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Intelligent energy management and operation efficiency of electric vehicles based on artificial intelligence algorithms and thermal energy optimization

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2024年 55卷

作者： Wang, Ying Shandong Inst Commerce & Technol Coll Digital Mkt Ind Jinan 250103 Shandong Peoples R China

As the global concern for environmental protection and sustainable development intensifies, electric vehicles (EVs), as a representative of clean transportation, are gradually receiving attention. This study aims to explore an intelligent energy management system for electric vehicles based on artificial intelligence algorithms, and focuses on optimizing heat energy utilization to improve the overall operating efficiency of electric vehicles. This paper uses machine learning and deep learning technologies to build intelligent decision-making models, and realizes dynamic optimization of energy demand and heat recovery by analyzing real-time data of electric vehicles under different working conditions. At the same time, a set of thermal management system is designed, which combines the driving state and environmental conditions of the vehicle to optimize the thermal management strategy of the battery and the motor. The experimental results show that the intelligent energy management system based on this research shows good adaptability and stability under different climate conditions, and significantly reduces energy consumption. Therefore, the intelligent energy management system of electric vehicles based on artificial intelligence algorithm and thermal energy optimization effectively improves the operating efficiency of electric vehicles.

关键词： artificial intelligence algorithms Electric vehicles Energy management Operational efficiency

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An empirical analysis of applications of artificial intelligence algorithms in wind power technology innovation during 1980-2017

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JOURNAL OF CLEANER PRODUCTION 2021年 297卷 126536-126536页

作者： Lee, Mekyung He, Gang Korea Univ Grad Sch Energy & Environm GETPPP Seoul South Korea SUNY Stony Brook Dept Technol & Soc Stony Brook NY 11794 USA

We investigated the applications of artificial intelligence (AI) algorithms in wind power technology changes over time and found that AI accelerates the automation of wind power systems. This study shows evidence of the evolution of wind technology innovation following the advancement in AI algorithms using the patents data issued in four intellectual property (IP) offices from 1980 through 2017. artificial intelligence and more advanced data analytics can be effectively applied to increase the efficiency of wind power systems and to optimize wind farm operations. This study empirically analyzes the evolution of applications of AI algorithms in wind power technology by employing machine learning-based text mining and network analysis, demonstrating the dynamic changing pattern of applications of AI algorithms in wind power technology innovation. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Wind power technology artificial intelligence algorithms Machine learning Technology innovation Patent data analysis

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Investigation on the coordinated operation control technology of distributed new energy grid connection using artificial intelligence algorithms

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Lu, Yonglin Sun, Jinyong Hu, Jinying Chen, Rui Liao, Yifa Shang, Jingrui Yunnan Power Grid Co Ltd Dehong Power Supply Bur Dehong Yunnan Peoples R China

New energy power generation has strong randomness and volatility. Especially in the case of a high proportion of NE (network security) power generation, its sudden random power generation in a short period of time will seriously affect the stable operation of the power grid. Therefore, this paper proposes BP neural network algorithm to study the distributed NE grid-connected cooperative operation control technology. First of all, this paper studies the artificial intelligence algorithm in detail and applies it to the coordinated operation control of distributed NE grid-connected;then, based on the status quo of renewable energy PG (power generation), this paper establishes a suitable wind speed time series model, and thus proposes an optimization model based on a rolling scheduling optimization algorithm. The experimental results show that the average running time of the rolling scheduling optimization algorithm is maintained at about 0.2 s, which can effectively realize online operation. In addition, through rolling adjustment, the error between the total output curve of the unit and the actual total output curve of the unit can be significantly reduced. The research shows that the rolling scheduling optimization algorithm has a good optimization effect, can promote the coordinated development of wind farms and power systems, and increase the capacity of power systems.

关键词： distributed new energy coordinated operation control technology artificial intelligence algorithms coordinated development

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Indoor Visible Light Positioning System Based on Point Classification Using artificial intelligence algorithms

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SENSORS 2023年第11期23卷 5224页

作者： Long, Qianqian Zhang, Junyi Cao, Lu Wang, Wenrui Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

In RSSI-based indoor visible light positioning systems, when only RSSI is used for trilateral positioning, the receiver height needs to be known to calculate distance. Meanwhile, the positioning accuracy is greatly affected by multi-path effect interference, with the influence of the multi-path effect varying across different areas of the room. If only one single processing is used for positioning, the positioning error in the edge area will increase sharply. In order to address these problems, this paper proposes a new positioning scheme, which uses artificial intelligence algorithms for point classification. Firstly, height estimation is performed according to the received power data structure from different LEDs, which effectively extends the traditional RSSI trilateral positioning from 2D to 3D. The location points in the room are then divided into three categories: ordinary points, edge points and blind points, and corresponding models are used to process different types of points, respectively, to reduce the influence of the multi-path effect. Next, processed received power data are used in the trilateral positioning method for calculating the location point coordinates, and to reduce the room edge corner positioning error, so as to reduce the indoor average positioning error. Finally, a complete system is built in an experimental simulation to verify the effectiveness of the proposed schemes, which are shown to achieve centimeter-level positioning accuracy.

