Box–Jenkins Black-Box Modeling of a Lithium-Ion Battery Cell Based on Automotive Drive Cycle Data

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources

Parameter identification of permanent magnet synchronous machines (PMSMs) represents a well-established research area. However, parameter estimation of multiple running machines in large-scale applications has not yet been investigated. In this context, a flexible and automated approach is required to minimize complexity, costs, and human interventions without requiring machine information. This paper proposes a novel identification strategy for surface PMSMs (SPMSMs), highly suitable for large-scale systems. A novel multistep approach using measurement data at different operating conditions of the SPMSM is proposed to perform the parameter identification without requiring signal injection, extra sensors, machine information, and human interventions. Thus, the proposed method overcomes numerous issues of the existing parameter identification schemes. An IoT/cloud architecture is designed to implement the proposed multistep procedure and massively perform SPMSM parameter identifications. Finally, hardware-in-the-loop results show the effectiveness of the proposed approach.

Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

Rotordynamic Analysis of Piezoelectric Gas Foil Bearings with a Mechanical Preload Control Based on Structural Parameter Identifications

This paper presents a rotordynamic analysis and experimental characterization of a novel concept of a controllable gas foil bearing (C-GFB) with piezoelectric (PZT) actuators. The C-GFB consists of bump foil structures and three PZT actuators, and the PZT actuators push the bump foil structures in different displacements according to the driving voltage, enabling preload control. In order to predict the piezoelectric preload according to the driving voltage, an equivalent spring model for PZT actuators and foil structures is introduced. In addition, PZT parameters (a piezoelectric constant and stiffness) are measured through parameter identification tests using a latch. Next, static lubrication analysis for C-GFB reveals that the gas-film pressure reduces the effect of piezoelectric preload by up to a maximum of 11%, because the piezoelectric actuator has structural compliance so that it is structurally deformed by the pressure. Finally, nonlinear orbit simulation is performed, and the performance of real-time vibration control of C-GFB is evaluated. The real-time preload control is carried out at ~32.6 krpm, where the rotordynamic instability sufficiently occurs. As the driving voltage increases, the instability suppression and delay effect increase. In particular, when controlled at 150 V, the onset speed of the instability increases to 79.1 krpm. Consequently, this study demonstrates that the GFB with piezoelectric preloads is a simple, effective, and real-time method to improve the rotordynamic stability.

DynManto: A Matlab Toolbox for the Simulation and Analysis of Multibody Systems

The Dynamic Modeling and Analysis Toolbox DynManto is an acedemic Matlab code which allows the modeling, simulation and sensitivity analysis of spatial multibody systems. The kinematics of rigid and flexible bodies is described by the floating frame of reference formulation and the body properties are provided by standard input data files. In this way the evaluation of the equations of motion is computationally efficient and an arbitrary parameterization of the system can be achieved. The latter is important in the automated adjoint sensitivity analysis of multibody systems, which yields gradient information for system analyses, parameter identifications or gradient-based optimizations. The capabilities of DynManto are demonstrated by the application examples of a flexible two-arm manipulator and Chebyshev’s Lambda Mechanism.

A Fractional-Order Kinetic Battery Model of Lithium-Ion Batteries Considering a Nonlinear Capacity

Accurate battery models are integral to the battery management system and safe operation of electric vehicles. Few investigations have been conducted on the influence of current rate (C-rate) on the available capacity of the battery, for example, the kinetic battery model (KiBaM). However, the nonlinear characteristics of lithium-ion batteries (LIBs) are closer to a fractional-order dynamic system because of their electrochemical materials and properties. The application of fractional-order models to represent physical systems is timely and interesting. In this paper, a novel fractional-order KiBaM (FO-KiBaM) is proposed. The available capacity of a ternary LIB module is tested at different C-rates, and its parameter identifications are achieved by the experimental data. The results showed that the estimated errors of available capacity in the proposed FO-KiBaM were low over a wide applied current range, specifically, the mean absolute error was only 1.91%.