Full Operating Conditions Research Articles

An IMFO-LSTM_BIGRU combined network for long-term multiple battery states prediction for electric vehicles

Predicting the state of power batteries is crucial for ensuring the safe and reliable operation of electric vehicles. However, accurately forecasting multiple battery parameters under full operating conditions remains challenging. Traditional prediction models struggle to accurately represent the actual battery behavior due to irregular vehicle driving patterns. Furthermore, long-term prediction of battery states, essential for potential fault diagnosis, poses significant difficulties for conventional neural networks. To address the issue of error accumulation in recurrent neural networks (RNNs) during multi-parameter battery prediction across various operating conditions, we propose a combined model featuring multi-level feature extraction. This model integrates Long Short-Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BiGRU) networks, where the LSTM layer employs forget gates to filter out unnecessary data while preserving valuable information. The bidirectional update gate in the BiGRU layer effectively combines historical and future data to enhance time series modeling. This comprehensive approach improves sequence modeling efficiency, thereby increasing reliability. However, the multi-level feature extraction structure introduces higher-dimensional hyperparameters, complicating the optimization process. To maintain prediction accuracy across multiple battery parameters, we propose an improved moth-flame optimization (IMFO) algorithm to optimize the complex hyperparameters of the LSTM_BiGRU model. Extensive experiments conducted on a real-world vehicle dataset demonstrate that the IMFO-LSTM_BiGRU combined network surpasses existing state-of-the-art methods in terms of accuracy and stability.

Closed-Loop Pointing Feedback for Heliostats in Concentrating Solar Power

Abstract Concentrating solar power (CSP) facilities need to ensure alignment of all sun-tracking heliostats onto the sunlight receiver. A method is proposed to keep all heliostats in a CSP facility under closed-loop pointing control while also providing feedback on the detailed alignment of the segment mirrors of each heliostat. The method is based on the sunlight directed toward the receiver, and thus works under full operational conditions and without the need of secondary optical alignments. It is based on retroreflectors (“retros”) to simultaneously return samples of the sunlight reflected by each mirror back to that same mirror. It goes beyond previous efforts at using retros by placing them into the concentrated sunlight, instead of in its periphery. Quartz glass will be used for its heat tolerance, and reflectivity modulation for visibility is achieved by rotating the retros. The technology can be retrofitted into existing CSP facilities to improve operational efficiency, and it can be used to relax the stability requirements of heliostats, and thus their cost, in the planning of new ones.

Fixed-time fractional-order sliding mode controller with disturbance observer for U-tube steam generator

Water level control of a U-tube steam generator plays a critical and challenging role in ensuring safe and reliable operation for a pressurized-water reactor-type nuclear power plant, as both excessively high and low water level in the U-tube steam generator would affect the normal operation of the nuclear power plant and even lead to unplanned shutdown events. This work presents a fixed-time fractional-order sliding mode water level controller coupled with a fixed-time disturbance observer, aiming to further improve the water level control performance under full operating conditions, considering uncertainties and actuator saturation. The proposed scheme is formulatedbased on the popular Irving water level model with uncertainties and the pre-existing actual water level control framework, while at the same time incorporating the benefits of the state-of-the-art anti-windup techniques, disturbance observer-based feedforward compensation techniques, fractional-order calculus, sliding mode control techniques, and fixed-time stability theorem, such that the transient water level response and steady-state control accuracy can be further enhanced even in the presence of uncertainties and actuator saturation phenomenon. It is theoretically proved that under the proposed scheme, both the estimation error of the uncertainties and the water level control error can be driven to zero within a fixed time regardless of their initial values by Lyapunov’s theorem. Meanwhile, simulation studies, involving the comparisons with a real-world adopted water level controller and a pre-existing integer-order sliding mode water level controller coupled with an uncertainty estimator, reveal the feasibility and superiority of the proposed approach.

