低重心型平行二輪ビークルにおける搭乗者の体格を考慮した車体揺動抑制制御

<div class="htmlview paragraph">For some vehicle dynamics applications, an estimate of a vehicle's center of gravity (cg) height and mass moments of inertia can suffice. For other applications, such as vehicle models and simulations used for vehicle development, these values should be as accurate as possible. This paper presents several topics related to inertial parameter estimation and measurement. The first is a simple but reliable method of estimating vehicle mass moment of inertia values from data such as the center of gravity height, roof height, track width, and other easily measurable values of any light road vehicle. The second is an error analysis showing the effect, during a simple static cg height test, of vehicle motion (relative to the support system) on the vehicle's calculated cg height. A method of accounting for this motion is presented. Similarly, the effects of vehicle motion are analyzed for subsequent mass moment of inertia tests. The third topic is a method of measuring the ratio of the sprung mass to the unsprung mass of any light vehicle without disassembly. Knowledge of this ratio is also important for vehicle development. As an addendum, cg heights and mass moments of inertia measured on the Vehicle Inertia Measurement Facility are presented for several vehicles.</div>

Electrorheological fluids (ERFs) offer rapid control of damping using very low power requirements. Different models have been proposed to simulate the hysteresis phenomenon of ERFs. A modular test facility was designed to perform measurements of a specific electrorheological fluid in squeeze and shear modes in Part I. Based on these measurement cycles, material models for squeeze and shear modes are proposed and corresponding model parameters are identified within this parameter space. The fitted models are benchmarked against the measurement data and are capable of resembling the fluid’s dynamic properties at harmonic excitation, including transitions between Bingham-like and visco-elastic material behavior. Once model parameters are identified, the dynamics of an ERF are resembled excellently by phenomenological models. However, clear trends within the parameter space cannot be stated for all model parameters which prevents the derivation of analytical statements for the model parameters. Since the identification of model parameters is not transferable, a new adaptation is necessary for every application. Nevertheless, the presented procedure can be applied directly in these cases and is robust.

Rainfall distributional properties control hydrologic model parameter importance.

Contemporary tasks of complex system simulation are often related to the issue of uncertainty management. It comes from the lack of information or knowledge about the simulated system as well as from restrictions of the model set being used. One of the powerful tools for the uncertainty management is ensemble-based simulation, which uses variation in input or output data, model parameters, or available versions of models to improve the simulation performance. Furthermore the system of models for complex system simulation (especially in case of hiring ensemble-based approach) can be considered as a complex system. As a result, the identification of the complex model's structure and parameters provide additional sources of uncertainty to be managed. Within the presented work we are developing a conceptual and technological approach to manage the ensemble-based simulation taking into account changing states of both simulated system and system of models within the ensemble-based approach. The states of these systems are considered as a subject of classification with consequent inference of better strategies for ensemble evolution over the simulation time and ensemble aggregation. Here the ensemble evolution enables implementation of dynamic reactive solutions which can automatically conform to the changing states of both systems. The ensemble aggregation can be considered within a scope of averaging (regression way) or selection (classification way, which complement the classification mentioned earlier) approach. The technological basis for such approach includes ensemble-based simulation techniques using domain-specific software combined within a composite application; data science approaches for analysis of available datasets (simulation data, observations, situation assessment etc.); and machine learning algorithms for classes identification, ensemble management and knowledge acquisition.

