Dynamic Working Conditions Research Articles

Dynamic analysis for ultra-supercritical boiler water wall considering uneven heat flux distribution and combustion instability

The dynamic characteristics of water-cooled wall are of great significance to the evaluation of the safety and flexibility for the boiler during peak shaving process. However, few dynamic studies considered the uneven heat flux distribution on the water-cooled wall, let alone the variable heat load caused by the combustion instability. In this paper, a segmented dynamic model is developed for the water-cooled wall of a 660 MW ultra supercritical boiler. The model is validated under steady and dynamic working conditions, respectively. Then, the step responses of the steam temperature in different flow loops under different load conditions are comparatively studied under the disturbances of single-parameter changes. In the cases of 10 % step decrease of either the feedwater temperature, the heat load or the feedwater mass flowrate, the outlet steam temperature is influenced greatly by the step decreased feedwater temperature under 100 % BMCR condition, while it is impacted most by the step decreased feedwater mass flow under 50 % THA condition. Under these disturbances, the outlet steam temperature of the tube with higher heat loads has larger variation and longer response time. In addition, the simulated combustion instability by the sinusoidal fluctuating heat load on the water wall is employed to explore the influences of the flame fluctuation amplitude and frequency to the water wall. The maximum steam temperature rises with the increase in amplitude but drops with the frequency of flame fluctuation. The thresholds for the flame fluctuation amplitude and frequency are obtained under different load conditions.

A real-time temperature field prediction method for steel rolling heating furnaces based on graph neural networks

In the billet reheating process during steel rolling, the real-time and accurate prediction of the temperature field is a prerequisite for the dynamic regulation of the heating process, which is crucial for ensuring the quality of billet reheating and reducing the energy consumption of the reheating furnace. The most commonly used finite element thermal field simulation and analysis methods are unable to meet the demand for real-time prediction under dynamic working conditions. Furthermore, traditional machine learning methods struggle to handle irregular data that includes spatial location information, resulting in inaccurate temperature field predictions, which in turn affect the furnace control efficiency and the quality of the product. In light of these limitations, this study proposes a Graph Neural Network (GNN)-based temperature field prediction method for steel rolling reheating furnaces. This method considers the interactions between the nodes within the reheating furnace and constructs a temperature field topology graph to effectively capture these interactions, including heat conduction and convection. Additionally, in the feature fusion and state updating mechanism of the model, we introduce process parameters, such as the air-fuel ratio, as additional inputs to enhance the ability of the GNN to handle complex variables. This enables real-time accurate simulation of the internal temperature distribution of the reheating furnace. The experimental results demonstrate that the model prediction error is controlled within 2.9 % and the response time is maintained within 20 ms, thereby validating the reliability and efficacy of the method in practical applications.

A Novel Rotating Wireless Power Transfer System for Slipring with Redundancy Enhancement Characteristics

This study investigates the dynamics of wireless power supply technology under rotation and its system redundancy, aiming to design a redundant, rotating wireless power supply system. In order to satisfy specifications of redundancy and fault tolerance, the circuit design of the wireless power transmission system was developed, and a planar three-sector coil coupling mechanism was designed; finally, the stability and power output characteristics of the system were assessed under static and dynamic working conditions, and the results show that the maximum output power of the system can reach 3 kW and the efficiency is more than 91% under both static and dynamic working conditions. The study improved the rotating wireless charging system’s efficiency, which improves the energy utilization efficiency.