Stability analysis of grid-connected inverter under full operating conditions based on small-signal stability region

Impedance analysis is a practical approach for assessing the small-signal stability of renewable energy power systems. However, existing research predominantly focuses on specific operating conditions, neglecting the fundamental principles governing stability evolution under time-varying operating conditions. This paper presents a methodology to develop the small-signal stability region (SSSR) for grid-connected inverters using the impedance method. A comprehensive stability analysis for grid-connected inverter systems is performed based on the stability region. Firstly, the multi-parameter SSSR of the grid-connected inverter is defined according to both the aggregated impedance criterion and the generalized Nyquist criterion. Furthermore, a polynomial approximation expression for the SSSR boundary is derived. Secondly, the sensitivity analysis of operating points and control parameters is performed under full operating conditions to investigate their impact on stability based on the quantified boundary. The analyses reveal that the stability of the grid-connected inverter system near the SSSR boundary decreases with increasing active power and decreasing reactive power but exhibits an initial increase followed by a decrease with a larger PLL bandwidth. Finally, the accuracy of the stability region and the influence of key parameters are verified through case studies and experiments. The study in this paper can be used for quantitative analysis of stability margins and decision guidance of control optimization for grid-connected inverters.

Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm

The flexible operation capability of combined heat and power (CHP) unit under full operating conditions must be promoted to suppress the power grid fluctuation in large-scale integration of renewable energy. However, obtaining CHP unit model under large-scale variable loads and deep peak shaving is extremely challenging. To this end, an intelligent modeling method incorporating improved crossformer and precise sparrow search algorithm is designed to provide reference for flexible controller design. Firstly, to address the strong time dependence of CHP unit boiler-turbine coupling process, a bidirectional gated recurrent unit is employed as the position encoding to extract the hidden temporal feature. Secondly, a novel encoder structure with multi-receptive field convolution module is designed to enhance the local feature extraction capability of the network, which is conducive to the analysis of multivariable coupling characteristics of CHP unit. On this basis, a precise sparrow search algorithm with stronger search ability is formed by combining Tent chaotic mapping, osprey optimization algorithm, Cauchy mutation and quantum behavior, which provides support for the dynamic characteristics analysis of CHP unit. The time dependence, local feature information and optimal parameter settings are fully considered in the comprehensive modeling scheme, which results in an accurate identification model for CHP unit under full operating conditions. Finally, the effectiveness of the proposed method is verified based on the actual operating data of a 350 MW CHP unit. The accuracy of established model is greater than 99.4 %, which can well reflect the nonlinear characteristics of the unit. Therefore, the built model can be used for controller design and simulation analysis.

The friction behavior and wear mechanism of RV reducer gear steel (20CrMo) subjected to three different heat treatment processes from −20 °C to 100 °C

The gear friction pairs in RV reducer always withstand a broad range of temperature, sliding velocity and heavy load. In the research, three types of heat treatment methods (carburizing + quenching, nitrocarburizing and high concentration nitrocarburizing) are applied on the 20CrMo (cycloidal gear material) disc to study the tribological behavior and wear mechanism. The friction coefficient, wear depth and wear rate of the three types of friction pairs under various temperature (−20 °C, 25 °C, 50 °C, 100 °C) are measured and the microstructure characteristics are analyzed by SEM and EDS. The results reveal that high temperature can dramatically decrease the wear scar dimensions and wear rate due to the more active lubricant. Although the lubricant lost its activity at −20 °C, the friction coefficient and maximum wear depth for GCr15/20CrMo (carburizing + quenching) and GCr15/20CrMo (high concentration nitrocarburizing) are relatively less than 0.1 and 4 μm. The wear rate of GCr15/20CrMo (carburizing + quenching) and GCr15/20CrMo (high concentration nitrocarburizing) are 14.49 μm3/N·m and 17.20 μm3/N·m, which are only 28.7% and 34.1% of GCr15/20CrMo (nitrocarburizing). With the temperature increases to 25 °C, tribofilm is more likely to form on the 20CrMo specimen after nitrocarburizing, leading to better wear resistance. At high temperature (50 °C and 100 °C), GCr15/20CrMo (high concentration nitrocarburizing) system presents excellent tribological behavior by the combination of good lubricant activity, formation of tribofilm and refined nitrides. The wear rate even reaches 0.45 μm3/N·m and 0.43 μm3/N·m at the sliding velocity of 300 mm/s and 562 mm/s under 100 °C. Overall, specimen with high concentration nitrocarburizing process exhibits exceptional wear performance under full operating conditions of RV reducer.

