Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.6
4.8
Journals
Batteries
Special Issues
AI-Powered Battery Management and Grid Integration for Smart Cities
share announcement

AI-Powered Battery Management and Grid Integration for Smart Cities

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Electric Vehicles and Mobile Energy Storage Systems".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 11248

Special Issue Editors

Dr. Muhammed Cavus

E-Mail Website
Guest Editor
Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle, UK
Interests: electric vehicles; energy management; batteries; model predictive control; deep learning methods; digital twins
Prof. Dr. Margaret Carol Bell

E-Mail Website
Guest Editor
Civil Engineering and Geosciences, Newcastle University, Newcastle, UK
Interests: traffic and environment monitoring; modelling management and control; battery management for electric vehicles

Special Issue Information

Dear Colleagues,

The electrification of mobility, the rise of renewable energy integration, and the evolution of smart city infrastructures are driving new demands for intelligent battery management and grid interaction. Central to this transformation is the deployment of AI-powered approaches to optimise battery State of Health (SOH), Remaining Useful Life (RUL), and operational efficiency across electric vehicle (EV) fleets and distributed energy storage systems. This Special Issue invites original research and comprehensive reviews on advanced machine learning (ML), deep learning (DL), and digital twin methodologies that enhance battery diagnostics, predictive maintenance, and grid-aware optimisation. Particular attention is given to solutions that enable seamless Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) integration, improve grid resilience, and support sustainable energy management in urban environments. Contributions demonstrating real-world applications, interdisciplinary innovations, and explainable AI frameworks for battery systems are highly encouraged. The aim of this Special Issue is to advance knowledge at the intersection of battery intelligence, grid dynamics, and smart city mobility, in line with the mission of Batteries to foster sustainable and efficient energy storage technologies.

Expected topics:

  • Battery SOH and RUL estimation using AI;
  • Predictive maintenance of EV batteries;
  • AI-enhanced V2G and G2V frameworks;
  • ML/DL for EV fleet energy management;
  • Digital twins for smart battery systems;
  • Explainable AI for battery health monitoring;
  • Grid resilience through intelligent battery control;
  • Data-driven optimisation of smart grid energy flows;
  • AI for ageing-aware charging and load scheduling;
  • Case studies of AI-powered battery-grid interaction in smart cities.

Dr. Muhammed Cavus
Prof. Dr. Margaret Carol Bell
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Batteries is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI-powered battery management
  • machine learning
  • deep learning
  • battery SOH estimation
  • predictive maintenance
  • V2G and G2V integration
  • digital twins
  • smart grids
  • electric vehicle fleets
  • grid resilience
  • smart cities
  • sustainable energy storage

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

23 pages, 1607 KB  
Article
Simulation and Optimization of V2G Energy Exchange in an Energy Community Using MATLAB and Multi-Objective Genetic Algorithm Optimization
by Mohammad Talha Yaar Khan and Jozsef Menyhart
Batteries 2026, 12(4), 143; https://doi.org/10.3390/batteries12040143 - 17 Apr 2026
Viewed by 397
Abstract
The Vehicle-to-Grid (V2G) technology is considered one of the best solutions for integrating renewable energy systems; however, most literature reports favorable economic results using synthetic data, without accounting for seasonal or market limitations. The current research presents the results of the MATLAB R2023b [...] Read more.
The Vehicle-to-Grid (V2G) technology is considered one of the best solutions for integrating renewable energy systems; however, most literature reports favorable economic results using synthetic data, without accounting for seasonal or market limitations. The current research presents the results of the MATLAB R2023b (Version 23.2, MathWorks, Natick, MA, USA) simulation of the 100-household energy community in Debrecen, Hungary, with 30 electric vehicles (EVs) using entirely simulation-based Lithium Iron Phosphate (LiFePO4) batteries, a simulation-based 150 kW solar photovoltaic (PV) system, and a simulation-based 200 kW wind power system, using real meteorological data for January 2024. The optimization of charging/discharging for electric vehicles was performed using a multi-objective genetic algorithm (GA) over 30 days at a 15 min time resolution, accounting for stochastic loads and temperature effects on battery degradation, with a sensitivity analysis of key parameters. The results of the optimized solution for the electric vehicle charging/discharging were unexpected: the total energy cost increased by 68.9% ($4337.65 to $7327.54), the peak demand increased by 266.2% (31.9 to 116.9 kW), the degradation cost was $479.63, the load factor was reduced from 0.847 to 0.722, and the SOC constraint was violated for 0.758% of measurements. The V2G is not economically viable under current Hungarian pricing and Central Europe winter conditions. Results are robust for varying parameters using sensitivity analysis and Pareto front tracing. The break-even point is achieved when ratios of peak-to-off-peak prices are above 3.5:1. Seasonal policies and market reforms are critical for V2G viability. Importantly, the influence of inherent design deficiencies in the optimization model on the reported results cannot be ruled out. Full article
(This article belongs to the Special Issue AI-Powered Battery Management and Grid Integration for Smart Cities)
►▼ Show Figures