The research and development results on the con- trol system design governing by the thermal field into the solid sample are given. The design is based on the following steps: 1) definition of model for thermal field dynamics, 2) prior (in first approximation) spectral decomposition of the field, 3) identifica- tion of field and sample parameters, 4) improved spectral de- composition. Computer simulation and implementation of the control system was performed for 1D sample. The efficiency and high accuracy of control system and control algorithms are demonstrated in details. I. INTRODUCTION The design of systems for control of the fields in distrib- uted samples (e.g., thermal or deformations field, form of wave front etc.) when external perturbations take place is a very complicated problem (even for linear systems). The root is that the system under control has infinite number degrees of freedom while real system can have under the control only finite number of degrees of freedom. So there is a competi- tion between high accuracy of a control and excessive com- plexity . The finding of this problem solution is under the de- tail investigation in this paper. To that end the theoretical modeling, computer simulation and implementation in ex- perimental device of the control over distributed sample have been done and definite recommendations for precision con- trol system design are proposed. A number of methods of analysis, control modeling and engineering implementations were developed for design of control of distributed samples. For example in paper (1) plasmotronic dynamics is investigated. Distributed system is approximated by finite-dimensional system for which regula- tor was synthesized that is helped to decrease transition time up to 3…4 times. In the paper (2) method was proposed for control of distributed systems under the uncertain environ- ment by using so called sliding mode. The specific problem of this kind is the design of control over the space-distributed thermal field when only finite number of heaters concentrated near some space points are in use. In the paper (3) review of physical models and engineering solutions for methods of wave front compensation in optical systems is given. There are many other methods were developed for different kinds of application. But nevertheless the problem of control systems design over the fields is still far from its termination. In our investigation the system for automatic compensation of external thermal disturbances in solid sample is studied which serves for precision control of temperature drops over the sample. This system was named as Control System for Thermo-Gradient Stabilization (CSTGS). Decomposition of the thermal field have been done on the base of harmonics method or in the other terms spectral method (4). The advantages of the spectral method over the finite-difference method are marked out in the papers (5), (6). Parameters of the model (parametric identification) may be calculated (ideal identification) by analysis of model prop- erties. But the most appropriate parametric identification are given by experimental measurements. It is shown in our pa- per that only by this way it is possible to achieve high effec- tiveness and high accuracy of control. In what follows we will refer to experimental realization of CSTGS that was specially designed for the purposes of inves- tigations of the control theory problems mentioned above.

Parameter identification of reduced-order electrochemical model simplified by spectral methods and state estimation based on square-root cubature Kalman filter

To use lithium-ion cells on-board electric vehicles (EVs) safely and efficiently, a battery management system (BMS) is essential. It monitors the battery’s states and controls utilization by limiting e.g., current and temperature. Advanced BMS increasingly use electrochemical models to assess internal states of battery cells and apply usage constraints based on these [1]. The performance of an electrochemical BMS is closely tied to the performance of the underlying model and the accuracy of its parameters [2]. We employ a novel re-parametrization method to update electrochemically constrained optimal fast-charging procedure to minimize battery degradation. The parameters of electrochemical models are typically identified through a combination of physico-chemical characterization techniques and curve fitting [3]. Since model parameters change with aging [4], there is a need to re-parametrize electrochemical models on-board to conserve model accuracy. As basis for this re-parametrization, we propose a novel reference performance test that can be incorporated into a conventional over-night charge and performed intermittently.Optimal control has previously been demonstrated for fast-charging in EV applications [1]. However, electrochemical, and mechanical degradation need to be considered to allow optimal fast charging throughout batteries’ lifetimes. Khalik et al. [5] demonstrated how a charging procedure could be designed a-priori for the entire cycle life, considering a direct implementation of the solid-electrolyte-interphase growth and capacity fade due to the associated lithium consumption. Such approaches can extend battery lifetime, but neither yield optimal charging-procedures at every state-of-health nor handle unknown aging modes on the fly. Our proposed approach involves intermittent identification of electrochemical model parameters and subsequent update of the fast-charging protocol such that it adheres to electrochemical constraints throughout the entire battery lifetime.In this work, summarized in Figure 1, we present this methodology and show results from an experimental study testing the proposed approach vs. conventional charging procedures. The experimental study is performed on state-of-the art automotive cells used in heavy-duty electric vehicles.Figure caption: Comparison of a conventional electrochemically constrained fast-charging procedure with the aging-sensitive approach proposed in the present work.

This paper deals with the identification of material parameters in constitutive models of cyclic plasticity for sheet metals using the bending moment vs. curvature diagrams obtained by cyclic bending tests. After a brief discussion on the identification for some simple plasticity models, a new identification method based on the iterative multipoint approximation concept is presented for complicated elastic-plastic consititutive models incorporating many numbers of material parameters. As an example, eight material parameters in Chaboche-Rosselier's model were identified by this method. This approach has been verified by comparing the simulated stress-strain curve using the constitutive model incorporating the identified material parameters with the experimental curve determined by uniaxial tension.

Smart sensors are the core of condition monitoring of power equipment. However, energy supply for sensors in complex electric power field is difficult and in the spotlight. Solar energy as an easily available energy is a good solution to this problem. Accurate identification of photovoltaic cell model parameters can ensure the subsequent stable energy supply. The purpose of this paper is to realize the accurate identification of photovoltaic cell model parameters, so as to serve the energy supply of monitor devices. Firstly, the basic circuit of photovoltaic panel energy supply is built. Secondly, the U-I characteristic curve of photovoltaic cell under different loads is measured. Thirdly, the equivalent parameter model of photovoltaic cell is constructed. Finally, the model parameters are accurately identified based on Levenberg Marquarelt (LM)-Genetic Algorithm (GA). LM algorithm is used for fast global calculation to determine the approximate range of global optimal solution, and then GA is used to further iterate within this range to obtain high-precision local extremum. The proposed method achieved a better fitting effect and results has higher precision in parameter identification of photovoltaic cell. Moreover, under the rated working condition, the errors between the identified parameters and the numerical values provided by the manufacturer are within ±5%. This paper presents an optimization algorithm combining LM and GA, which can accurately identify the parameters of photovoltaic model under different solar radiation levels, and provide strong technical support for the energy supply of sensors and other monitoring equipment.