Internal Temperature Estimation of Lithium Batteries Based on a Three-Directional Anisotropic Thermal Circuit Model

In order to improve the accuracy of internal temperature estimation in batteries, a 10-parameter time-varying multi-surface heat transfer model including internal heat production, heat transfer and external heat transfer is established based on the structure of a lithium iron phosphate pouch battery and its three directional anisotropic heat conduction characteristics. The entropy heat coefficient, internal equivalent heat capacity and internal equivalent thermal resistance related to the SOC and temperature state of the battery were identified using experimental tests and the least square fitting method, and were then used for online calculation of internal heat production and heat transfer in the battery. According to the time-varying and nonlinear characteristics of the heat transfer between the surface and the environment of the battery, an internal temperature estimation algorithm based on the square root cubature Kalman filter was designed and developed. By iteratively calculating the estimated surface temperature and the measured value, dynamic tracking and online correction of the internal temperature of the battery can be achieved. The verification results using FUDS and US06 dynamic working condition data show that the proposed method can quickly eliminate the influence of initial temperature deviations and accumulated process errors and has the characteristics of a high estimation accuracy and good robustness. Compared with the estimation results of the adaptive Kalman filter, the proposed method improves the estimation accuracy of FUDS and US06 working conditions by 67% and 54%, respectively, with a similar computational efficiency.

A novel multi-factor fuzzy membership function - adaptive extended Kalman filter algorithm for the state of charge and energy joint estimation of electric-vehicle lithium-ion batteries

In view of the unmeasurable state parameters of electric-vehicle lithium-ion batteries, this paper investigates a novel multi-factor fuzzy membership function - adaptive extended Kalman filter (MFMF-AEKF) algorithm for the online joint estimation of the state of charge and energy. Strong nonlinear characteristics of model parameters are characterized by considering multiple processing factors of electrochemical and diffusion effects for lithium-ion batteries and constructing an optimized multifactor coupling model. In the proposed MFMF-AEKF method, multi-space-scale factors are introduced to realize the numerical analysis of the multi-factor coupled model parameters and state estimation under dynamic working conditions of electric-vehicle lithium-ion batteries. The proposed MFMF-AEKF algorithm estimates the state of charge (SOC) with the overall best mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and maximum error (ME) values of 1.822 %, 4.322 %, 1.947 %, and 2.954 %, respectively, under challenging working conditions. And The MAE, MAPE, RMSE, and ME values for the state of energy (SOE) are 0.617 %, 1.711 %, 0.695 %, and 1.011 %, respectively. Both state estimation results are better than the traditional method. The proposed MFMF-AEKF algorithm has higher estimation accuracy which provides a feasible estimation algorithm for the joint SOC and SOE of lithium-ion batteries.

Adaptive thermal error prediction for CNC machine tool spindle using online measurement and an improved recursive least square algorithm

Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for smart manufacturing. However, the prediction performance of existing methods deteriorates significantly with dynamic working conditions of machine tools because training from static conditions leads to the inability to adapt to dynamic conditions. Therefore, an adaptive thermal error modeling method using online measurement and an improved recursive least square algorithm is proposed to fill this research gap, which updates the thermal error model adaptively to ensure that dynamic working conditions are learned in real time. Particularly, Spearman's rank correlation coefficient method is first adopted for temperature-sensitive point selection to capture the nonlinear relationship between temperature and thermal error variables. Furthermore, a variable-forgetting factor-based recursive least square (VFF-RLS) algorithm is proposed to improve the prediction performance, in which the proposed variable forgetting factor is adaptively updated according to real-time thermal error data collected by online measurement. The experimental results showed that the proposed VFF-RLS method can maintain a high prediction accuracy of 1.75 μm and robustness of 0.16 μm on both constant and dynamic working conditions. The effectiveness of the VFF-RLS method is validated by verification experiments.

Deep transfer learning enables battery state of charge and state of health estimation

In the realm of lithium-ion battery state estimation, traditional data driven approaches face challenges in accurately estimating state of charge and state of health throughout the battery's life cycle under dynamic working condition, and there is still a lack of research on models that can fulfill these requirements simultaneously. To address these issues, this study proposes an adaptive convolutional gated recurrent unit with Kalman filter for state of charge estimation throughtout battery's full life cycle, leveraging transfer learning and deep learning techniques. Additionally, an adaptive convolutional gated recurrent unit with average post-processor is developed to estimate the battery state of health under dynamic working conditions, using voltage, current, temperature, state of charge, and accumulated discharge capacity as input features. Furthermore, a joint adaptive deep transfer learning model is proposed for simultaneously state of charge and state of health estimation through battery's full life cycle under dynamic working conditions. Experimental results validate the feasibility, accuracy, and robustness of the proposed models.

