Machine learning provides an efficient pathway toward a more economical and reliable low-carbon energy transition. It improves generation and load forecasting, accelerates the discovery of next-generation battery chemistries, enhances the coordination of distributed energy resources, and advances battery management systems.

Beyond stationary infrastructure, energy is increasingly a first-class concern for embodied intelligent systems — such as mobile robots, autonomous vehicles, drones, and smart industrial agents — that sense, compute, and act under tight and time-varying energy budgets. Machine learning offers powerful tools for the intelligent energy management of such agents, including energy-optimal control, energy-aware computation and load scheduling, and the modeling, diagnosis, and management of their onboard power sources.

The purpose of this special issue is to provide an overview of the state of the art, to present new research results, and to discuss promising future research directions at the interface between energy, machine learning, and embodied intelligence.

The scope includes, but is not limited to:

• Machine learning and reinforcement learning for distributed optimization and control of large-scale energy systems;
• Physics-informed machine learning for Energy system optimization;
• Machine learning for battery management systems;
• Energy management for embodied intelligent systems, such as mobile robots, autonomous vehicles, drones, and smart industrial agents;
• Energy-aware load computing and power-, energy-, and thermal-aware scheduling of onboard workloads for resource-constrained embodied platforms;
• Reinforcement learning and physics-informed methods for energy-optimal perception, planning, and control of embodied agents.

All manuscripts submitted to the special issue will be subjected to peer review. Prospective authors should submit an electronic copy of their completed manuscript to https://mc03.manuscriptcentral.com/cai with “Special Issue on Machine Learning for Advanced Energy Systems and Embodied Intelligence” marked in the cover letter.

Important Date

Manuscript Due: March 31, 2027

Guest Editors

Assoc. Prof. Benben Jiang

Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University, China

E-mail: bbjiang@tsinghua.edu.cn

Dr. Ruixue Liu

Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University, China

E-mail: liuruixue@mail.tsinghua.edu.cn