Battery Management Processes, Modeling, and Optimization

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (5 August 2024) | Viewed by 1537

Special Issue Editors


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Guest Editor
Center of Sustainable Process Engineering (CoSPE), Department of Chemical Engineering, Hankyong National University, Gyeonggi-do, Anseong-si 17579, Jungang-ro 327, Republic of Korea
Interests: multiphase flows; chemical engineering; computational fluid dynamics; multiscale modeling; high-performance computing; machine learning in CFD
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Guest Editor Assistant
NTT Hi-Tech Institute, Nguyen Tat Thanh University, 300A Nguyen Tat Thanh, Ward 13, District 4, Ho Chi Minh City, Vietnam
Interests: porous electrode material from biomass; eco-friendly ion exchange membrane; water desalination via capacitive deionization

Special Issue Information

Dear Colleagues,

The model-based engineering solution framework for electric vehicle battery packs encompasses three crucial components: battery management, battery modeling, and battery optimization.

Battery management processes (BMPs) encompass a diverse set of techniques and procedures meticulously designed to enhance battery performance, efficiency, and overall lifespan. The key aspects of BMPs encompass the management of the battery's state of charge (SOC) and state of health (SOH), safety protocols, cell balancing techniques, and effective thermal management.

Battery modeling, a fundamental aspect of the framework, encompasses a variety of approaches such as computational fluid dynamics (CFD), electro-thermal models, circuit models, and surrogate or neural network models. These models play a pivotal role in predicting and understanding the battery behavior under various conditions, enabling more precise and efficient control strategies.

In parallel, battery optimization aims to achieve real-time adaptivity, cost analysis, model predictive control, and multi-objective optimization. This optimization process strives to strike a balance between conflicting objectives, enhancing the battery pack's performance while considering factors like cost-effectiveness and energy efficiency.

By synergizing battery management, modeling, and optimization, this comprehensive framework serves as a sophisticated foundation for advancing electric vehicle battery technology. It enables manufacturers and researchers to create cutting-edge battery solutions, ensuring electric vehicles are safer, more efficient, and more reliable, thus propelling the widespread adoption of sustainable transportation.

Dr. Son Ich Ngo
Guest Editor

Dr. Hoang Long Ngo
Guest Editor Assistant

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Keywords

  • electric vehicles
  • battery performance
  • thermal management
  • battery management
  • battery modeling

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Published Papers (1 paper)

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Research

17 pages, 4972 KiB  
Article
Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage–Distribution Networks
by Lihua Zhong, Tong Ye, Yuyao Yang, Feng Pan, Lei Feng, Shuzhe Qi and Yuping Huang
Processes 2024, 12(9), 1791; https://doi.org/10.3390/pr12091791 - 23 Aug 2024
Viewed by 852
Abstract
As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance [...] Read more.
As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon responsibility allocation on the user side. Additionally, we integrate user-side SES and ladder-type carbon emission pricing into DN to create a low-carbon economic dispatch model. By framing the problem as a Markov decision process (MDP), we employ the DRL, specifically the deep deterministic policy gradient (DDPG) algorithm, enhanced with prioritized experience replay (PER) and orthogonal regularization (OR), to achieve both economic efficiency and environmental sustainability. The simulation results indicate that this method significantly reduces the operating costs and carbon emissions of DN. This study offers an innovative perspective on the synergistic optimization of SES with DN and provides a practical methodology for low-carbon economic dispatch in power systems. Full article
(This article belongs to the Special Issue Battery Management Processes, Modeling, and Optimization)
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