Optimal power distribution control in modular power architecture using hydraulic free piston engines

Vehicle modularization has become an emerging trend in the automotive industry, leading to research on modular configuration, composition, and related control strategies. In this paper, we propose a modular power system with a hydraulic free piston engine (HFPE) as the power unit and develop a power distribution control strategy to enhance the overall efficiency of the system. Firstly, we determine the configuration scheme of the modular power system and establish a simulation model of the HFPE using MATLAB/Simulink. We conduct principle verification of the simulation model. Secondly, based on the simulation model of HFPE, we research the power unit control strategy using the machine learning regression prediction algorithm, enabling dynamic working condition switching of the power unit. Next, we propose a power distribution optimization algorithm which is named as the Rule Based Double Iterative Optimization Algorithm (RBDI) and compare it with several mature optimization algorithms under the framework of model predictive control, considering related constraints. Finally, we validate the performance of the proposed power distribution control strategy using a hardware-in-loop system. The results demonstrate that the output power of the modular power system can be effectively ensured. Compared with the average distribution algorithm (AVE), the genetic algorithm (GA), and the ameliorated particle swarm optimization algorithm (APSO), the overall working efficiency of the modular power system using the proposed control strategy is increased by 6.57%, 6.13%, and 5.59%, respectively, under the three test driving cycles.

A Novel Method for State of Charge Estimation in Lithium-Ion Batteries Using Temporal Convolutional Network and Multi-Verse Optimization

This study presents a novel data-driven method for state-of-charge estimation in lithium-ion batteries. It integrates a temporal convolutional network with multi-verse optimization to enhance the accuracy of predicting the state of charge. The temporal convolutional network possesses advantages such as an extended memory window and efficient parallel computation, exhibiting exceptional performance in time-series tasks for state of charge estimation. Its hyperparameters are optimized by adopting multi-verse optimization to obtain better model performance. The driving model utilizes various measurable data as inputs, including battery terminal voltage, current, and surface temperature. To validate the effectiveness of the proposed method, extensive datasets from diverse dynamic working conditions at different ambient temperatures are employed for model training, validation, and testing. The numerical outcomes provide evidence of the proposed method’s superior performance compared to the other two methods, providing a more robust and accurate solution for the state of charge estimation in lithium-ion batteries.

Application of Modeling and Control Approaches of Piezoelectric Actuators: A Review

Piezoelectric actuators find extensive application in delivering precision motion in the micrometer to nanometer range. The advantages of a broader range of motion, rapid response, higher stiffness, and large actuation force from piezoelectric actuators make them suitable for precision positioning applications. However, the inherent nonlinearity in the piezoelectric actuators under dynamic working conditions severely affects the accuracy of the generated motion. The nonlinearity in the piezoelectric actuators arises from hysteresis, creep, and vibration, which affect the performance of the piezoelectric actuator. Thus, there is a need for appropriate modeling and control approaches for piezoelectric actuators, which can model the nonlinearity phenomenon and provide adequate compensation to achieve higher motion accuracy. The present review covers different methods adopted for overcoming the nonlinearity issues in piezoelectric actuators. This review highlights the charge-based and voltage-based control methods that drive the piezoelectric actuators. The survey also includes different modeling approaches for the creep and hysteresis phenomenon of the piezoelectric actuators. In addition, the present review also highlights different control strategies and their applications in various types of piezoelectric actuators. An attempt is also made to compare the piezoelectric actuator’s different modeling and control approaches and highlight prospects.

