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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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A hybrid observer for SOC estimation of lithium-ion battery based on a coupled electrochemical-thermal model

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2019年第15期16卷 1527-1538页

作者： Xu, Xing Lin, Yuzhen Wang, Feng Yang, Shichun Zhou, Zhiguang Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang Jiangsu Peoples R China Beihang Univ Transportat Sci & Engn Beijing Peoples R China Chery Automobile Co New Energy Dev Dept Wuhu Anhui Peoples R China

An accurate prediction of the state of charge (SOC) of lithium-ion battery provides crucial information for the Battery Management System (BMS). In this paper, a simplified uneven electrochemical model coupled to a bulk thermal model of Lithium-ion battery is built up. The parameters of battery model identified in previous research can provide a more accurate description of the battery dynamic characteristics. This implementation of the coupled model is then applied to amalgamation with the extend Kalman filter combined with smoothing variable structure filter (EK-SVSF) to obtain an accurate and robust SOC result. MATLAB/Simulink and experiments, including 1C pulse discharge and NEDC cycle tests are adopted to evaluate the performance of the proposed hybrid algorithm. The simulation results indicate that the SOC error is less than 2%, therefore the algorithm is suitable for the new energy vehicle power BMS.

关键词： A coupled electrochemical-thermal model a hybrid observer extended Kalman filter lithium-ion battery smooth variable structure filter state of charge estimation

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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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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 014503-014503-9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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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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Robust fault diagnosis of an electro-hydrostatic actuator using the Novel dynamic second-order SVSF and IMM strategy

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INTERNATIONAL JOURNAL OF FLUID POWER 2014年第3期15卷 181-196页

作者： Afshari, Hamed H. Gadsden, Stephen Andrew Habibi, Saeid R. McMaster Univ Dept Mech Engn Hamilton ON Canada Univ Maryland Baltimore Cty Dept Engn Mech Baltimore MD 20742 USA

This paper introduces a new robust fault detection and identification (FDI) structure applied to an electro-hydrostatic actuator (EHA) experimental setup. This FDI structure consists of the dynamic second-order smooth variable structure filter (Dynamic second-SVSF) and the interacting multiple model (IMM) strategy. The dynamic second-order smooth variable structure filter (SVSF) is a new robust-state estimation method that benefits from the robustness and chattering suppression properties of second-order sliding mode systems. It produces robust-state estimation by preserving the first and second-order sliding conditions such that the measurement error and its first difference are pushed towards zero. Moreover, the EHA prototype works under two different operational regimes that are the normal EHA mode and the faulty EHA mode. The faulty EHA setup contains two types of faults, namely friction and internal leakage. The FDI structure contains a bank of dynamic second-order SVSFs estimating state variables based on these models. The IMM strategy combines these filters in parallel and determines the particular operating regime based on the system models and the input-output data. Experimental results demonstrate superior performance in terms of accuracy, robustness, and smoothness of state estimates.

关键词： smooth variable structure filter dynamic sliding mode systems interacting multiple model fault detection and diagnosis hydrostatic actuator

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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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A Multiple Model Adaptive SVSF-KF Estimation Strategy 28

A Multiple Model Adaptive SVSF-KF Estimation Strategy

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

作者： Goodman, Jacob M. Wilkerson, Stephen A. Eggleton, Charles Gadsden, S. Andrew Univ Maryland Baltimore Cty Autonomous Syst Res Lab 1000 Hilltop Circle Baltimore MD 21250 USA York Coll Penn Dept Mech Engn 441 Country Club Rd York PA 17403 USA Univ Guelph Intelligent Control & Estimat Lab 50 Stone Rd East Guelph ON N1G 2W1 Canada

ISBN: (纸本)9781510627024

State estimation strategies play a critical role in obtaining accurate information about the state of dynamic systems as they develop. Such information can be important on its own and critical for precise and predictable control of such systems. The Kalman filter (KF) is a classic algorithm and among the most powerful tools in state estimation. The Kalman filter however can be sensitive to modeling uncertainty and sudden changes in system dynamics. The smooth variable structure filter (SVSF) is a relatively new estimation strategy that operates on variable structure concepts. In general, the SVSF has the advantage that is can be quite robust to modeling uncertainty and sudden fault conditions. Recent advancements to the SVSF, such as the addition of a covariance formulation, and the derivation of a time varying smoothing boundary layer (VBL), have allowed for combined SVSF - KF strategies. In a typical SVSF-KF approach, the VBL is used to detect the presence of a system fault, and switch from the more optimal KF gain to the more robust SVSF gain. While this approach has been proven effective in several cases, there are circumstances where the VBL will fail to indicate the presence of an ongoing fault. A new form of the SVSF-KF is proposed, based on the framework of the Multiple Model Adaptive Estimator.

关键词： smooth variable structure filter SVSF-KF Kalman filter MMAE fault detection robust estimation

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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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