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Interacting Multiple Model Strategy for Electric Vehicle Batteries State of Charge/Health/ Power Estimation

IEEE ACCESS 2021年 9卷 109875-109888页

作者： Rahimifard, Sara Ahmed, Ryan Habibi, Saeid McMaster Univ Ctr Mechatron & Hybrid Technol CMHT Dept Mech Engn Hamilton ON L8S 4L8 Canada

States estimation of lithium-ion batteries is an essential element of Battery Management Systems (BMS) to meet the safety and performance requirements of electric and hybrid vehicles. Accurate estimations of the battery's State of Charge (SoC), State of Health (SoH), and State of Power (SoP) are essential for safe and effective operation of the vehicle. They need to remain accurate despite the changing characteristics of the battery as it ages. This paper proposes an online adaptive strategy for high accuracy estimation of SoC, SoH and SoP to be implemented onboard of a BMS. A third-order equivalent circuit model structure is considered with its state vector augmented with two more variables for estimation including the internal resistance and SoC bias. An Interacting Multiple Model (IMM) strategy with a smooth variable structure filter (SVSF) is then employed to determine the SoC, internal resistance, and SoC bias of a battery. The IMM strategy results in the generation of a mode probability that is related to battery aging. This mode probability is then combined with an estimation of the battery's internal resistance to determine the SoH. The estimated internal resistance and the SoC are then used to determine the battery SoP which provides a complete estimation of the battery states of operation and condition. The efficacy of the proposed condition-monitoring strategy is tested and validated using experimental data obtained from accelerated aging tests conducted on Lithium Polymer automotive battery cells.

关键词： Batteries Estimation Battery charge measurement Resistance Integrated circuit modeling Adaptation models Temperature measurement Lithium-ion battery battery management system interacting multiple model smooth variable structure filter state of charge state of health state of power

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Model-based condition monitoring for lithium-ion batteries

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JOURNAL OF POWER SOURCES 2015年 295卷 16-27页

作者： Kim, Taesic Wang, Yebin Fang, Huazhen Sahinoglu, Zafer Wada, Toshihiro Hara, Satoshi Qiao, Wei Univ Nebraska Dept Comp Sci & Comp Engn Lincoln NE 68588 USA Mitsubishi Elect Res Labs Cambridge MA 02139 USA Univ Kansas Dept Mech Engn Lawrence KS 66045 USA Mitsubishi Electr Corp Adv Technol R&D Ctr Amagasaki Hyogo 6618661 Japan Univ Nebraska Dept Elect & Comp Engn Lincoln NE 68588 USA

Condition monitoring for batteries involves tracking changes in physical parameters and operational states such as state of health (SOH) and state of charge (SOC), and is fundamentally important for building high-performance and safety-critical battery systems. A model-based condition monitoring strategy is developed in this paper for Lithium-ion batteries on the basis of an electrical circuit model incorporating hysteresis effect. It systematically integrates 1) a fast upper-triangular and diagonal recursive least squares algorithm for parameter identification of the battery model, 2) a smooth variable structure filter for the SOC estimation, and 3) a recursive total least squares algorithm for estimating the maximum capacity, which indicates the SOH. The proposed solution enjoys advantages including high accuracy, low computational cost, and simple implementation, and therefore is suitable for deployment and use in real-time embedded battery management systems (BMSs). Simulations and experiments validate effectiveness of the proposed strategy. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Lithium-ion battery condition monitoring Fast upper-triangular and diagonal recursive least squares Maximum capacity estimation Recursive total least squares smooth variable structure filter

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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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Adaptive state estimation for tracking of civilian aircraft

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第6期12卷 777-784页

作者： Patra, Nilanjan Sadhu, Smita Ghoshal, Tapan Kumar Jadavpur Univ Dept Elect Engn Kolkata 700032 India

Tracking of manoeuvring subsonic aerospace vehicles has traditionally been handled by state estimators. Ordinary state estimators perform poorly as the concerned process model can only be defined imprecisely. This contribution evaluates and compares the performance of adaptive single mode non-linear estimators against several versions of other estimators. The primary comparison is with the recently introduced smooth variable structure filter (SVSF) which is claimed to inherit the robustness of variable structure approach. Both the above types of estimators are then benchmarked with a well-known version of interacting multiple model (IMM) estimator which treats the manoeuvring aircraft as a hybrid system consisting of multiple modes. Monte Carlo simulation has been used and several descriptors have been used for comparison. The comparison demonstrates that a version of non-linear adaptive estimators incorporating sigma points and a single constant turn model performs substantially better than the SVSF and its tracking performance approaches that obtainable by the IMM estimator. Novelty of this contribution lies in providing a detailed comparison of the above three families of estimators, which provides adequate insight for selecting tracking estimators by trading off estimation accuracy, algorithm complexity, tuning requirement, and computational load.

