Optimal Control Applications and Methods最新文献

Model predictive control with thermal constraints for fuel cell hybrid electric vehicle based on speed prediction 基于速度预测的燃料电池混合动力电动汽车热约束模型预测控制

Optimal Control Applications and Methods Pub Date : 2024-08-10 DOI: 10.1002/oca.3197

Jiangtao Fu, Bo Fan, Zhumu Fu, Shuzhong Song

{"title":"Model predictive control with thermal constraints for fuel cell hybrid electric vehicle based on speed prediction","authors":"Jiangtao Fu, Bo Fan, Zhumu Fu, Shuzhong Song","doi":"10.1002/oca.3197","DOIUrl":"https://doi.org/10.1002/oca.3197","url":null,"abstract":"Because of the soft dynamic performance of the fuel cell stack, the battery is usually integrated in the power system in fuel cell hybrid electric vehicles. In this article, a real time energy management strategy considering thermal constraints based on speed prediction with neuron network is proposed. The main principle of the proposed control strategy is to get the future power requirement with model predictive control based on the historic speed information, then optimize the objective, function according to the state variables. The objective function is set to minimize the equivalent fuel consumption of the vehicle and extend the life span of the fuel cell stack based on thermal constraints. Contrasting with the control strategy without thermal constraints under the World Light Vehicle Test Cycle driving cycle, the proposed energy management is 0.9% higher, but the temperature of the fuel cell stack and the battery can be limited within an appropriate range. The total equivalent fuel consumption is 3.9% lower than dynamic programming control strategy, which proves the availability of the proposed control strategy can reduce the equivalent fuel consumption while prolonging the fuel cell stack life span. Hardware in loop (HIL) experiment is implemented to testify the real time application of the proposed control strategy.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"15 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal iterative learning control under varying iteration lengths with input saturation 不同迭代长度下的最佳迭代学习控制与输入饱和度

Optimal Control Applications and Methods Pub Date : 2024-08-08 DOI: 10.1002/oca.3198

Mingchao You, Jie Shen, Liwei Li, Mouquan Shen

引用次数: 0

Optimizing solar photovoltaic and biomass integration for electric vehicle charging stations in metropolitan cities: A hybrid approach 优化大都市电动汽车充电站的太阳能光伏发电和生物质能集成：混合方法

Optimal Control Applications and Methods Pub Date : 2024-08-08 DOI: 10.1002/oca.3190

S. Udaiyakumar, G. Kannayeram, V. S. Hariharan, R. Saravanan

{"title":"Optimizing solar photovoltaic and biomass integration for electric vehicle charging stations in metropolitan cities: A hybrid approach","authors":"S. Udaiyakumar, G. Kannayeram, V. S. Hariharan, R. Saravanan","doi":"10.1002/oca.3190","DOIUrl":"https://doi.org/10.1002/oca.3190","url":null,"abstract":"This paper proposes a hybrid strategy for designing and optimizing a hybrid solar photovoltaic (PV) and biomass‐based electric vehicle charging station (EVCS) in metropolitan cities. The proposed strategy is the joint execution of the dung beetle optimizer (DBO) and Finite Basis Physics‐Informed Neural Networks Technique. It is hence called the DBO‐FBPINNs approach. The proposed strategy aims are to minimize initial cost and operating cost, net present cost, and levelized cost of energy. The design phase involves the energy storage systems, integration of solar PV panels, and biomass generators to warranty a reliable and continuous power supply for the EV charging infrastructure. Feasibility analysis encompasses various technical, economic, and environmental aspects. The converter's control signal is optimized via the DBO method. The FBPINNs model is used to forecast the optimal control parameters of the converter. By then, the proposed DBO‐FBPINNs method is implemented in the MATLAB platform and evaluated their performance with various present strategy's like deep neural network (DNN), fuzzy neural network (FNN), and recurrent neural network (RNN). When compared to other current technologies, the proposed strategy exhibits a low cost of $1.2.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"17 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust data‐driven dynamic optimization using a set‐based gradient estimator 使用基于集合的梯度估计器进行稳健的数据驱动动态优化

Optimal Control Applications and Methods Pub Date : 2024-06-12 DOI: 10.1002/oca.3157

Ali Kashani, Shirin Panahi, Ankush Chakrabarty, Claus Danielson

引用次数: 0

Delay effects on distributed constrained optimization over double‐integrator multi‐agent systems 双积分器多代理系统分布式约束优化的延迟效应

Optimal Control Applications and Methods Pub Date : 2024-06-10 DOI: 10.1002/oca.3145

Bingxue Shao, Minghui Lu, Xiasheng Shi, Lu Ren

引用次数: 0

Optimization of electric power prediction of a combined cycle power plant using innovative machine learning technique 利用创新的机器学习技术优化联合循环发电厂的电功率预测

Optimal Control Applications and Methods Pub Date : 2024-06-10 DOI: 10.1002/oca.3152

Efstratios L. Ntantis, Vasileios Xezonakis

{"title":"Optimization of electric power prediction of a combined cycle power plant using innovative machine learning technique","authors":"Efstratios L. Ntantis, Vasileios Xezonakis","doi":"10.1002/oca.3152","DOIUrl":"https://doi.org/10.1002/oca.3152","url":null,"abstract":"Accurate 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.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal pursuit strategies for multi‐pursuer single‐evader games based on cooperative tracking 基于合作追踪的多追求者单追求者博弈的最佳追求策略

Optimal Control Applications and Methods Pub Date : 2024-06-09 DOI: 10.1002/oca.3154