A procedure for the experimental identification of the material standard linear solid model parameters by means of dynamic mechanical analysis test instrument measurements is presented. Since the standard linear solid material stress–strain functional D(ω) relationship in the frequency domain formally depends on the standard linear solid material parameters, a procedure able to identify these parameters from test measurement estimates is proposed in this work. Nevertheless, a critical, nonlinear and non-parametric approach is to be followed since the number of the material standard linear solid block components is generally unknown, and the material D(ω) shows a highly nonlinear dependency on the unknown standard linear solid material parameters. For these reasons, measurement and test model noise is expected to strongly influence the accuracy of the identification results. A multi-step procedure is presented, consisting first in the non-parametric identification of a frequency dependent, two degrees of freedom model instrument frame by means of a polynomial rational function, where polynomial order and parameters, such as polynomial coefficients and pole-residue couples, are optimally identified by means of an algebraic numerical technique and of an iterative stabilization procedure. Another procedure able to identify the material D(ω) polynomial rational functional relationship in the frequency domain is also proposed, taking into account the dynamic contribution of the instrument frame, of the inertial contribution of the distributed mass of the beam and of the lumped mass of the instrument force measuring system. An effective procedure, able to identify the standard linear solid material model parameters in the time domain from the identified material physical poles, is finally proposed. Some application examples, concerning the identification of the standard linear solid model of a known material and of an unknown composite material, are shown and discussed as well.

ABSTRACTIn this paper, a new improved search space boundary resizing method for an optimal model's parameter identification for continuous real time transfer function by standard genetic algorithms (SGAs) is proposed and demonstrated. Premature convergence to local minima, as a result of search space boundary constraints, is a key consideration in the application of SGAs. The new method improves the convergence to global optima by resizing or extending the upper and lower search boundaries. The resizing of the search space boundaries involves two processes, first, an identification of initial value by approximating the dynamic response period and desired settling time. Second, a boundary resizing method derived from the initial search space value. These processes brought the elite groups within feasible boundary regions by consecutive execution and enhanced the SGAs in locating the optimal model's parameters for the identified transfer function. This new method is applied and examined on two processes, a third-order transfer function model with and without random disturbance and raw data of excess oxygen. The simulation results assured the new improved search space resizing method's efficiency and flexibility in assisting SGAs to locate optimal transfer function model parameters in their explorations.

In this work, the electro-thermal behavior of a prismatic 50 Ah Lithium Manganese Oxide (LMO)/ Graphite cell is investigated. This cell is designed to be integrated in independent 12-cell modules, the global battery rack being composed of several modules. Optimal sizing of the thermal management system requires accurate knowledge end prediction of the thermal load associated with given operating conditions (current profile). It was obtained by identifying the electro-thermal model parameters for a single cell, carried out using a custom electrical bench able to impose a predefined current evolution, controllable by serial interface. From these measurements, the heating power is predicted using the Bernardi model. The identified electrical and thermal model parameters are then integrated in a 3D FEM transient simulation of the cell thermal behavior under realistic conditions.

Several stochastic models, with various degrees of complexity, have been proposed to model the neuronal activity from different parts of the human brain. In this article, we use a simple Ornstein-Uhlenbeck process (OUP) to model the spike activity recorded from the subthalamic nucleus of patients suffering from Parkinson's disease at the time of implantation of the electrodes for deep brain stimulation. From the recorded data, which contains information about the spike times of a single neuron, we identify and extract the model parameters of the OUP. We then use these parameters to numerically simulate the inter-spike intervals and the voltage across the neuron membrane. We finally assess how well the proposed mathematical model fits to the measured data and compare it with other commonly adopted stochastic models. We show an excellent agreement between the computer-generated data according to the OUP model and the measured one, as well as the superiority of the OUP model when compared to the Poisson process model and the random walk model; thus, establishing the validity of the OUP as a simple yet biologically plausible model of the neuronal activity recorded from the subthalamic nucleus of Parkinson's disease patients.

Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I – Building modeling

Cyclic control of water quality in a tidal river segment