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This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.

Improvement of wireless power transfer (WPT) systems is necessary to tackle issues of power transfer efficiency, high costs due to sensor and communication requirements between the transmitter (Tx) and receiver (Rx), and maintenance problems. Analytical techniques and hardware-based synchronization research for Rx-sensorless WPT may not always have been available or accurate. To address these limitations, researchers have recently employed machine learning (ML) to improve efficiency and accuracy. The objective of this work was to replace Tx–Rx communication with ML, utilizing Tx-side parameters to predict the load and coupling coefficients on an LC–LC tuned WPT system. Based on current and voltage features collected on the Tx-side for various load and coupling coefficient values, we developed two models for each load and coupling prediction. This study demonstrated that the extra trees regressor effectively predicted the characteristics of LC–LC tuned WPT systems, with coefficients of determination of 0.967 and 0.996 for load and coupling, respectively. Additionally, the mean absolute percentage errors were 0.11% and 0.017%.

Extended Kalman filter (EKF) is widely used in state estimation of induction motor (IM), and its performance depends on both the use of proper noise covariance matrices and the precise knowledge of IM parameters. These matrices are generally tuned using the trial-and-error method. However, they vary with operating conditions and should be updated online to achieve higher estimation performance. Furthermore, the assumption of constant rotor resistance ( R r ) in the IM model adversely affects the estimation performance at all speeds due to temperature- and frequency-dependent variations of R r . To overcome these issues, an adaptive fading EKF (AFEKF) is designed and tested by simulation and experimental studies. The results, which include performance comparison between EKF and AFEKF, clearly demonstrate the improvement in estimating IM states, especially in transients. Finally, an AFEKF observer compensating for the adverse effects of incorrect selection of noise covariance matrices and parameter changes is introduced to the literature.

Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.

The behaviour of masonry infilled RC frames, with and without openings, subjected to both in-plane (IP) and out-of-plane (OoP) shear loading, indicates the most unfavourable shear (seismic) design condition. A series of calibrated computational micromodels of previously tested 1/2.5 scaled single-bay and single-story RC frames with masonry infill walls, were employed to investigate the effect of openings of various type, size and position, on structure’s shear resistance under different IP and OoP load combinations. The load combination was characterised by an angle α of the IP (or OoP) resultant force, ranging from 0° to 90°. Considered were walls with centrically or eccentrically positioned door or window opening, of an opening to wall area ratio Ao/Ai = 0.1–0.3, walls without openings, and frame without wall i.e. bare frame. A total of 252 models were considered. The obtained behaviour revealed the specific load resisting mechanisms and accompanying failure modes. The IP–OoP shear resistance interaction curves and surfaces were constructed with regard to load direction α and opening to wall area ratio Ao/Ai. Considering these criteria, the IP–OoP shear resistance estimation functions were derived in order to improve the existing shear (seismic) design methodologies of masonry infilled RC frames.

Timely and accurate fault diagnosis for a lithium-ion battery pack is critical to ensure its safety. However, the early fault of a battery pack is difficult to detect because of its unobvious fault effect and nonlinear time-varying characteristics. In this paper, a fault diagnosis method based on relative entropy and state of charge (SOC) estimation is proposed to detect fault in lithium-ion batteries. First, the relative entropies of the voltage, temperature and SOC of battery cells are calculated by using a sliding window, and the cumulative sum (CUSUM) test is adopted to achieve fault diagnosis and isolation. Second, the SOC estimation of the short-circuit cell is obtained, and the short-circuit resistance is estimated for a quantitative analysis of the short-circuit fault. Furthermore, the effectiveness of our method is validated by multiple fault tests in a thermally coupled electrochemical battery model. The results show that the proposed method can accurately detect different types of faults and evaluate the short-circuit fault degree by resistance estimation. The voltage/temperature sensor fault is detected at 71 s/58 s after faults have occurred, and a short-circuit fault is diagnosed at 111 s after the fault. In addition, the standard error deviation of short-circuit resistance estimation is less than 0.12 Ω/0.33 Ω for a 5 Ω/10 Ω short-circuit resistor.

