Optimization Of Control Strategy Research Articles

Investigation on the temperature control performance and optimization strategy of a battery thermal management system combining phase change and liquid cooling

For ensuring the safety of lithium-ion batteries in application, keeping the temperature of the battery pack in the desired range is crucial under different operating condition. This paper incorporates experiments and the numerical model to design a novel thermal management system with the combination of the phase change material and liquid cooling for twelve cylindrical lithium-ion batteries. The experimental results reveal that at the ambient temperature of 35 °C, the maximum temperature and temperature difference of the blank control system during 1 C charge and 2 C discharge are 57.6 °C and 4.1 °C, while the maximum temperature difference is 3.6 °C with single liquid cooling. Compared with them, the maximum temperature of the coupled system is only 44.8 °C and the maximum temperature difference is less than 2 °C with superior cycling performance. Moreover, an electric-thermal model is proposed to study the cooling effect of coolant, from which controlling the coolant flow within 250 mL/min is the best choice. Based on these results, an optimization strategy of hierarchical management is proposed for the coolant flow and inlet temperature by monitoring the maximum temperature of the battery pack and ambient temperature. This strategy not only controls the temperature of the system in desired range under different ambient temperatures, but also reduces the unnecessary energy consumption of liquid cooling. The proposed system is scalable to be applied to the other types of batteries for thermal management.

Optimization of a Robust Modified Smith Predictor Control Strategy for Integrating Processes with Dead Time

The presence of an integrator and dead time in physical processes reduces stability and robustness. It limits the response time of a system. Integrating plus dead time (IPDT) processes provide oscillatory and slow response if the parameters of a system are not tuned properly despite dead time compensators (DTCs) being used. To overcome these shortcomings, the Smith predictor based sliding-mode control (SP-SMC) strategy using the Jaya optimization technique for IPDT processes is proposed in this study. For the selected populations, the cost function and best controller parameters are evaluated. The proposed strategy is compared with the typical Smith predictor-based proportional, integral and derivative (SP-PID), and conventional SP-SMC design methods. To evaluate the performance, integrating first-order with dead time (IFODT) process models with different controllability relationships (CR) is considered. Robustness analysis of the controller is carried out in this study for 30% parametric uncertainties and bounded disturbances. The simulation tests on a laboratory process (level) control system reveal the supremacy of the Jaya optimization algorithm over prevalent control strategies. Compared to SP-PID and SP-SMC, the proposed design method shows an improvement of 33.07% and 12.58% in settling time and an improvement of 19.73% and 22.93% in rise time with 0% overshoot, respectively. The applied setup elicits better multi-level set point tracking and disturbance rejection capabilities with the step input. Besides, the proposed algorithm shows better closed-loop performance for numerical simulations of Models 1 and 2.

Analytical Approximation-Based Approach for Passenger Flow Control Strategy in Oversaturated Urban Rail Transit Systems

Focusing on a heavily congested urban rail corridor, this study investigates the passenger flow control strategy optimization problem from a mesoscopic perspective to reduce platform congestion and enhance service quality. Based on a quadratic functional approximation for passenger arrival rates, an analytical formula for calculating passenger waiting time is derived based on the classic deterministic queueing theory. We formulate the problem as a continuous nonlinear programming model to minimize the total passenger waiting time within transportation capacity constraints. A Lagrangian relaxation approach effectively transforms the original complex problem into an unconstrained minimization program. The analytical solution relating to optimal flow control strategy is derived by directly solving the unconstrained program. To further provide an integrated optimization framework from both the supply and demand sides, we extend the abovementioned passenger flow control optimization model into an integrated mixed-integer nonlinear programming model to jointly optimize the passenger-flow control strategy and train frequency setting. Numerical examples are presented to demonstrate the applicability and effectiveness of the proposed models. The computational results show that the produced high-quality passenger flow control strategy significantly reduces total passenger delay.

