New Energy and Energy Storage System

Submission Deadline: 31 May 2026 View: 268 Submit to Special Issue

Guest Editors

Prof. Shunli Wang

Email: wangshunli1985@126.com

Affiliation: College of Electric Power, Inner Mongolia University of Technology, Hohhot, 010080, China

Homepage:

Research Interests: energy management, energy storage, artificial neural networks, advanced machine learning, lithium battery

Prof. Guangchen Liu

Email: liugc@imut.edu.cn

Affiliation: College of Electric Power, Inner Mongolia University of Technology, Hohhot, 010080, China

Homepage:

Research Interests: new energy power generation

Prof. Zhongbao Wei

Email: weizb@bit.edu.cn

Affiliation: School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing, 100081, China

Homepage:

Research Interests: new energy vehicles, battery management, fuel cell system control, artificial intelligence theory and application, comprehensive control of energy systems

Prof. Carlos Fernandez

Email: c.fernandez@rgu.ac.uk

Affiliation: School of Pharmacy and Life Sciences, Robert Gordon University, Aberdeen, AB10 7GJ, United Kingdom

Homepage:

Research Interests: new energy vehicles, voltammetric sensing and electrochemical diagnostics, comprehensive control of energy storage and conversion

Prof. Cher Ming Tan

Email: cmtan@cgu.edu.tw

Affiliation: Department of Electronic Engineering, Chang Gung University, Taoyuan, 33302, Taiwan

Homepage:

Research Interests: lithium battery, reliability, energy storage, battery management

Summary

The rapid development of new energy and energy storage technologies is vital for building a green and low-carbon smart grid. While significant progress has been achieved, systematic solutions remain limited. In particular, current modeling and prediction theories cannot fully meet industrial demands, creating bottlenecks that restrict large-scale application and sustainable promotion.

This special issue focuses on advanced studies in green and low-carbon energy storage, aiming to enhance efficiency, stability, and scalability of smart grids. Researchers are encouraged to contribute innovative methods in system modeling, prediction, and optimization to overcome technical barriers and support industrial transformation.

Topics of interest include, but are not limited to:
·Green and Low-Carbon Energy Storage: Technologies for smart grid integration and sustainable development.
·Modeling & Prediction Theory: Advanced algorithms for system behavior and performance forecasting.
·Systematic Solutions: Frameworks addressing bottlenecks in industrial-scale applications.
·Smart Grid Applications: Integration of renewable generation and storage for enhanced reliability.
·Innovative Energy Storage Systems: Design, optimization, and next-generation deployment.
·Industrial Promotion & Sustainability: Strategies to accelerate commercialization and long-term adoption.

Keywords

Published Papers