A Wide Operating Range Ejector with Part Nested Nozzles for PEMFC Hydrogen Recirculation System

The ejector drives unreacted hydrogen from the anode to improve fuel utilization ratio and discharges redundant water to prevent flooding and shutdown in the proton exchange membrane fuel cell (PEMFC). However, the traditional fixed structure ejector cannot meet the recycling requirements in the whole dynamic working condition of the fuel cell. In this article, a part nested four-nozzle (PNFN) ejector is proposed to enhance the hydrogen recycling efficiency under variable working conditions of the PEMFC by restricting the nozzle flow as 10%, 20%, 20%, and 50% of the fuel cell-rated power, respectively. Systematical analyses are performed on the experimentally verified 3D model to study inner flow characteristics and performance under different nozzle running modes. The results indicate that the PNFN ejector satisfies the recirculation ratio requirements in the power range of 34–220 kW within the 7–9 bar suitable supply pressure. By comparing with traditional ejectors, the PNFN ejector has a wider working range and especially outputs better performance in the low power range.

An Enhanced Single-Particle Model Using a Physics-Informed Neural Network Considering Electrolyte Dynamics for Lithium-Ion Batteries

As power sources for electric vehicles, lithium-ion batteries (LIBs) have many advantages, such as high energy density and wide temperature range. In the algorithm design process for LIBs, various battery models with different model structures are needed, among which the electrochemical model is widely used due to its high accuracy. However, the electrochemical model is composed of multiple nonlinear partial differential equations (PDEs) that make the simulating process time-consuming. In this paper, a physics-informed neural network single-particle model (PINN SPM) is proposed to improve the accuracy of the single-particle model (SPM) under high C-rates, while ensuring high solving speed. In PINN SPM, an SPM-Net is designed to solve the distribution of lithium-ion concentration in the electrolyte. In the neural network learning process, a loss function is designed based on the physical constraints brought by the PDEs, which reduces the error of the neural network under dynamic working conditions. Finally, the PINN SPM proposed in this paper can achieve a maximum relative error of up to 1.2% compared with the high-fidelity data generated from the P2D model under various conditions. Additionally, the PINN SPM is 20.8% faster than traditional numerical solution methods with the same computational resources.

Rolling Bearing Fault Diagnosis across Operating Conditions Based on Unsupervised Domain Adaptation

In practical industrial scenarios, mechanical equipment frequently operates within dynamic working conditions. To address the challenge posed by the incongruent data distribution between source and target domains amidst varying operational contexts, particularly in the absence of labels within the target domain, this study presents a solution involving deep feature construction and an unsupervised domain adaptation strategy for rolling bearing fault diagnosis across varying working conditions. The proposed methodology commences by subjecting the original vibration signal of the bearing to a fast Fourier transform (FFT) to extract spectral information. Subsequently, an innovative amalgamation of a one-dimensional convolutional layer and an auto-encoder were introduced to construct a convolutional auto-encoder (CAE) dedicated to acquiring depth features from the spectrum. In a subsequent step, leveraging the depth features gleaned from the convolutional auto-encoder, a balanced distribution adaptation (BDA) mechanism was introduced to facilitate the domain adaptation of features from both the source and target domains. The culminating stage entails the classification of adapted features using the K-nearest neighbor (KNN) algorithm to attain cross-domain diagnosis. Empirical evaluations are conducted on two extensively used datasets. The findings substantiate that the proposed approach is capable of accomplishing the cross-domain fault diagnosis task even without labeled data within the target domain. Furthermore, the diagnostic accuracy and stability of the proposed method surpass those of various other migration and deep learning approaches.

Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions

The state of health (SOH) of batteries is an important but unmeasurable parameter closely related to battery safety and durability. However, most existing SOH estimation strategies rely on a specific load profile (e.g., constant current). To tackle this issue, we here report a method that first converts the dynamic voltage trajectories to the curves corresponding to the constant current profiles using a neural network model. Then, the aging characteristics are selected and the battery SOH is estimated accordingly from the converted voltage data using a Gaussian process regression model. Batteries with different aging degrees are tested with different working conditions to verify the proposed method. Numerically, the errors of the voltage reconstruction are bounded within 2 mV, while the SOH estimation errors under four dynamic working conditions remain within 2%. Our technical approach reduces the dependency of traditional SOH estimation methods on specific working conditions and shows strong potential for practical applications.

