Regional differences in China's electric vehicle sales forecasting: Under supply-demand policy scenarios

Liu, Bingchun; Song, Chengyuan; Liang, Xiaoqin; Lai, Mingzhao; Yu, Zhecheng; Ji, Jie

doi:10.1016/j.enpol.2023.113554

Cited by 20 publications

(6 citation statements)

References 42 publications

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“…These models can forecast demand with remarkable precision, aiding manufacturers in making informed decisions about production volumes and inventory management. Machine learning algorithms, such as time-series analysis, regression, and neural networks, can be trained on historical sales data to identify patterns and trends, enabling accurate predictions for future demand (Liu et al, 2023). AI can continually learn from prior forecasting inaccuracies and modify its models accordingly, resulting in progressively enhanced accuracy over time.…”

Section: Engineering Management In Production and Servicesmentioning

confidence: 99%

Generative AI in the Manufacturing Process: Theoretical Considerations

Doanh,

Dufek,

Ejdys

et al. 2023

Engineering Management in Production and Services

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The paper aims to identify how digital transformation and Generative Artificial Intelligence (GAI), in particular, affect the manufacturing processes. Several dimensions of the Industry 4.0 field have been considered, such as the design of new products, workforce and skill optimisation, enhancing quality control, predictive maintenance, demand forecasting, and marketing strategy. The paper adopts qualitative research based on a critical review approach. It provides evidence of the GAI technology support in the mentioned areas. Appropriate use of emerging technology allows managers to transform manufacturing by optimising processes, improving product design, enhancing quality control, and contributing to overall efficiency and innovation in the industry. Simultaneously, GAI technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks, improve a marketing strategy and identify market trends.

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Section: Engineering Management In Production and Servicesmentioning

confidence: 99%

Generative AI in the Manufacturing Process: Theoretical Considerations

Doanh,

Dufek,

Ejdys

et al. 2023

Engineering Management in Production and Services

View full text Add to dashboard Cite

show abstract

“…In recent years, the uneven distribution of renewable energy, , as well as the development of new energy vehicles, has increased the demand for batteries. Lithium-ion batteries are currently the most studied and most widely used commercially. , However, lithium reserves may not suffice to meet the rapidly growing demand.…”

Section: Introductionmentioning

confidence: 99%

Electrospun Core–Shell Carbon Nanofibers as Free-Standing Anode Materials for Sodium-Ion Batteries

Li,

Pei,

et al. 2024

ACS Appl. Nano Mater.

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The development of wearable devices requires flexible batteries that can be bent and folded. However, deficiencies in material flexibility, conductivity, and other aspects can affect the performance of a flexible electrode. One-dimensional nanofibers possess high specific surface area, high conductivity, and a 3D network structure that enables them to buffer stress and strain. Consequently, they hold significant potential in the field of electrochemical energy storage as flexible electrode materials. In this study, we utilized polyvinylpyrrolidone (PVP) as a template to form a supramolecular polymer (PNDS) through hydrogen bonding between 1,4,5,8-naphthalene tetracarboxylic acid (NTCA) and 3,3′-diaminobenzidine (DAB) monomers. Through core−shell electrospinning and carbonization, PNDS precursors were used to prepare polybis(benzimidazobenzophenanthroline-dione) (BBB)-based carbon nanofibers featuring a core−shell structure. The BBB polymer, featuring a continuous aromatic ring structure, undergoes conversion into a highly graphitic carbon skeleton upon carbonization at 1000 °C. This transformation enhances the conductivity of flexible electrodes, improves the current collection effect under high currents, and ensures stability in the charge−discharge cycle. The iron-containing polymers within the shell layer ultimately transform into iron oxide and iron carbide nanoparticles encapsulated within the carbon fibers, compensating for the lower specific capacity characteristic of pure carbon materials. Serving as a SIB flexible anode, the specific capacity can achieve 250 mAh g −1 after 950 cycles at 0.2 A g −1 , with negligible attenuation throughout the cycling process. This study demonstrates that BBB-based carbon nanofibers featuring core−shell structures exhibit excellent electrochemical performance while retaining flexibility, presenting clear advantages over traditional electrodes characterized by complicated processes and limited active substance content.

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“…Xia et al [9] combines grey correlation analysis with multiobjective optimization problems. Bingchun Liu [10] proposes a multi-factor prediction model integrating GRA, discrete wavelet transform (DWT), and bidirectional long short-term memory, studies the incentive effect of policies on the electric vehicle market under different regional characteristics. Dalal Aassouli [11] et al used grey correlation analysis to make a comparative analysis of policy formulation and implementation of food security governance.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective capacity estimation of wind ‐ solar ‐ energy storage in power grid planning consideration policy effect

Huan,

Zhang,

et al. 2024

IET Generation Trans & Dist

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Numerous policies have been implemented to advance the growth of renewable energy. Nonetheless, certain policies have not yielded the anticipated impact on the progression of renewable energy development. In order to maximize the promotion effect of renewable energy policies, this study proposes a capacity allocation optimization method of wind power generation, solar power and energy storage in power grid planning under different policy objectives. First, based on the policy quantification, grey relation analysis (GRA) is used to calculate the correlation degree of the policy indicators on the planning capacity of renewable energy. Further, a multi‐objective capacity estimation model for wind, solar and energy storage is comprehensively presented. Some highly correlated policy indicators are transformed into the special constraints. And the economy and the stability of the power grid are integrated as the objective function. Meanwhile, the carbon trading and punishment for wind power and solar power abandonment are considered. Finally, the proposed model is solved by NSGA‐II‐PSO (particle swarm optimization) algorithm. The novelty of the algorithm is that the crossover operation of NSGA‐II is replaced by the position updating of particle swarm. The calculation result of the case study can effectively evaluate the optimal planning capacity of renewable energy under different policies, while ensuring the economic and the stability of the power system. The study can provide the reasonable basis and the valid analytical method for the policy formulation and the renewable energy development.

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Regional differences in China's electric vehicle sales forecasting: Under supply-demand policy scenarios

Cited by 20 publications

References 42 publications

Generative AI in the Manufacturing Process: Theoretical Considerations

Generative AI in the Manufacturing Process: Theoretical Considerations

Electrospun Core–Shell Carbon Nanofibers as Free-Standing Anode Materials for Sodium-Ion Batteries

Multi‐objective capacity estimation of wind ‐ solar ‐ energy storage in power grid planning consideration policy effect

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