A versatile volume-based modeling technique of distributed local quadratic convergence for aeroengines

For advanced aero-engine design and research, modeling and simulation in a digital environment is indispensable, especially for engines of complicated configurations, such as variable cycle engines (VCE) and adaptive cycle engines (ACE). Also, in the research of future smart engines, reliable real-time digital twins are paramount. However, the 2 dominant methods that used in solving the simulation models, Newton-Raphson (N-R) method and volume-based method, are not fully qualified for the study requirements, because neither of them reaches the satisfactory balance of convergence rate and calculating efficiency. In this study, by deeply analyzing the mathematical principle of these 2 methods, a novel modeling and solving method for aero-engine simulation, which integrates the advantages of both N-R and volume-based methods, is established. It has distributed architecture and local quadratic convergence rate. And a novel modeling method for variable area bypass injectors (VABI) is put forward. These facilitate simulation of various configurations of aero-engines. The modeling cases, including a high bypass-ratio (BPR) turbofan and an ACE, illustrate that the novel technique decreases the iterations by about two-thirds comparing with volume-based method, while the success rate of convergence remains over 99%. This proves its superiority in both convergence and calculating efficiency over the conventional ones. This technique can be used in advanced gas turbine engine design and control strategy optimization, and study of digital twins.

Resilience enhancement strategy for cyber–physical distribution systems that considers cross‐space propagation of information risk

AbstractCyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness.

Modeling and performance analysis of underwater gliders based on the virtual prototype technology

An underwater glider often comprises a buoyancy propulsion system, attitude regulation system, control system, hydrodynamic shape, etc. Numerous dynamic models based on rigid body dynamics have been established to study the motion performance of underwater gliders, which often neglect their attitude regulation and control systems. In this paper, a virtual prototype model of underwater gliders is developed based on the virtual prototype technology and verified by sea trials. Using the virtual model developed, the effects of three different control strategies and passive accumulators on underwater glider motion performance are discussed. The results demonstrate the feasibility of the virtual prototype model for motion performance analysis and control strategy optimization instead of the physical prototype. Thus, the proposed model could cut down the research and design time and reduce design costs.

Driving cycle prediction based on Markov chain combined with driving information mining

Driving cycle prediction (DCP) is of great importance for vehicle’s awareness of surrounding environment and optimization of control strategy. It has won much attention around the world. However, the used driving database could not reflecting the great diversity in multi driving cycles in real world. The characteristic parameters could not represent the complex and diverse driving conditions. Therefore, DCP models’ adaptability and generalization ability are limited. Moreover, prediction methods in existing researches always take large computation burden, difficult to apply in practice. To tackle these issues, the present research focus on DCP based on Markov chain combined with driving information mining (DIM). Firstly, many standard test cycles and real cycles are collected to construct an database of great diversity. Secondly, DIM technologies are studied to extract the optimal parameter set reflecting driving characteristics and determine the driving cycle categories. Thirdly, prediction method based on recursive self-learning Markov chain is proposed. Recursive equation of Markov state transition probability matrix (TPM) is deduced to save computation time. The key parameter of prediction method is adjusted to achieve optimal prediction performance. Synthesized standard test driving cycles (SSTDC) and real driving cycles (RDC) in database, as well as a new real driving cycle (NRDC) are tested. Driving pattern prediction accuracy of the proposed method is 99.25% for SSTDC, 100.00% for RDC, and 99.84% for NRDC, respectively. Compared with the benchmark method, the prediction accuracy is improved by 18.87% for SSTDC, 10.07% for RDC and 23.51% for NRDC, respectively. In addition, processing time of the prediction program is significantly reduced by 2.23 s (36.14%) for SSTDC, 12.30 s (51.25%) for RDC and 3.99 s (52.16%) for NRDC, respectively. It can be verified that the proposed method has high precision, short response time, as well as good adaptability and charming generalization ability for new cycles.

Analysis of primary frequency regulation characteristics of PV power plant considering communication delay

With the large-scale development of photovoltaic power generation, photovoltaic power plants (PVPP) are required to participate in primary frequency regulation to maintain the stability of the power system. Existing researches seldom consider the influence of the data communication delay of PVPP on the primary frequency regulation ability of PVPP. From the perspective of the communication system structure and control strategy of PVPP, this paper firstly analyzes the composition of the communication delay involved in the process of primary frequency regulation. Through the simulation of the three-machine nine-bus power system, the frequency regulation performance of PVPP under different time delays are analyzed. Furthermore, the influence mechanism of time delays on the primary frequency regulation results is discussed. Finally, two optimization schemes are proposed, which can reduce the communication delay and improve the tolerance of PVPP to the communication delay, respectively. The research results show that there are mainly two types of delays in the primary frequency regulation process of traditional large-scale PVPP. The longer the delays, the weaker the PVPP’s ability to participate in primary frequency regulation. In addition, the optimization of PVPP communication system and control strategy of inverters can help improve the frequency regulation ability of the PVPP, thereby maintaining the frequency stability of the power system.