Energy Consumption Optimization for the Cold Source System of a Hospital in Shanghai - Part II: Operation Control Strategy Using EnergyPlus

Background: Energy consumption is a common problem in hospital buildings, which consume twice that of other public buildings. Therefore, it is of great significance to study the control strategy for the efficient operation of the cold source system. Objective: The study aimed to explore an efficient operation control strategy for cold source system, and new technologies and patents have emerged for the same. This work, utilizing EnergyPlus, modelled and analyzed the cold source system in a Shanghai hospital to optimize its operation. Methods: The accuracy of the simulation was verified by comparing it with experimental data. Based on the simulation results, the factors influencing the energy efficiency of the cold source system were analyzed, and then the operation control strategy of the cold source system was obtained. The XGBoost was used to fit the simulation results, and the operation strategy under full operating conditions was obtained. Results: The simulated results indicated the average energy saving rates during the summer season of the chillers, the chilled water pumps, the cooling water pumps, and the cooling towers to be 6.5%, -4.0%, 38.3%, and 5.4%, respectively, under the optimal operation control strategy. The average system Coefficient of Performance (COP) of the cold source system was 5.9, and the total energy consumption was 957016.3 kW·h, which was 7.1 % energy saving compared to that under the original operation. Conclusion: The conclusions of this study could provide references for the hospital buildings’ cold source system and group control method. This study has important practical significance for the efficient control strategy of cold source systems.

High-resolution and reduced-order multi-physics simulation on thermal–hydraulic and thermoelectric characteristics of the thermionic space reactor core

The simulation of a thermionic space reactor core necessitates a comprehensive consideration of the intricate interplay between fluid flow, heat transfer, and thermionic energy conversion, constituting a multi-physics coupling challenge. Given the absence of a thermionic conversion model in contemporary CFD (Computational Fluid Dynamics) codes, accurately simulating the thermal–hydraulic and thermo-electric behavior of thermionic reactors in existing CFD software is particularly challenging. To analyze the multi-physics coupling phenomena in thermionic reactors, this paper revises the conjugate heat transfer solver in the proprietary 3D (three-dimensional) finite-volume CFD code, mFVM (modular Finite-Volume Method Code), to enable bidirectional coupling with the thermionic conversion model. This enhancement aims to predict the high-resolution three-dimensional temperature field and thermoelectric characteristics of the thermionic reactor core. Furthermore, a reduced-order multi-physics model of the thermionic reactor core is established using RESYS (Reactor System Simulation Code). The numerical simulation outcomes for both the high-resolution and reduced-order multi-physics models, pertaining to electrically heated and fission-heated TFEs (Thermionic Fuel Elements), are validated against experimental data. The results indicate that the emitter electron cooling effects caused by thermionic emission reduced the maximum temperature of emitter by about 200 K for both electrically heated and fission-heated TFE. Therefore, a bidirectional coupling between the thermal–hydraulic model and the thermionic conversion model is essential for precisely calculating the temperature field within the TFE and the thermionic reactor core. Thereafter, both the high-resolution model and the reduced-order model are utilized to investigate the thermal–hydraulic and thermoelectric characteristics of the TOPAZ-II reactor core. The calculated electrical power of the TOPAZ-II reactor is 5.39 kWe using the mFVM model and 5.47 kWe using the RESYS model. Finally, an analysis of the thermoelectric conversion characteristics based on the simulation results of the reduced-order model reveals that the TOPAZ-II reactor system is capable of load-following only when the load resistance exceeds 0.06 Ω under nominal full power operation conditions.