Figure 1

21 pages, 5487 KB  
Article
A Health-Aware Hybrid Reinforcement–Predictive Control Framework for Sustainable Energy Management in Photovoltaic–Electric Vehicle Microgrids
by Muhammed Cavus and Margaret Bell
Batteries 2026, 12(1), 5; https://doi.org/10.3390/batteries12010005 - 24 Dec 2025
Cited by 2 | Viewed by 1842
Abstract
The increasing electrification of mobility within smart cities has accelerated the need for intelligent energy management strategies that jointly address cost, emissions, and battery health. This study develops a health-aware hybrid reinforcement–predictive energy manager (H-RPEM) designed for photovoltaic–electric vehicle (PV-EV) microgrids. The proposed [...] Read more.
The increasing electrification of mobility within smart cities has accelerated the need for intelligent energy management strategies that jointly address cost, emissions, and battery health. This study develops a health-aware hybrid reinforcement–predictive energy manager (H-RPEM) designed for photovoltaic–electric vehicle (PV-EV) microgrids. The proposed controller unifies model-based predictive optimisation with adaptive reinforcement learning to achieve both short-term operational efficiency and long-term asset preservation. A comprehensive dataset of solar generation, EV charging behaviour, and stochastic load profiles was employed to train and validate the hybrid control framework under realistic operating conditions. Quantitative results indicate that the proposed H-RPEM controller achieves an 18.7% reduction in total operating cost and a 22.5% decrease in carbon emissions, whilst maintaining the battery state-of-health above 0.95 throughout a 24 h operational cycle. When benchmarked against standard predictive control, the hybrid strategy converges 30–40 episodes faster and delivers a 25% improvement in reward stability, demonstrating enhanced robustness and learning efficiency. The results confirm that H-RPEM achieves robust and balanced performance across economic, environmental, and technical domains, establishing it as a scalable and health-conscious control solution for next-generation smart city microgrids. Full article
(This article belongs to the Special Issue AI-Powered Battery Management and Grid Integration for Smart Cities)
►▼ Show Figures

Figure 1

12 pages, 1642 KB  
Article
Modelling of Battery Energy Storage Systems Under Real-World Applications and Conditions
by Achim Kampker, Benedikt Späth, Xiaoxuan Song and Datao Wang
Batteries 2025, 11(11), 392; https://doi.org/10.3390/batteries11110392 - 24 Oct 2025
Cited by 1 | Viewed by 3523
Abstract
Understanding the degradation behavior of lithium-ion batteries under realistic application conditions is critical for the design and operation of Battery Energy Storage Systems (BESS). This research presents a modular, cell-level simulation framework that integrates electrical, thermal, and aging models to evaluate system performance [...] Read more.
Understanding the degradation behavior of lithium-ion batteries under realistic application conditions is critical for the design and operation of Battery Energy Storage Systems (BESS). This research presents a modular, cell-level simulation framework that integrates electrical, thermal, and aging models to evaluate system performance in representative utility and residential scenarios. The framework is implemented using Python and allows time-series simulations to be performed under different state of charge (SOC), depth of discharge (DOD), C-rate, and ambient temperature conditions. Simulation results reveal that high-SOC windows, deep cycling, and elevated temperatures significantly accelerate capacity fade, with distinct aging behavior observed between residential and utility profiles. In particular, frequency modulation and deep-cycle self-consumption use cases impose more severe aging stress compared to microgrid or medium-cycle conditions. The study provides interpretable degradation metrics and visualizations, enabling targeted aging analysis under different load conditions. The results highlight the importance of thermal effects and cell-level stress variability, offering insights for lifetime-aware BESS control strategies. This framework serves as a practical tool to support the aging-resilient design and operation of grid-connected storage systems. Full article
(This article belongs to the Special Issue AI-Powered Battery Management and Grid Integration for Smart Cities)
►▼ Show Figures

Figure 1

Other

Jump to: Research

39 pages, 4912 KB  
Systematic Review
Grid-Scale Battery Energy Storage and AI-Driven Intelligent Optimization for Techno-Economic and Environmental Benefits: A Systematic Review
by Nipon Ketjoy, Yirga Belay Muna, Malinee Kaewpanha, Wisut Chamsa-ard, Tawat Suriwong and Chakkrit Termritthikun
Batteries 2026, 12(1), 31; https://doi.org/10.3390/batteries12010031 - 17 Jan 2026
Cited by 3 | Viewed by 4849
Abstract
Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability. However, harnessing their full [...] Read more.
Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability. However, harnessing their full potential and lifetime requires intelligent operational strategies that balance technological performance, economic viability, and environmental sustainability. This systematic review examines how artificial intelligence (AI)-based intelligent optimization enhances GS-BESS performance, focusing on its techno-economic, environmental impacts, and policy and regulatory implications. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we review the evolution of GS-BESS, analyze its advancements, and assess state-of-the-art applications and emerging AI techniques for GS-BESS optimization. AI techniques, including machine learning (ML), predictive modeling, optimization algorithms, deep learning (DL), and reinforcement learning (RL), are examined for their ability to improve operational efficiency and control precision in GS-BESSs. Furthermore, the review discusses the benefits of advanced dispatch strategies, including economic efficiency, emissions reduction, and improved grid resilience. Despite significant progress, challenges persist in data availability, model generalization, high computational requirements, scalability, and regulatory gaps. We conclude by identifying emerging opportunities to guide the next generation of intelligent energy storage systems. This work serves as a foundational resource for researchers, engineers, and policymakers seeking to advance the deployment of AI-enhanced GS-BESS for sustainable, resilient power systems. By analyzing the latest developments in AI applications and BESS technologies, this review provides a comprehensive perspective on their synergistic potential to drive sustainability, cost-effectiveness, and energy systems reliability. Full article
(This article belongs to the Special Issue AI-Powered Battery Management and Grid Integration for Smart Cities)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop