrid Electric Vehicle

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Journal of Energy and Power Engineering 2013年第7期7卷 1344-1351页

作者： S. Andrew Gadsden Saeid R. Habibi Department of Mechanical Engineering MeMaster University Hamilton L8S 4L7 Ontario Canada

Recently, a new type of IMM （interacting multiple model） method was introduced based on the relatively new SVSF （smooth variable structure filter）, and is referred to as the IMM-SVSF. The SVSF is a type of sliding mode estimator that is formulated in a predictor-corrector fashion. This strategy keeps the estimated state bounded within a region of the true state trajectory, thus creating a stable and robust estimation process. The IMM method may be utilized for fault detection and diagnosis, and is classified as a model-based method. In this paper, for the purposes of fault detection, the IMM-SVSF is applied through simulation on a simple battery system which is modeled from a hybrid electric vehicle.

关键词： Battery system fault detection and diagnosis interacting multiple model smooth variable structure filter Kalman filter.

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A variable structure-Based Estimation Strategy Applied to an RRR Robot System

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JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE 2017年第2期4卷 142-145页

作者： Goodman, Jacob Kim, Jinho Lee, Andrew S. Gadsden, S. Andrew Al-Shabi, Mohammad UMBC Dept Mech Engn 1000 Hilltop Circle Baltimore MD 21250 USA Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada Univ Sharjah Dept Mech Engn Sharjah U Arab Emirates

Nonlinear estimation strategies are important for accurate and reliable control of robotic manipulators. This brief paper studies the application of estimation theory to a simple robotic manipulator. Two estimation techniques are considered: the classic extended Kalman filter (EKF), and the robust smooth variable structure filter (SVSF). The EKF is included to present a basic background in estimation techniques and the SVSF is described and implemented on the system. We simulate the SVSF applied to a dynamically modeled three-link robotic manipulator. The results of the paper demonstrate that nonlinear estimation techniques such as the SVSF can be applied effectively to robots with modeling uncertainty and external disturbances.

关键词： Robotic manipulator estimation theory Kalman filter smooth variable structure filter

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A Novel smooth variable structure smoother for Robust Estimation

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SENSORS 2020年第6期20卷 1781-1781页

作者： Chen, Yu Xu, Luping Yan, Bo Li, Cong Xidian Univ Sch Aerosp Sci & Technol Xian 710126 Peoples R China Univ Bologna Dept Elect Elect & Informat Engn I-47521 Cesena FC Italy Acad Space Elect Informat Technol Xian 710100 Peoples R China

The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) based on SVSF is presented here, which mainly focuses on this drawback and improves the SVSF estimation accuracy of the system. The estimation of the linear Gaussian system state based on SVSS is divided into two steps: Firstly, the SVSF state estimate and covariance are computed during the forward pass in time. Then, the smoothed state estimate is computed during the backward pass by using the innovation of the measured values and covariance estimate matrix. According to the simulation results with respect to the maneuvering target tracking, SVSS has a better performance compared with another smoother based on SVSF and the Kalman smoother in different tracking scenarios. Therefore, the SVSS proposed in this paper could be widely applied in the field of state estimation in dynamic system.

关键词： robust estimation smooth variable structure filter Kalman smoother target tracking uncertain system

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Adaptive SVSF-KF estimation strategies based on the normalized innovation square metric and IMM strategy

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RESULTS IN ENGINEERING 2022年 16卷

作者： Goodman, Jacob Hilal, Waleed Gadsden, S. Andrew Eggleton, Charles D. McMaster Univ Dept Mech Engn Hamilton ON Canada Univ Maryland Baltimore Cty Dept Mech Engn Baltimore MD USA

The smooth variable structure filter (SVSF) is highly robust to modeling uncertainty and unknown disturbances. Recent developments to the SVSF have allowed for the creation of an adaptive estimation scheme termed the SVSF-KF, which balances the optimality of the Kalman filter (KF) with the robustness of the SVSF. The approach utilizes the KF estimate during normal operation, and utilizes the robust SVSF gain to estimate the states during the presence of a fault. However, the gain adaptation involved in detecting a fault and switching between the KF and SVSF estimates suffers from several limitations, including unwanted chattering. In this work, we review the original SVSF-KF approach and present two novel SVSF-KF strategies based on the normalized innovation square metric and the interacting multiple model strategy to address these limitations. Experimental simulations involving a simple harmonic oscillator subject to a fault condition are conducted, which verify the effectiveness of our proposed approaches.

关键词： smooth variable structure filter Kalman filter adaptive filtering sliding mode concepts interacting multiple models modeling uncertainty robust estimation

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