Estimation Strategies for the Condition Monitoring of a Battery Systemin a Hybrid Electric Vehicle

This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time.Comment: 18 pages, 16 figures, ISRN Signal Processing, 201

Integrated control and estimation based on sliding mode control applied to electrohydraulic actuator

Many problems in tracking control have been identified over the years, such as the availability of systems states, the presence of noise and system uncertainties, and speed of response, just to name a few. This thesis is concerned with developing novel integrated control and estimation algorithms to overcome some of these problems in order to achieve an efficient tracking performance. Since there are some significant advantages associated with Sliding Mode Control (SMC) or Variable Structure Control (VSC), (fast regulation rate and robustness to uncertainties), this research reviews and extends new filtering concepts for state estimation, referred to as the Variable Structure Filter (VSF)and Smooth Variable Structure Filter (SVSF). These are based on the philosophy of Sliding Mode Control.The VSF filter is designed to estimate some of the states of a plant when noise and uncertainties are presented. This is accomplished by refining an estimate of the states in an iterative fashion using two filter gains, one based on a noiseless system with no uncertainties and the second gain which reflects these uncertainties. The VSF is combined “seamlessly” with the Sliding Mode Controller to produce an integrated controller called a Sliding Mode Controller and Filter (SMCF). This new controller is shown to be a robust and effective integrated control strategy for linear systems. For nonlinear systems, a novel integrated control strategy called the Smooth Sliding Mode Controller and Filter (SSMCF), fuses the SMC and SVSF in a particular form to address nonlinearities. The gain term in the SVSF is redefined to form a new algorithm called the “SVSF with revised gain” in order to obtain a better estimation performance. Its performance is compared to that of the Extended Kalman Filter (EKF) when applied to a particular nonlinear plant.The SMCF and SSMCF are applied to the experimental prototype of a precision positioning hydraulic system called an ElectroHydraulic Actuator (EHA) system. The EHA system is known to display nonlinear characteristics but can approximate linear behavior under certain operating conditions, making it ideal to test the robustness of the proposed controllers.The main conclusion drawn in this research was that the SMCF and SSMCF as developed and implemented, do exhibit robust and high performance state estimation and trajectory tracking control given modeling uncertainties and noise. The controllers were applied to a prototype EHA which demonstrated the use of the controllers in a “real world” application. It was also concluded that the application of the concepts of VSC for the controller can alleviate a challenging mechanical problem caused by a slip-stick characteristic in friction. Another conclusion is that the revised form of the SVSF could obtain robust and fast state estimation for nonlinear systems.The original contributions of the research include: i) proposing the SMCF and SSMCF, ii) applying the Sliding Mode Controller to suppress cross-over oscillations caused by the slip-stick characteristics in friction which often occur in mechanical systems, iii) the first application of the SVSF for state estimation and iv) a comparative study of the SVSF and Extended Kalman Filter (EKF) to the EHA demonstrating the superiority of the SVSF for state estimation performance under both steady-state and transient conditions for the application considered.The dissertation is written in a paper format unlike the traditional Ph.D thesis manuscript. The content of the thesis discourse is based on five manuscripts which are appended at the end of the thesis. Fundamental principles and concepts associated with SMC, VSF, SVSF and the fused controllers are introduced. For each paper, the objectives, approaches, typical results, conclusions and major contributions are presented. Major conclusions are summarized and original contributions reiterated

State of charge estimation based on a realtime battery model and iterative smooth variable structure filter

State of charge estimation based on a realtime battery model and iterative smooth variable structure filter Kim, T.; Wang, Y.; Sahinoglu, Z.; Wada, T.; Hara, S.; Qiao, W TR2014-041 May 2014 Abstract This paper proposes a novel real-time model-based state of charge (SOC) estimation method for lithium-ion batteries. The proposed method includes: 1) an electrical circuit battery model incorporating the hysteresis effect, 2) a fast upper-triangular and D-diagonal recursive least square (FUDRLS)-based online parameter identification algorithm for the electrical battery model, and 3) an iterated smooth variable structure filter (ISVSF) for SOC estimation. The proposed method enables an accurate and robust condition monitoring for lithium-ion batteries. Due to its low complexity, the proposed method is suitable for the real-time embedded battery management system (BMS) application. Simulation and experiment are performed to validate the proposed method for a polymer lithium-ion cell. IEEE PES Innovative Smart Grid Technologies Conference -Asia (ISGT Asia) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved

On dimension reduction in Gaussian filters

A priori dimension reduction is a widely adopted technique for reducing the computational complexity of stationary inverse problems. In this setting, the solution of an inverse problem is parameterized by a low-dimensional basis that is often obtained from the truncated Karhunen-Loeve expansion of the prior distribution. For high-dimensional inverse problems equipped with smoothing priors, this technique can lead to drastic reductions in parameter dimension and significant computational savings. In this paper, we extend the concept of a priori dimension reduction to non-stationary inverse problems, in which the goal is to sequentially infer the state of a dynamical system. Our approach proceeds in an offline-online fashion. We first identify a low-dimensional subspace in the state space before solving the inverse problem (the offline phase), using either the method of "snapshots" or regularized covariance estimation. Then this subspace is used to reduce the computational complexity of various filtering algorithms - including the Kalman filter, extended Kalman filter, and ensemble Kalman filter - within a novel subspace-constrained Bayesian prediction-and-update procedure (the online phase). We demonstrate the performance of our new dimension reduction approach on various numerical examples. In some test cases, our approach reduces the dimensionality of the original problem by orders of magnitude and yields up to two orders of magnitude in computational savings

Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation

In this paper, we present the optimization formulation of the Kalman filtering and smoothing problems, and use this perspective to develop a variety of extensions and applications. We first formulate classic Kalman smoothing as a least squares problem, highlight special structure, and show that the classic filtering and smoothing algorithms are equivalent to a particular algorithm for solving this problem. Once this equivalence is established, we present extensions of Kalman smoothing to systems with nonlinear process and measurement models, systems with linear and nonlinear inequality constraints, systems with outliers in the measurements or sudden changes in the state, and systems where the sparsity of the state sequence must be accounted for. All extensions preserve the computational efficiency of the classic algorithms, and most of the extensions are illustrated with numerical examples, which are part of an open source Kalman smoothing Matlab/Octave package.Comment: 46 pages, 11 figure

Indirect approach to continuous time system identification of food extruder

A three-stage approach to system identification in the continuous time is presented which is appropriate for day-to-day application by plant engineers in the process industry. The three stages are: data acquisition using relay feedback; non-parametric identification of the system step response; and parametric model fitting of the identified step response. The method is evaluated on a pilot-scale food-cooking extruder

Stochastic Analysis of the LMS Algorithm for System Identification with Subspace Inputs

This paper studies the behavior of the low rank LMS adaptive algorithm for the general case in which the input transformation may not capture the exact input subspace. It is shown that the Independence Theory and the independent additive noise model are not applicable to this case. A new theoretical model for the weight mean and fluctuation behaviors is developed which incorporates the correlation between successive data vectors (as opposed to the Independence Theory model). The new theory is applied to a network echo cancellation scheme which uses partial-Haar input vector transformations. Comparison of the new model predictions with Monte Carlo simulations shows good-to-excellent agreement, certainly much better than predicted by the Independence Theory based model available in the literature