关键词： visible light communication indoor positioning artificial intelligence algorithms point classification

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Optimization of computer programming based on mathematical models of artificial intelligence algorithms

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COMPUTERS & ELECTRICAL ENGINEERING 2023年第1期110卷

作者： Zheng, Yuhui Zhongyuan Univ Technol Coll Sci Zhengzhou 450007 Henan Peoples R China

artificial intelligence (AI) technology is a remarkable combination of computer science, biotechnology, and other disciplines. However, due to the late start of research on AI algorithms, the lack of practical experience, and an incomplete theoretical system, there are still several issues that need to be solved and improved upon further. This article uses the mathematical model of AI algorithms to optimize computer programing. The importance and practical role of mathematical model-optimization methods and mathematical algorithms based on AI algorithms in computer programing optimization are highlighted. The results show that optimizing computer programing based on mathematical models of AI algorithms can effectively improve the organization and hierarchy of code programing, thus improving the programing efficiency of the code. The application of mathematical models of AI algorithms improved the quality of computer programing, computer programing was optimized, and the utilization of programing space was increased by 5.75%.

关键词： Computer programming optimization Mathematical models artificial intelligence algorithms Computer and electrical engineering Objective function artificial neural networks Distributed system Pattern recognition

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ETHICAL EVALUATION AND OPTIMIZATION OF artificial intelligence algorithms BASED ON SELF SUPERVISED LEARNING

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 3594-3602页

作者： Deng, Ruoyu Zhao, Yang Chengdu Technol Univ Sch Marxism Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China

Active learning solves the problem of requiring a large amount of manpower and resources due to the large size of training samples. The core problem is how to select valuable samples to reduce annotation costs. Using neural networks as classifiers, most methods choose samples with large amounts of information without considering the issue of information redundancy between the selected samples. Through the study of redundancy issues, the author proposes a sample selection optimization method to reduce information redundancy. Using uncertainty methods to select samples with high information content to form a candidate sample set, and using latent variable vectors calculated in the network to represent sample information, the cosine distance between candidate samples is calculated using this vector to select subsets with large interval distance and low information redundancy. Compared with several uncertainty methods in the Mnist, Fashion mnist, and Cifar-10 datasets, this method reduces labeled samples by a minimum of 11% with the same sample accuracy. The higher the dimensionality of the feature vector calculated by CNN, the more candidate samples it contains, and the more information it contains. After being improved by self supervised learning algorithms, the effect becomes more significant. The more candidate samples are selected, the stronger the information redundancy. The better the performance of self supervised learning algorithms.

关键词： artificial intelligence algorithms Information redundancy Cosine distance Uncertainty method

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Application of magnetic resonance imaging and artificial intelligence algorithms in cancer screening

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SLAS TECHNOLOGY 2024年第6期29卷 100218页

作者： Guo, Jian Xue, Yu Shanghai Pudong Hosp Dept Gen Surg Shanghai 201300 Peoples R China

In this society with a high incidence of cancer, cancer screening has become an important method to reduce the incidence and mortality of cancer. Traditional cancer screening methods such as CT have certain limitations and are difficult to adapt to large-scale and periodic cancer screening scenarios. Magnetic resonance imaging technology is an effective auxiliary method in CT methods, which can achieve high image resolution at lower doses and lower costs. Therefore, magnetic resonance imaging has become the most popular imaging method in clinical practice and a key research direction in the field of medical imaging. Therefore, this article intends to conduct indepth research on the application of image feature extraction based on magnetic resonance imaging and artificial intelligence algorithms in cancer screening. This article introduces particle swarm optimization algorithm into the learning of artificial intelligence models and further improves it. And compared multiple algorithms, such as Chaos Particle Swarm Optimization, Genetic Particle Swarm Optimization, and Grey Wolf Algorithm, in order to verify the effectiveness and feasibility of the algorithm proposed in this paper. On this basis, the intelligent optimization algorithm was further improved and validated. Experimental results have shown that the new method proposed in this article has strong fault tolerance, and various functional modules of the cancer screening management system have been optimized and designed from five aspects: front-end, back-end, external, database, and infrastructure.