Optimization of electric power prediction of a combined cycle power plant using innovative machine learning technique

AbstractAccurate prediction of electric power generation in combined cycle power plants is challenging yet crucial, especially when employing machine learning techniques like artificial neural networks. This research presents an advanced forecasting model based on the robust adaptive neuro‐fuzzy inference system to estimate electric power generation under full operating conditions. The research dataset comprises 9568 data points featuring four input parameters, including ambient temperature, ambient pressure, exhaust vacuum, and relative humidity, spanning 6 years of the publicly available UCI Machine Learning Repository. These data were partitioned into 70% for training, 30% for validation, and 0% for testing to ensure robustness. A hybrid approach is implemented for optimization, combining the least squares method and gradient descent. The first‐order Sugeno fuzzy model was adopted to defuzzification the entire fuzzy set, achieving optimal results with three membership functions assigned to each input variable. This configuration minimizes the training's root mean square error values and the checking error of 3.8395 and 3.7849 regarding the generalized bell‐shaped membership functions, improving computational efficiency. These values are optimum when employing the root mean square error performance metric of identical studies. The validation of the adaptive neuro‐fuzzy inference system method and an optimal data selection strategy for training should be considered for optimum outcomes in the energy field.

A full operating conditions ejector model for refrigeration systems driven by low-grade heat sources

The ejector refrigeration technology driven by low-grade heat sources constitutes as one of the crucial means to solve the energy shortage in present-day society and to reduce the global environmental pollution. Thermodynamics ejector models are the basis of performance prediction, system control and structural design in ejector refrigeration systems driven by low-grade heat sources. However, the majority of present ejector models are unable to simultaneously meet the general feasibility of full operating conditions and the level of prediction accuracy for improving the energy efficiency of the refrigeration system. In this study, a novel theoretical model of ejectors is successfully derived based on the foundation of compound-choking theory and gas dynamic relations, which easily predicts the entrainment performance under the on-design and off-design conditions in ejector refrigeration systems. Indeed, a direct solution algorithm is presented to quickly and accurately solve this model and a linear correlation is employed between the mixing loss coefficient and the back pressure at the subcritical mode. The developed model is verified with experimental results in R245fa, R134a and R600a ejector refrigeration system. The results show that the model provides a precise estimation of the entrainment performance with the mean relative errors of 2.45 %, 5.49 % and 3.67 % for R245fa, R134a and R600a refrigerants at full working conditions, while the prediction of critical condensing temperatures is considerably approximated by the experimental measurements. In the comparative analysis with the previous model, this model demonstrates a superior prediction performance, especially in the subcritical mode using the linear function mixing loss coefficient. This study will be a valuable aid in the improvement of energy efficiency in ejector refrigeration systems.

A flexible and deep peak shaving scheme for combined heat and power plant under full operating conditions

Improving the flexible and deep peak shaving capacity of combined heat and power (CHP) plant under full operating conditions to facilitate renewable energy consumption is the main choice of novel power system. Accordingly, a dry/wet state automatic conversion control scheme fuses the precise mechanism structure, reinforcement learning and multi-objective model predictive control (MPC) algorithms is designed to promote the deep peak shaving ability of CHP plant in this paper. Firstly, the detailed mechanism models of once-through boiler at dry and wet state are presented by analyzing the dynamic characteristics of steam-water flow. Secondly, the large-scale unknown parameters in mechanism models are identified via the Takagi-Sugeno fuzzy modeling, reinforcement learning algorithms and actual dry/wet state conversion data. Thanks to the proposed hybrid modeling strategy, the high-precision boiler-steam unit models at dry and wet state are rapid obtained. Then, to meet different peak shaving demands, the multi-objective MPC algorithm is respectively constructed under dry and wet state with the comprehensive consideration of operational constraints, load tracking error, algorithm stability, power generation cost, CO2 and NOx emission costs. Aiming at maximizing the peak shaving efficiency and flexible operation ability of plant, a dry/wet automatic control scheme combined the designed multi-objective MPC algorithms and identified dry/wet state models is proposed. Finally, the load variation rate of 5 % and 2 % RCM/min is respectively achieved in the dry/wet state conversion tests on a 350 MW CHP plant based on the proposed control scheme. Thus, the rapid and thorough dry/wet state conversion performance of once-through boiler has successfully improved the operational flexibility of CHP plant under full operating conditions.