Modeling, Simulation and Control Strategy Optimization of Fuel Cell Hybrid Electric Vehicle

This work represents the development of a Fuel Cell Hybrid Electric Vehicle (FCHEV) model, its validation, and the comparison of different control strategies based on the Toyota Mirai (1st generation) vehicle and its subsystems. The main investigated parameters are hydrogen consumption, and the variation of the state of charge, current, and voltage of the battery. The FCHEV model, which is made up of multiple subsystems, is developed and simulated in MATLAB® Simulink environment using a rule-based control strategy derived from the real system. The results of the model were validated using the experimental data obtained from the open-source Argonne National Laboratory (ANL) database. In the second part, the equivalent consumption minimization strategy is implemented into the controller logic to optimize the existing control strategy and investigate the difference in hydrogen consumption. It was found that the ECMS control strategy outperforms the rule-based one in all drive cycles by 0.4–15.6%. On the other hand, when compared to the real controller, ECMS performs worse for certain considered driving cycles and outperforms others.

Design and Parameter Optimization of Trajectory Correction Control Strategy for Air Duct Structure Projectile

In order to make the new air duct structure trajectory correction projectile have good dynamic correction control effect, the control strategy of the projectile’s correction mechanism is studied in this paper. A design method of trajectory correction control strategy based on particle swarm optimization-cuckoo search (PSO-CS) hybrid algorithm is proposed to obtain the optimal control parameters that can make the projectile flight stable and correct accurately. Firstly, the mathematical model of the air duct structure projectile is established. Secondly, the multiobjective optimization problem is analyzed. The projectile’s correction control strategy optimization model is established by taking the start control time, the number of corrections, the correction working time, and the interval time as the control variables. The optimization model innovatively considers the influence of the correction action on the flight stability of the projectile and the influence of the start control time on the correction range. Finally, the PSO-CS hybrid algorithm is used to design the calculation method of the optimization model and solve the optimal correction working parameters. The simulation results indicate that the control strategy optimization model can be solved by the proposed calculation method. Moreover, optimal correction working parameters of the correction mechanism in the current state can be obtained. Compared with the results of using single PSO algorithm and CS algorithm, the correction scheme calculated by PSO-CS hybrid optimization algorithm is better. This correction control scheme can effectively reduce the impact point deviation and make the projectile flight stable. At the same time, the circular error probable (CEP) of the projectile after correction is reduced from 42.3 m to 4.6 m while the impact point dispersion is lowered. The research results show that the design method of correction control strategy proposed in this paper is effective for trajectory correction of the new air duct structure projectile.

Photochemical Mechanism and Control Strategy Optimization for Summertime Ozone Pollution in an Industrial City in the North China Plain

To investigate the characteristics and formation mechanism of ozone (O3) pollution in an industrial city, an extensive one-month field campaign focusing on O3 and its precursors (e.g., volatile organic compounds[VOC] and nitrogen oxides[NOx]) was conducted in Zibo City, a highly industrializd city in the North China Plain, in June 2021. The 0-D box model incorporating the latest explicit chemical mechanism (MCMv3.3.1) was applied using an observation dataset (e.g., VOC, NOx, HONO, and PAN) as model contraints to explore the optimal reduction strategy for O3 and its precursors. The results showed that ① during high-O3 episodes, stagnant weather conditions with high temperature and solar radiation as well as low relative humidity were observed, and oxygenated VOCs and alkenes from anthropogenic VOCs contributed the most to the total ozone formation potential and OH reactivity (k·OH). ② The in-situ O3 variation was primarily affected by local photochemical production and export process horizontal to downwind areas or vertical to the upper layer. The reduction in local emissions was essential to alleviate O3 pollution in this region. ③ During high-O3 episodes, high concentrations of ·OH (10×106 cm-3) and HO2· (14×108 cm-3) radical drove and generated a high O3 production rate (daytime peak value reached 36×10-9 h-1). The reaction pathways of HO2·+NO and ·OH+NO2 contributed the most to the in-situ gross Ox photochemical production (63%) and photochemical destruction (50%), respectively. ④ Compared to those during low-O3 episodes, the photochemical regimes during high-O3 episodes were more inclined to be considered as the NOx-limited regime. Detailed mechanism modeling based on multiple scenarios further suggested that the synergic emission reduction strategy of NOx and VOC, while focusing on NOx emission alleviation, would be practical options for controlling local O3 pollutions. This method could also provide policy-related guidance for the precise O3 pollution prevention and control in other industrialized Chinese cities.