Optimization of Heat Dissipation and Control Scheme for Wind Turbine Gearbox Based on FloMaster-Simulink co-Simulation

Due to favorable wind conditions and steadily rising temperatures, the demand for heat dissipation for the gearbox of wind turbines rises every spring and summer, and the energy consumption of the heat dissipation system rises as well. In cases where the gearbox oil temperature is excessively high due to poor heat dissipation, the wind turbine unit may only operate at a limited load or shut down. This article suggests a restoration strategy that uses seawater to cool the gearbox lubrication system, based on the operation of the gearbox cooling system of wind turbines. The original wind turbine gearbox cooling system and the enhanced seawater cooling system simulation models are built at the same time, and dynamic working conditions simulation research is done by utilizing the FloMaster simulation platform. To achieve automatic management of the gearbox temperature, we alter the fan or pump’s speed by the oil temperature at the gearbox’s outlet. We design a control scheme based on the working principle and use the FloMaster-Simulink co-simulation to calculate the operating characteristics of the system under dynamic working conditions. The results show that by simultaneously controlling the speed of the pump and the speed of the fan, the cooling system has the best control effect and the lowest energy consumption, providing a reference for the development of the gearbox cooling system of wind turbines.

Unsupervised Domain Deep Transfer Learning Approach for Rolling Bearing Remaining Useful Life Estimation

Abstract Accurate estimation of remaining useful life (RUL) becomes a crucial task when bearing operates under dynamic working conditions. The environmental noise, different operating conditions, and multiple fault modes result in the existence of considerable distribution and feature shifts between different domains. To address these issues, a novel framework TSBiLSTM is proposed that utilizes 1DCNN, SBiLSTM, and attention mechanism (AM) synergically to extract highly abstract feature representation, and domain adaptation is realized using the MK-MMD (multi-kernel maximum mean discrepancy) metric and domain confusion layer. One-dimensional CNN (1DCNN) and stacked bidirectional LSTM (SBiLSTM) are utilized to take advantage of spatiotemporal features with attention mechanism (AM) to selectively process the influential degradation information. MK-MMD provides effective kernel selection along with a domain confusion layer to effectively extract domain-invariant features. Both experimentation and comparison studies are conducted to verify the effectiveness and feasibility of the proposed TSBiLSTM model. The generalized performance is demonstrated using IEEE PHM data sets based on root mean squared error, mean absolute error, absolute percent mean error, and percentage mean error. The promising RUL prediction results validate the superiority and usability of the proposed TSBiLSTM model as a promising prognostic tool for dynamic operating conditions.

High-Precision and Robust SOC Estimation of LiFePO4 Blade Batteries Based on the BPNN-EKF Algorithm

The lithium iron phosphate (LiFePO4) blade battery is a long, rectangular-shaped cell that can be directly integrated into battery pack systems. It enhances volumetric power density, significantly reduces costs, and is widely utilized in electric vehicles. However, the flat open circuit voltage and significant polarization differences under wide operational temperatures are challenging for accurate voltage modeling of battery management systems (BMSs). In particular, inaccurate state of charge (SOC) estimation may cause overcharging and over-discharging risks. To accurately perceive the SOC of LiFePO4 blade batteries, a SOC estimation method based on the backpropagation neural network-extended Kalman filter (BPNN-EKF) algorithm is proposed. BPNN is a neural network model that utilizes the backpropagation algorithm to update model parameters, while EKF is an optimal estimation algorithm. Firstly, dynamic working condition tests, including the New European Driving Cycle (NEDC) and high-speed working (HSW) condition tests, are conducted under a wide temperature range (−25–43 °C). HSW conditions refer to a simulated operating condition that mimics the driving of an electric vehicle on a highway. The minimum voltage of the battery system is used as the output for training the BPNN model. We derive the Kalman gain by combining the BPNN output voltage. Additionally, the EKF algorithm is employed to correct the SOC value using voltage error information. Concerning long SOC calculation intervals, capacity errors, initial SOC errors, and current and voltage sampling errors, the maximum SOC estimation RMSE is 3.98% at −20 °C NEDC, 3.62% at 10 °C NEDC, and 1.68% at 35 °C HSW. The proposed algorithm can be applied to different temperatures and operations, demonstrating high robustness. This BPNN-EKF algorithm has the potential to be embedded in electric vehicle BMS systems for practical applications.