关键词： Monte Carlo methods nonlinear estimation aircraft adaptive estimation state estimation nonlinear adaptive state estimation manoeuvring subsonic aerospace vehicle tracking civilian aircraft tracking adaptive single mode nonlinear estimator smooth variable structure filter SVSF interacting multiple model estimator IMM estimator Monte Carlo simulation

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Kalman and smooth variable structure filters for Pose Estimation in Robotic Visual Servoing

Kalman and Smooth Variable Structure Filters for Pose Estima...

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2017 3rd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2017)

作者： Xi-yuan LU Liang DU Xiao-lin REN Bo DONG Yuan-chun LI Department of Control Science and Engineering Changchun University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

Pose estimation problem of an object in real time is an important issue for robotic visual servoing(RVS). Many pose estimation schemes in RVS rely on an extended Kalman filter(EKF) that provides good accurate estimation. However, it may cause the estimation to become unstable because of nonlinear modeling uncertainties. While, smooth variable structure filter(SVSF) is a new method that is more robust to disturbances and uncertainties. In this paper, a novel pose estimate method is developed based on the EKF and SVSF. It is combined the accuracy of the EKF and the robustness provided by the SVSF that will lead to more accurate state estimates and improve robustness to modeling errors and uncertainties. The resulting algorithms are called the EKF-SVSF. The simulation results are provided to demonstrate the robustness and accuracy.

关键词： Pose estimation Robotic visual servoing(RVS) Kalman filter(KF) smooth variable structure filter

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A New Robust filtering Strategy for Linear Systems

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2013年第1期135卷 014503页

作者： Gadsden, S. A. Habibi, S. R. McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada

For linear and well-defined estimation problems with Gaussian white noise, the Kalman filter (KF) yields the best result in terms of estimation accuracy. However, the KF performance degrades and can fail in cases involving large uncertainties such as modeling errors in the estimation process. The smooth variable structure filter (SVSF) is a relatively new estimation strategy based on sliding mode theory and has been shown to be robust to modeling uncertainties. The SVSF makes use of an existence subspace and of a smoothing boundary layer to keep the estimates bounded within a region of the true state trajectory. Currently, the width of the smoothing boundary layer is chosen based on designer knowledge of the upper bound of modeling uncertainties, such as maximum noise levels and parametric errors. This is a conservative choice, as a more well-defined smoothing boundary layer will yield more accurate results. In this paper, the state error covariance matrix of the SVSF is used for the derivation of an optimal time-varying smoothing boundary layer. The robustness and accuracy of the new form of the SVSF was validated and compared with the KF and the standard SVSF by testing it on a linear electrohydrostatic actuator (EHA). [DOI: 10.1115/1.4006628]

关键词： Kalman filter smooth variable structure filter sliding mode estimation linear estimation optimality

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AUTOMOTIVE TRACKING TECHNIQUE USING A NEW IMM BASED PDA-SVSF

AUTOMOTIVE TRACKING TECHNIQUE USING A NEW IMM BASED PDA-SVSF

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ASME International Mechanical Engineering Congress and Exposition (IMECE)

作者： Attari, Mina Habibi, Saeid McMaster Univ Dept Mech Engn Ctr Mechatron & Hybrid Technol Hamilton ON L8S 4L7 Canada

ISBN: (纸本)9780791849613

Car tracking algorithms are important for a number of applications, including self-driving cars and vehicle safety systems. The probabilistic data association (PDA) algorithm, in conjunction with Kalman filter (KF), and interacting multiple model (IMM) are well studied, specifically in the aero-tracking applications. This paper studies single targets while performing maneuvers in the presence of clutter, which is a common scenario for road vehicle tracking applications. The relatively new smooth variable structure filter (SVSF) is demonstrated to be robust and stable filtering strategy under the presence of modeling uncertainties. In this paper, SVSF based PDA technique is combined with IMM method. The new method, referred to as IMM-PDA-SVSF is simulated under several possible car motion scenarios. Also, the algorithm is tested on a real experimental data acquired by GPS device.