Optimization Reduces Knee-Joint Forces During Walking and Squatting: Validating the Inverse Dynamics Approach for Full Body Movements on Instrumented Knee Prostheses.

Because of the redundancy of our motor system, movements can be performed in many ways. While multiple motor control strategies can all lead to the desired behavior, they result in different joint and muscle forces. This creates opportunities to explore this redundancy, for example, for pain avoidance or reducing the risk of further injury. To assess the effect of different motor control optimization strategies, a direct measurement of muscle and joint forces is desirable, but problematic for medical and ethical reasons. Computational modeling might provide a solution by calculating approximations of these forces. In this study, we used a full-body computational musculoskeletal model to (a)predict forces measured in knee prostheses during walking and squatting and (b)study the effect of different motor control strategies (i.e.,minimizing joint force vs. muscle activation) on the joint load and prediction error. We found that musculoskeletal models can accurately predict knee joint forces with a root mean squared error of <0.5 body weight (BW) in the superior direction and about 0.1BW in the medial and anterior directions. Generally, minimization of joint forces produced the best predictions. Furthermore, minimizing muscle activation resulted in maximum knee forces of about 4BW for walking and 2.5BW for squatting. Minimizing joint forces resulted in maximum knee forces of 2.25BW and 2.12BW, that is, a reduction of 44% and 15%, respectively. Thus, changing the muscular coordination strategy can strongly affect knee joint forces. Patients with a knee prosthesis may adapt their neuromuscular activation to reduce joint forces during locomotion.

Design of Rural Road Lighting System Based on Internet of Things and Deep Learning

With the promotion of China's new rural construction and beautiful countryside movements, there is a growing demand to improve the living environment of residents in rural areas and meet the requirements for energy efficiency and sustainability by implementing novel technologies. The internet of things (IOT) that allows real-time monitoring, measurement, and control while ensuring sustainability has become the trend in the field of lighting management. On the other hand, deep learning algorithms could help to eliminate the labor-intensive and time-consuming mining and feature extraction of high-dimensional complex data, and provide analysis and optimization of control strategies in a much shorter time. In this study, we put forward a new lighting design model by integrating IOT and deep learning, and theoretically analyzed its feasibility and discussed its advantages, with the goal to provide a new scheme for the design of rural road lighting system that is energy-efficient with improved reliability, robust functionality and satisfying performance.

Influence of Different Data Interpolation Methods for Sparse Data on the Construction Accuracy of Electric Bus Driving Cycle

Battery electric vehicles (BEVs) are one of the most promising new energy models for industrialization and marketization at this stage, which is an important way to solve the current urban haze air pollution, high fuel cost and sustainable development of the automobile industry. This paper takes pure electric buses as the research object and relies on the operation information management platform of new energy buses in Nantong city to propose an electric bus cycle construction method based on the mixed interpolation method to process sparse data. Three different interpolation methods, linear interpolation, step interpolation and mixed interpolation, were used to preprocess the collected data. The principal component analysis method and K-means clustering algorithm were used to reduce and classify the eigen parameter matrix. According to the clustering results, different categories of moving section and idle section libraries were established. According to the length of time and the correlation among various types, several moving sections and idle sections were selected to form a representative driving cycle of Nantong city buses. The results show that the mixed interpolation method, based on linear interpolation and cubic spline interpolation, has a good processing effect. The average relative error between the synthesized working conditions and the measured data are 15.71%, and the relative error of the seven characteristic parameters is less than 10%, which meets the development requirements. In addition, the comparison and analysis with the characteristic parameters of the world typical cycle conditions (NEDC, WLTC) show that the constructed cycle conditions of Nantong city are reasonable and reliable to represent the driving conditions of pure electric buses in Nantong city, which can provide a reference for the optimization of the bus energy control strategy.