Electromagnetic field calculation and analysis of short primary linear induction motor for electromagnetic launch

AbstractThe aim of this study is to determine the electromagnetic field spatial distribution and longitudinal end effect characteristics of short primary linear induction motor (SPLIM) in the field of electromagnetic launch. First, in accordance to the electromagnetic model assumption of SPLIM and one‐dimensional electromagnetic field theory, the analytical expression of the whole‐region magnetic flux density of air‐gap is derived using electromagnetic field boundary conditions in line with the actual physical meaning. Second, the finite element method used in comparing the analytical calculation values of key physical quantities, such as air‐gap flux density and secondary eddy current in a multidimensional manner under various static and dynamic working conditions and verifying the correctness of the analytical calculation of the electromagnetic field. On this basis, the air‐gap flux density of SPLIM is decomposed according to the different generation mechanisms, and the spatial distribution of each part of the flux density is studied. Spatial Fourier analysis of flux density is conducted, the end effect coefficient is defined, and the change rule of the end effect coefficient with the design parameters of the motor is studied. Finally, a megawatt‐class high thrust prototype test platform was built, and the measured electromagnetic force was compared with the electromagnetic force calculated by air gap magnetic field and secondary eddy current. The steady‐state error of the two was less than 2%, which verified the accuracy of the analysis and calculation.

Digital twin for zinc roaster furnace based on knowledge-guided variable-mass thermodynamics: Modeling and application

Roaster furnace is a large-scale equipment in zinc smelting process, which plays an significant role in safety production and enviromental protection. An accurate digital twin of roaster furnace can help to explore the influence of control parameters and serve as a test platform to verify the effectiveness of control strategies. However, the traditional thermodynamics cannot be used for dynamic modeling, and inability to measure key data greatly affects the accuracy of kinetic modeling. Therefore, this paper establishes a novel digital twin of zinc roaster furnace based on knowledge-guided variable-mass thermodynamics. First, based on the integration of mechanism analysis for mass balance and energy balance, a dynamic modeling method is proposed. Then, particle swarm optimization (PSO) algorithm is introduced to optimize the parameters of conversion rates guided by knowledge. Finally, by connecting the dynamic model with distributed control system (DCS) through OPC communication protocol, a digital twin of roaster furnace is constructed. Extensive experiments show that the simulation results of the digital twin roughly agree with the actual industrial data under steady and dynamic working conditions, and the application of the digital twin on the performance analysis of control parameters and testing of control strategies can provide guidance for the optimal control of roaster furnace.

Hamilton–Jacobi Inequality Adaptive Robust Learning Tracking Controller of Wearable Robotic Knee System

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton–Jacobi Inequality (HJI) approach. WRK dynamics are established using the Lagrange approach at the outset of the analysis. Afterwards, the L2 gain technique is applied to enhance the control motion solutions and provide the main features of the designed WRK control systems. To prove the stability of the controlled system, the HJI approach is investigated next using optimization techniques. The synthesized RBF NN algorithm supports the easy implementation of the adaptive controller, as well as ensuring the stability of the WRK system. An analysis of the numerical simulation results is performed in order to demonstrate the robustness and effectiveness of the proposed tracking control algorithm. The results showed the ability of the suggested controller of this study to find a solution to uncertainties.