AI-Based Design With Data Trimming for Hybrid Phase Shift Modulation for Minimum-Current-Stress Dual Active Bridge Converter

Dual active bridge (DAB) topology has been recognized as the key circuit for the next generation of high-frequency-link power conversion systems. In order to realize desired operating performance, DAB modulation strategies need to be selected carefully. Different modulation strategies have been considered and combined into a hybrid one, which is able to fully optimize performance. However, to develop a hybrid modulation strategy, the conventional methods including harmonic model and piecewise model have difficulty in balancing modeling accuracy and manpower burden. Although recent data-driven modulation approaches can automate the design process, the accuracy of data-driven models decreases nontrivially with the existence of outliers and without sufficient data. Hence, to alleviate human-dependence and improve modeling accuracy, this paper proposes an AI-based design with data trimming (AI-DT) for hybrid phase shift modulation. Two two-degree-of-freedom modulation strategies are considered for the sake of optimal current stress performance. AI-DT firstly adopts the one-class support vector machine (SVM) to exclude outliers. Second, the state-of-the-art extreme gradient boosting (XGBoost), which is insensitive to training data size, is adopted to build data-driven current stress models for the DAB converter. After that, differential evolution (DE) algorithm helps to choose modulation strategy and optimize modulation parameters for optimal current stress. Generally, the proposed AI-DT is developed in an automated fashion, which largely relieves manual computational complexity while exhibiting satisfactory modulation accuracy. The effectiveness of the proposed AI-DT approach has been experimentally verified with a 1kW hardware prototype, realizing optimal current stress under full operating conditions with more than 96.5% as peak efficiency.

Error-Triggered Adaptive Sparse Identification for Predictive Control and Its Application to Multiple Operating Conditions Processes.

With the digital transformation of process manufacturing, identifying the system model from process data and then applying to predictive control has become the most dominant approach in process control. However, the controlled plant often operates under changing operating conditions. What is more, there are often unknown operating conditions such as first appearance operating conditions, which make traditional predictive control methods based on identified model difficult to adapt to changing operating conditions. Moreover, the control accuracy is low during operating condition switching. To solve these problems, this article proposes an error-triggered adaptive sparse identification for predictive control (ETASI4PC) method. Specifically, an initial model is established based on sparse identification. Then, a prediction error-triggered mechanism is proposed to monitor operating condition changes in real time. Next, the previously identified model is updated with the fewest modifications by identifying parameter change, structural change, and combination of changes in the dynamical equations, thus achieving precise control to multiple operating conditions. Considering the problem of low control accuracy during the operating condition switching, a novel elastic feedback correction strategy is proposed to significantly improve the control accuracy in the transition period and ensure accurate control under full operating conditions. To verify the superiority of the proposed method, a numerical simulation case and a continuous stirred tank reactor (CSTR) case are designed. Compared with some state-of-the-art methods, the proposed method can rapidly adapt to frequent changes in operating conditions, and it can achieve real-time control effects even for unknown operating conditions such as first appearance operating conditions.