A Novel Power Injection Priority Optimization Strategy for Voltage Support Control of PMSG-based Wind Farm

This article presents a novel power injection priority optimization-based control strategy for a PMSG-based wind farm. The proposed strategy seeks to provide voltage support while mitigating the frequency excursion resulting from voltage dip during the fault. To achieve this objective, the conventional fixed reactive power injection priority plan is replaced by an adaptive power injection priority plan in the proposed method. The control problem is formulated as a multi-objective function optimization problem, where the power injection priority plan is calculated by minimizing the shortfall of WF active power and maximizing the reactive power injection of WF. During the fault period, the optimized result is directly applied to the outer control of machine-side converter and grid-side converter to regulate the active/reactive power generation. The simulation system model and proposed optimal model are built in EMTDC/PSCAD and MATLAB, respectively, to verify the effectiveness of the proposed strategy. Test results demonstrate that the proposed control strategy is applicable to mitigate frequency excursion while complying with the voltage support requirement during the fault.

Reachability Analysis Plus Satisfiability Modulo Theories: An Adversary-Proof Control Method for Connected and Autonomous Vehicles

Connected and autonomous vehicles (CAVs) are expected to operate with safety guarantee in presence of adversaries from the Internet of Vehicles. This article proposes a control method named reachability analysis plus satisfiability modulo theories (RA-SMT) for CAVs against integrity attacks caused by bounded adversary. This method enables vehicles to possess the reach-avoid specification and strict control safety ensurance even in the worst case scenario. The introduction of state-feedback control decomposes the original complex problem into more manageable reachability analysis and adversary-free control strategy optimization. Precisely, zonotope sets are employed for reachability analysis, and the control strategy is optimally solved and verified simultaneously via SMT. This method is applicable to complex traffic scenarios with the help of SMT, which can describe various constraints flexibly and conveniently. Simulation results reveal the effectiveness and safety of the proposed method in the classical car-following scenario against bounded adversary under various conditions. Particularly, RA-SMT exhibits significantly improved control performance (around 16%) and computation efficiency (around 63%) compared with existing methods. Finally, the RA-SMT is implemented on an autonomous driving platform to validate its practicability.

Research on optimization of human-machine interaction control strategy for exoskeleton based on impedance control

Research on optimization of human-machine interaction control strategy for exoskeleton based on impedance control

Deep Reinforcement Learning Algorithm Based on Fusion Optimization for Fuel Cell Gas Supply System Control

In a proton exchange membrane fuel cell (PEMFC) system, the flow of air and hydrogen is the main factor affecting the output characteristics of the PEMFC, and there is a coordination problem in the flow control of both. To ensure real-time gas supply in the fuel cell and improve the output power and economic benefits of the system, a deep reinforcement learning controller with continuous state based on fusion optimization (FO-DDPG) and a control optimization strategy based on net power optimization are proposed in this paper, and the effects of whether the two gas controls are decoupled or not are compared. The experimental results show that the undecoupled FO-DDPG algorithm has a faster dynamic response and more stable static performance compared to the fuzzy PID, DQN, traditional DRL algorithm, and decoupled controllers, demonstrated by a dynamic response time of 0.15 s, an overshoot of less than 5%, and a steady-state error of 0.00003.

Traffic control optimization strategy based on license plate recognition data

Traffic control optimization strategy based on license plate recognition data

Extending the application range of MMCs in high-performance drives: limiting issues and new proposals

Modular Multilevel Converters (MMCs) are becoming more and more popular in medium-voltage applications thanks to several interesting features. The major limitation for applications with variable speed drives (VSDs) is the submodule capacitors’ voltage ripple, which depends directly on the output current/torque and inversely on the output frequency/speed. Among all the hybrid MMC topologies addressed in recent literature, the Flying-Capacitor Passive Cross-Connected arms (FC-PCC) MMC seems one of the most promising to cope with this issue. This article provides a comprehensive overview of its features and capabilities. The main open issues limiting safe and reliable converter operation in VSD applications are addressed, and novel solutions are presented, such as the optimization of the converter digital control strategy, the distributed implementation of the architecture, the L‑C oscillations damping during the pre-charge/start-up of the converter, and the optimized arm inductor design based on magnetic coupling concepts. The effectiveness of all the proposals is demonstrated through an accurate simulation of a 22 kW Permanent Magnet Synchronous Machine (PMSM) drive, including a complete model of the effects of the real-time communication and the processing latency. A prototype of the system has been developed employing some known and novel architectural solutions, as discussed in the last section of the paper.