Introduction. Scalar control (SC) and field-oriented control (FOC) are widely used in sensorless induction motor (IM) drives for their balance of performance and cost. Among estimation techniques, the voltage-model (VM) based model reference adaptive system (MRAS) is preferred in industry due to its simple structure and low computational load. Problem. Traditional VM-based MRAS schemes are highly sensitive to parameter uncertainties, especially to variations in stator resistance Rs caused by temperature changes. These variations degrade flux estimation accuracy, leading to significant speed-tracking errors, increased transients, and reduced stability in both SC and FOC. Goal. This study quantitatively evaluates how the estimation of stator resistance Rs and the dependent rotor resistance Rr affects the speed-control performance of sensorless SC and FOC under parameter mismatch. Methodology. An improved VM-based MRAS is proposed with parallel Rs estimation and Rr updated via a linear relation to Rs. Estimator stability and convergence are proven using Lyapunov theory. The estimator is integrated into SC and FOC and tested in MATLAB/Simulink under identical conditions, including a sudden 30 % increase in resistance. Speed tracking is quantified using the integral of time-weighted absolute error (ITAE). Results. Parameter estimation markedly enhances the robustness of both strategies. In sensorless SC, ITAE drops by about 66.2 % (5.512 to 1.863), indicating much lower transient oscillations. In sensorless FOC, ITAE falls by about 54 % (0.7075 to 0.323), with speed overshoot nearly eliminated (0.031). Scientific novelty. The study provides a unified quantitative comparison of sensorless SC and FOC using ITAE under identical operating and estimation conditions, revealing different levels of performance recovery with the proposed dual-resistance adaptation. Practical value. The findings guide the design of more reliable industrial IM drives, showing that while FOC retains superior dynamics, SC with estimation becomes a robust, cost-effective option for applications with significant parameter uncertainty. References 31, table 1, figures 13.

Lithium-ion batteries have become a crucial energy source in electric vehicles and energy storage systems. Battery safety and reliability have emerged as significant issues with their increased usage. Failure in non-contact current sensors can lead to inaccuracies in internal current measurement, posing serious risks like overcharging and over-discharging, which may cause fires within battery circuits. To address these challenges, this study proposes an add-on-type current sensor fault diagnosis algorithm based on data analysis using a covariance matrix. The current sensor freezing fault can be captured by determining the internal resistance using the current–voltage relationship. The proposed algorithm enables fault detection without imposing additional load or modifications on the existing battery system. The performance of the proposed current sensor fault diagnosis algorithm was experimentally verified by using two cylindrical battery cells having different life conditions.

A sensorless online temperature estimator is presented in this paper, which estimates the temperature using a novel signal injection strategy. This allows to eliminate the temperature sensors in the machine, as well as their faults, increasing the system reliability. A double dead-time DC signal is injected in the machine, adding a controlled offset in the control drive through the inverter. The proposed strategy eliminates the effect of the dead-time in the injected signal, which is an important drawback in DC injection strategies for resistance estimation. Furthermore, additional hardware is not needed. The strategy has been implemented in an inverter-fed railway traction induction machine. The proposed algorithm has been validated in a real test-bench.

The experimental evaluation of fire resistance limits for steel constructions with fire-retardant coatings consists of a lot of experiments on the heating of steel structures of buildings by solving a heat engineering problem at various fire conditions. Building design implies the assessment of compliance of actual fire resistance limits for steel constructions with the required limits. Fire resistance limits for steel constructions are determined for “standard” temperature mode, and this can lead to overestimated fire resistance and underestimated heat influence for a real fire. Estimation of the convergence for “standard” temperature mode and possible “real” fire mode, as well as of the compliance of actual fire resistance limits with real fire conditions, was realized in the following stages: mathematical modeling of real fire development by the field model in software package Fire Dynamics Simulation (FDS) with various fire loads and mathematical modeling of steel construction heating for the standard temperature mode obtained by modeling “real” fire modes (the finite difference method of solving the Fourier heat conduction equation at external and internal nonlinearities was used for modeling the process of steel structure heating with the implementation in the ANSYS mechanical software package). Experiments of the assessment of fire-protective paint’s effectiveness were carried out for standard temperature mode and obtained by modeling “real” fire modes. The equivalent fire duration dependence on fire load type was determined. This dependence can be taken into account in determination of fire resistance limits for steel constructions in warehouse building roofing. Fire-protective paint effectiveness was estimated for “standard” temperature mode and various other temperature modes.