关键词： Magnetic resonance imaging Imaging images artificial intelligence algorithms Cancer screening

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Determinants and Pathways for Inclusive Growth in China: Investigation Based on artificial intelligence (AI) Algorithm

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COMPUTATIONAL ECONOMICS 2025年第3期65卷 1231-1264页

作者： Fan, Shuangshuang Li, Yichao Mbanyele, William Lai, Xiufeng Wenzhou Univ Technol Sch Econ & Management Wenzhou 325035 Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Shandong Univ Ctr Econ Res Jinan 250100 Peoples R China China Univ Min & Technol Beijing Sch Management Beijing 100091 Peoples R China

Can artificial intelligence (AI) algorithms help policymakers in their decisions on inclusive growth? In this study, we introduce artificial intelligence algorithms to calibrate China's inclusive growth determinants. We uncover various factors that significantly influence inclusive growth using machine learning forecasts. Furthermore, our results using best practice methods outperform findings from traditional regression-based strategies on other dimensions, which miss non-linear interactions in their estimations. However, we observe that when the actual value of the inclusive growth index is too large, the accuracy of the machine learning model is diminished. Meanwhile, the results of heterogeneity analysis reveal that the determinants of inclusive growth in cities with different region and different marketization level are distinct. In addition, we adopt the scenario simulation and prediction approach to reveal the best policy measure to promote inclusive growth in China. Our findings indicate that machine learning holds promise for understanding how inclusive growth can be achieved and can assist real-world economies in enhancing inclusive growth.

关键词： Inclusive growth Machine learning artificial intelligence algorithms Influencing factors China

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artificial intelligence-driven optimization of V2G and charging point selection en-route: A systematic literature review

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ENERGY CONVERSION AND MANAGEMENT-X 2025年 26卷

作者： Sommer, Christoph Hossain, M. J. Univ Technol Sydney Sch Elect & Data Engn POB 123 Ultimo NSW 2007 Australia

The common implementation of electric vehicles (EVs) necessitates effective integration strategies to ensure their seamless interaction with the electrical grid while meeting private EV customer preferences and enhancing grid stability. This review aims to enhance comprehension of both industrial and academic applications of vehicle-togrid (V2G) technology, with a specific emphasis on maximizing profits for private EV owners seeking to engage with this technology. Additionally, it provides an overview of prevalent machine learning (ML) algorithms suitable for addressing V2G-related challenges, along with discussions on predicting EV owners' charging behavoir and challenges associated with Electric Vehicle Routing Problems (EVRP). Notably, this review highlights the distinctive requirements and characteristics of the Australian consumer base and grid infrastructure. It underscores a notable gap in understanding how private customers utilizing V2G-capable EVs can maximize benefits, particularly given Australia's significant share of photovoltaic (PV) energy generation and its unique EVRP layout necessitating longer travel distances between major urban centres. Further investigation is warranted into the impact of high PV generation shares on charging behavoir and the optimization of charging strategies within the Australian context. For the industry, the primary advantage of this paper lies in its examination of existing profit maximization models and the consequent heightened interest from customers in this technology plus the strategic placement of charging stations. For academic researchers, this paper demonstrates that the prediction of charging behavoir can be effectively tackled through the appropriate selection of MLalgorithms.

关键词： Electric Vehicles Vehicle-to-grid Charging infrastructure Bidirectional charging Machine learning artificial intelligence algorithms

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Quantum-inspired metaheuristic algorithms for Industry 4.0: A scientometric analysis

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ENGINEERING APPLICATIONS OF artificial intelligence 2025年 139卷

作者： Pooja Sood, Sandeep Kumar Natl Inst Technol Kurukshetra Dept Comp Applicat Kurukshetra 136119 Haryana India

Quantum-inspired Metaheuristic algorithms have redefined non-deterministic polynomial time hard optimization challenges by leveraging quantum mechanics principles. These algorithms herald a broad range of application scenarios in Industry 4.0 and offer feasible time solutions for complex, large-scale industrial landscapes. The potential benefits provided by the quantum-inspired metaheuristic algorithms have accelerated the scientific advancements in this domain. Consequently, the present research contributes to the existing knowledge base by presenting the intellectual landscape through scientometric and systematic literature analysis. The study is conducted on the dataset derived from the Scopus and Web of Science databases, covering 2001 to 2023. The study employs co-citation and co-occurrence analyses to discern prominent research topics, emerging research frontiers, significant authors, and the most collaborating countries. The research findings underscore that electric vehicles, energy efficiency, and combinatorial optimization are prominent research topics, while carbon emission, resource management, and path planning are burgeoning areas of exploration in this knowledge domain. The intricate and entangled network linkage determines that the research community in this domain fosters a dynamic and synergistic relationship. Overall, the pivotal insights and the research challenges articulated in this article offer valuable insights to researchers and the academic community, aiding in discerning the intellectual terrain and emerging research patterns in quantum-inspired metaheuristic algorithms. This, in turn, fosters the advancement of innovation and facilitates well-informed decision-making within this evolving research paradigm.

关键词： Quantum-inspired Large-scale industrial optimization Non-deterministic polynomial time hard optimization Quantum computing artificial intelligence algorithms

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