关键词： smooth variable structure filter automotive tracking interacting multiple model probabilistic data association

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Robustness Comparison of Some State Estimation Methods with an Explicit Consideration of Modeling Uncertainties 28

Robustness Comparison of Some State Estimation Methods with ...

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IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Afshari, Hamed H. Al-Ani, Dhafar Habibi, Saeid McMaster Univ Dept Mech Engn Hamilton ON L8S 4L8 Canada

ISBN: (纸本)9781479958290

This paper presents a comparative analysis of well-known state estimation methods that are commonly used in real systems. The aim of this research is to measure and then evaluate the robustness (i. e., a measure of performance when a small and deliberate changes are made to the method conditions) of these methods against modeling uncertainties. The state estimation methods include the Kalman filter, the 1st-Order smooth variable structure filter (1st-order SVSF), and the new developed Dynamic 2nd-Order SVSF. A relatively new performance robustness criterion (so-called robustness index) is adopted in this work to first measure the robustness of the estimation methods, and then, evaluate their performance. The robustness index is calculated for each method when modeling uncertainties are explicitly considered. Simulation analysis is performed using the linear model of an Electro-Hydrostatic Actuator (EHA) setup under the normal and uncertain conditions. Simulation results showed the superior performance of the Dynamic 2nd-Order SVSF over other methods in terms of robustness against modeling uncertainties.

关键词： robust state estimation smooth variable structure filter dynamic sliding mode systems modeling uncertainties

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Derivation of the sliding innovation information filter for target tracking 32

Derivation of the sliding innovation information filter for ...

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

作者： Alsadi, Naseem Hilal, Waleed Gadsden, S. Andrew Al-Shabi, Mohammad McMaster Univ 1280 Main St Hamilton ON L8S 4L8 Canada Univ Sharjah Univ City Rd Sharjah 27272 U Arab Emirates

ISBN: (数字)9781510662117

ISBN: (纸本)9781510662100;9781510662117

An information filter is one that propagates the inverse of the state error covariance, which is used in the state and parameter estimation process. The term 'information' is based on the Cramer-Rao lower bound (CRLB), which states that the mean square error of an estimator cannot be smaller than an amount based on its corresponding likelihood function. The most common information filter (IF) is derived based on the inverse of the Kalman filter (KF) covariance. This paper introduces preliminary work completed on developing the information form of the sliding innovation filter. The SIF is a relatively new type of predictor-corrector estimator based on sliding mode concepts. In this brief paper, the recursive equations used in the sliding innovation information filter (SIIF) are derived and summarized. Preliminary results of application to a target tracking problem are also studied.

关键词： Information filter Kalman filter sliding innovation filter smooth variable structure filter target tracking

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Offline Parameter Identification and SOC Estimation for New and Aged Electric Vehicles Batteries

Offline Parameter Identification and SOC Estimation for New ...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Ahmed, Ryan Rahimifard, Sara Habibi, Saied McMaster Univ Dept Mech Engn Hamilton ON Canada

ISBN: (纸本)9781538693100

The hybrid ( HEVs) and battery electric vehicles (BEVs) represent a sustainable alternative in compare to conventional, fossil fuel-based vehicles. Battery pack is a major and the most expensive part of electric vehicles. It requires accuracy, real-time monitoring, and control. Parameters such as state of charge (SOC) and state of health (SOH) have to monitor accurately to guarantee battery safety and reliability and avoid overcharge or under-discharge conditions. These conditions can lead to irreversible capacity degradation and power fade. An accurate battery model with robust estimation method is needed for battery condition monitoring. In this paper, a way to estimate battery parameters is proposed for the first order equivalent circuit battery model (OCV-R-RC) using genetic algorithm (GA) optimization at various ages of the battery to track the changes. The smooth variable structure filter (SVSF) strategy has been considered to estimate the state of charge based on the optimized model. An aging test has been conducted over a period of 12 months using real-world driving scenarios. Experimental results are provided to show the efficacy of the proposed approach.

关键词： Lithium-Ion Batteries genetic algorithm optimization equivalent circuit-based battery model parameter identification smooth variable structure filter state of charge

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