Experimental and modeling study of full- condition combustion characteristics of exhaust gas burners for solid oxide fuel cells

SOFC exhaust gas burner is an essential component of the SOFC thermal management system. The burner in the whole operating conditions of safe and stable combustion and smooth transition in the switching operating conditions is of great significance to the long-term and efficient operation of the SOFC power generation system. This paper builds an experimental test platform for the SOFC exhaust gas burner, tests the combustion performance of the burner in full operating conditions and operating conditions switching process, and establishes a prediction model for the combustion performance of SOFC exhaust gas burner by machine learning algorithm. The results show that the SOFC exhaust gas burner can meet the requirements of safe and stable combustion in all operating conditions and smooth transition between operating conditions; reliable combustion is achieved at the stable power generation condition with excess air factor of 7 ∼ 26, and the average cathode and anode pressure drop in the stable power generation condition is 142 Pa and 185 Pa, respectively; BP neural networks, support vector machines and random forests are used to establish a prediction model for the performance of the SOFC exhaust gas burner, and the random forests achieve the best prediction effect.

A novel deep learning architecture and its application in dynamic load monitoring of the vehicle system

In a complex vehicle system, the monitoring of dynamic load is difficult work. Therefore, a novel deep learning architecture based on a divide-and-conquer strategy is developed to construct the dynamic load monitoring model. The mathematical model of the vehicle system is established to provide data and theoretical support for the deep learning model. To solve the problems of long modeling time and poor accuracy under full operating conditions, the unsupervised Deep Temporal Clustering (DTC) method and supervised lightweight 3D convolutional neural network (3DCNN) are respectively used to identify operating conditions and construct local models. The test results show that the proposed deep learning model has high reliability, and the shape and amplitude of the predicted results of the dynamic load are basically consistent with the real results. Compared with the traditional model, the MAE index and MSE loss of the proposed model are smaller and have better performance.

Stability of hydropower units under full operating conditions considering nonlinear coupling of turbine characteristics

The nonlinearity of the hydro-turbine governing system (HTGS) will affect the regulation stability. This paper investigates the stability of HTGS considering the nonlinear coupling of hydro-turbine characteristics (NCTC) under full operating conditions. Firstly, the HTGS models considering the NCTC and power grid conditions are established. Then, the Hopf bifurcation analysis is carried out to comprehensively explain the mechanism of HTGS stability. Finally, the effect regularity of the NCTC on the stability of HTGS is summarized under the full operating conditions. The results indicate that the NCTC includes the coupling of nonlinear speed characteristic and linear head characteristic (NSHC), the coupling of nonlinear head characteristic and linear speed characteristic (NHSC), and the coupling of nonlinear head characteristic and linear opening characteristic (NHOC). The effect of NCTC on stability and dynamic response is mainly realized through the NHOC. The NHOC is favorable for stability under negative load disturbances and unfavorable for stability under positive load disturbances. A smaller unit inertia time constant or a greater flow inertia reduce the effect of NHOC on stable domain. The NHOC has different effects on stability under different operating conditions with a certain regularity. This paper provides potential support for optimizing control parameters of the HTGS.

Thermoeconomic performance of supercritical carbon dioxide Brayton cycle systems for CNG engine waste heat recovery

The supercritical carbon dioxide (S–CO2) Brayton cycle is a potential technology for recovering waste heat from compressed natural gas (CNG) engines. To choose suitable cycle configurations is important considering the complex operating characteristics of CNG engines. The traditional evaluation models of the S–CO2 cycles configuration are relatively one-sided without the comprehensive considerations of evaluation metrics. On the basis of the analytic hierarchy process and information entropy theory, this paper proposes a multi-dimensional comprehensive evaluation method for S–CO2 cycles from three perspectives: thermodynamic, economic, and environmental performance. A thermoeconomic performance optimization is conducted using a non-dominated genetic algorithm Ⅱ. Unlike most of the existing researches that use a single stable operating condition, this paper considers the full operating conditions of engines. Results show that the recompression cycle has the best overall performance among the investigated cycles with an exergy efficiency and electricity production cost of 67.20 % and 0.45 $/kWh, respectively. The efficiency improvement of the CNG engine coupled with the WHR system can reach up to 6.31 %. This study can provide a new reference for the comprehensive multidimensional evaluation and performance characteristics and optimal performance acquisition under full engine operating conditions of the S–CO2 cycle.