Yinglu Zhou, Yinya Li, A. Sheng, Guoqing Qi, Jinliang Cong

引用次数: 0

Torque fault compensation in electric vehicle switched reluctance motor drives: A jellyfish search optimization method 电动汽车开关磁阻电机驱动器中的扭矩故障补偿：水母搜索优化方法

Optimal Control Applications and Methods Pub Date : 2024-06-05 DOI: 10.1002/oca.3133

S. Anita, Y. Sukhi, Y. Jeyashree, N. Manoj Kumar

{"title":"Torque fault compensation in electric vehicle switched reluctance motor drives: A jellyfish search optimization method","authors":"S. Anita, Y. Sukhi, Y. Jeyashree, N. Manoj Kumar","doi":"10.1002/oca.3133","DOIUrl":"https://doi.org/10.1002/oca.3133","url":null,"abstract":"In this paper, an enhanced indirect instantaneous‐torque‐control is proposed based on the torque sharing function approach of switched reluctance motor drives for electric vehicles by employing the jelly fish search. The major goal is to attain vehicle desires that include minimal torque ripple, maximum torque per ampere (MTPA), and huge performance and extend speed limit. First, a simplest analytic design is developed a determine more proficient turn‐on angle for the torque product. Second, an altered torque sharing function (TSF) is used for compensating the faults of torque tracking. The proposed technique is calculated to represent an accurate switched reluctance motor and its magnetized features. They have worked to create the machine model and execute the necessary transmits. The torque fault is evaluated and compensated inside the torque sharing function. The adapting TSF is compensates for the torque fault with receiving the phase because it is the minimal flux rate connecting variation. Finally, the jellyfish search technique is accepted to determine the optimal control parameters. The proposed strategies are done in MATLAB and its performance is contrasted with different existing strategies. According to the simulation result, the proposed strategy‐based accuracy is 94.2% at 50 iteration and 80% at 100th iteration which is higher than the existing methods. From this analyses, it proved that the proposed technique gives superior performance to existing one.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"1 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141385080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing electric vehicle performance through buck‐boost converters with renewable energy integration using hybrid approach 采用混合方法，通过集成可再生能源的降压-升压转换器提高电动汽车性能

Optimal Control Applications and Methods Pub Date : 2024-06-05 DOI: 10.1002/oca.3153

A. Sakthivel, S. Ramesh, R. M. Das, F. T. Josh, U. A. Kumar, B. S. Mohan

{"title":"Enhancing electric vehicle performance through buck‐boost converters with renewable energy integration using hybrid approach","authors":"A. Sakthivel, S. Ramesh, R. M. Das, F. T. Josh, U. A. Kumar, B. S. Mohan","doi":"10.1002/oca.3153","DOIUrl":"https://doi.org/10.1002/oca.3153","url":null,"abstract":"The electrification of vehicles has emerged as a pivotal technique for addressing environmental concerns and reducing reliance on conventional fuel sources. Conversely, the best way to incorporate renewable energy into electric vehicles (EVs) is still a challenging task, particularly in enhancing the performance of EVs through efficient energy management. The transition to EVs has gained momentum as part of global efforts to mitigate environmental impacts and reduce dependence on fossil fuels. This paper proposes a hybrid method for enhancing EV performance through buck‐boost converters with renewable energy integration. The proposed technique is the joined execution of Flying Foxes Optimization (FFO) and Viscoelastic Constitutive Artificial Neural Networks (vCANNs) techniques. The proposed method's goal is to enhance the energy efficiency, minimize EV charging cost, and mitigating environmental impacts. The renewable energy sources: solar panels, fuel cells, and wind turbines, are integrated into the EV power system through buck‐boost converters. The buck‐boost converter's control signal is optimized through the FFO method. vCANNs are used to predict these control parameters. The proposed strategy is executed in MATLAB software and is compared with existing strategies. In comparison with other current approaches like particle swarm optimization, heap based optimizer, and wild horse optimize, the proposed method achieves a high efficiency of 99% and low cost of 0.05 €/KWh.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel predictive optimal control strategy for renewable penetrated interconnected power system 可再生能源渗透互联电力系统的新型预测优化控制策略

Optimal Control Applications and Methods Pub Date : 2024-05-15 DOI: 10.1002/oca.3144

Vineet Kumar, Veena Sharma, R. Naresh, Yogendra Arya

{"title":"A novel predictive optimal control strategy for renewable penetrated interconnected power system","authors":"Vineet Kumar, Veena Sharma, R. Naresh, Yogendra Arya","doi":"10.1002/oca.3144","DOIUrl":"https://doi.org/10.1002/oca.3144","url":null,"abstract":"The stable and efficient regulation of voltage and frequency is a critical issue associated with the functioning of modern power system network having renewable integration. This manuscript focuses on the concurrent regulation of voltage and frequency in a power network comprising multiple generating sources. Herein, along with conventional thermal and diesel plants, renewable generations from different sources have been considered such as wind and solar photovoltaic (PV) resources. Further, in a fresh attempt, the authors have modeled wind and solar PV generation using two different stochastic modeling methods concerning combined voltage and frequency loops in the regulated power system model. Furthermore, the novel Leader Harris Hawks Optimized Model Predictive Controller (MPC‐LHHO) has been applied to concurrently minimize the voltage and frequency deviations in the given power network. To address the intermittent nature of load and renewable generations, various auxiliary frequency controllers, such as the unified power flow controller (UPFC) and electric vehicle (EV) integration with the grid, have been implemented. The robustness of the proposed control method has been successfully validated through diverse scenarios of random loadings.","PeriodicalId":105945,"journal":{"name":"Optimal Control Applications and Methods","volume":"42 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0