Study on the optimal solution recirculation ratio of liquid desiccant dehumidification system based on matching rate evaluation

The recirculation of dehumidification and regeneration solutions has shown promising moisture removal rates and energy-saving effects in liquid desiccant dehumidification systems. The different combinations of dehumidification solution recirculation ratio and regeneration solution recirculation ratio under varying operating conditions can have different impacts on the system. Nevertheless, the system performance under full operating conditions has not been comprehensively studied. To address this research gap, this study proposes a liquid desiccant dehumidification system with a solution recirculation structure, defines a new evaluation index — matching rate, and conducts a parameterized study and optimization of the steady-state performance of the liquid desiccant dehumidification system with a solution recirculation structure within the range of solution recirculation ratios under all operating conditions, by integrating the system cooling capacity and energy effectiveness ratio. The performance of the system is studied by varying seven key parameters: dehumidification solution recirculation ratio, regeneration solution recirculation ratio, total solution mass flow rate, dehumidification solution inlet temperature, regeneration solution inlet temperature, and ambient air temperature-humidity ratio. The results show that under design conditions, the solution recirculation ratio within the full operating range can process air to a range of 17.8℃～18.5℃ and 12.5 g/kg～17.4 g/kg. The maximum system cooling capacity, energy effectiveness ratio and matching rate values are 21.5 kW, 0.21 and 0.42 respectively. The recommended (dehumidification solution recirculation ratio, regeneration solution recirculation ratio) schemes for high, medium and low demand of system cooling capacity are (0.7～0.9, 0～0.5), (0.4～0.6, 0～0.7) and (0～0.3, 0.4～0.7) respectively, based on the requirements of system cooling capacity.

Design and experiment of a low-temperature charging preheating system for power battery packs with an integrated dissipative balancing function

The performance degradation of lithium-ion batteries (LiB) at low temperatures, as well as variability among batteries after battery grouping, limit the application range of electric vehicles (EVs). A low-temperature preheating method for power battery packs with an integrated dissipative balancing function is proposed in this research. The system builds its topology structure and sets the placement of heating units in the battery pack, as well as using heating units as preheating elements and balancing resistors. Furthermore, this paper presents an online balancing control technique to completely utilize the minimal capacity single battery module and formulates the balancing control threshold for different SOCnew states. The results showed that the temperature rise and temperature difference caused by the battery pack balancing process are within the range of 7 °C under the full operation conditions of balancing resistance at an ambient temperature of 20 °C. Under a −20 °C ambient temperature, the preheating system can raise the average temperature of the battery pack to 16 °C within 40 min and keep the temperature inside the battery pack above 0 °C for the following 4 h. This paper provides a new idea for coupling the research of efficient balancing technology and low-temperature preheating technology for power battery packs.

Investigation of Structural Strength and Fatigue Life of Rotor System of a Vertical Axial-Flow Pump under Full Operating Conditions

Axial-flow pumps consider both the conventional pump mode and the pump as turbine (PAT) mode operation and put forward higher requirements for long-term operation stability and structural strength; therefore, it is of great engineering significance to evaluate the structural strength and fatigue life of the rotor under full operating conditions. In this study, based on computational fluid dynamics and the one-way fluid-structure interaction algorithm, the structural strength and fatigue life of the rotor system of a large vertical axial-flow pump under full operating conditions were evaluated and studied. The results show that blade deformation and equivalent stress are generally higher in the PAT mode than in the pump mode. The maximum deformation in both modes occurs at the tip of the blade, while the area of stress concentration is at the root of the blade. Both the deformation and the equivalent stress increase with increasing flow rate. The minimum safety factor occurs at the blade root in both modes, and the safety factor in the PAT mode is relatively smaller than that in pump mode. Therefore, when designing and manufacturing axial flow pumps for turbine duties, priority should be given to material strength at the blade root during PAT mode operation to ensure safe and stable operation. The aim of this study is to provide technical references and theoretical foundations for evaluating the service cycle of axial-flow pumps and the influence on pump life under different operation modes.