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Batteries
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Towards a Smarter Battery Management System: 3rd Edition
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Towards a Smarter Battery Management System: 3rd Edition

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Performance, Ageing, Reliability and Safety".

Deadline for manuscript submissions: 25 January 2026 | Viewed by 1932

Special Issue Editors

Dr. Zhi Cao

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: battery management systems; energy management systems; electric machines; magnetic bearings
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chris Mi

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: wireless power transfer; battery management systems; power electronics; hybrid electric vehicles; electric machines
Special Issues, Collections and Topics in MDPI journals
Dr. Naser Vosoughi Kurdkandi

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, College of Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: DC–DC and DC–AC power electronics converters; battery-based energy storage systems; on-board and off-board battery chargers for EVs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lithium-ion batteries are widely used in electric vehicles (EVs) and the energy storage industry due to their high energy density and long cycle life. As their price decreases, lithium-ion batteries will continue to be used in the future.

Battery management systems (BMSs) are the key component to ensure the stable and reliable operation of battery systems. They monitor battery operation data, estimate the battery state of charge (SOC) and state of health (SOH), conduct battery balance, manage thermal systems, and perform fault diagnosis. BMS-related hardware and algorithms have developed rapidly in recent years. Therefore, this Special Issue aims to demonstrate the latest BMS-related technologies, such as SOC and SOH estimation algorithms, balance systems, wireless BMSs, and second-life battery applications.

Potential topics include, but are not limited to, the following:

  • Battery management system hardware and algorithms;
  • Battery modeling;
  • Battery parameter identification;
  • Battery state of charge (SOC) estimation;
  • Battery state of health (SOH) estimation;
  • Battery fault diagnostics;
  • Battery balance or equalization topology and method;
  • Battery thermal management;
  • Battery second-life application;
  • Wireless BMSs.

Dr. Zhi Cao
Prof. Dr. Chris Mi
Dr. Naser Vosoughi Kurdkandi
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

  • lithium-ion battery
  • battery management system
  • battery modeling
  • battery SOC estimation
  • battery SOH estimation
  • battery parameter identification
  • battery balance
  • battery equalization
  • battery thermal management
  • battery thermal runaway
  • second-life battery
  • battery recycling
  • wireless BMSs

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

Related Special Issue

Published Papers (3 papers)

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Research

27 pages, 2037 KB  
Article
Multi-Objective Sizing Method for PV-BESS Integration with EV Charging Stations and Analysis Across Different Parking Scenarios
by Francesco Maria Tiburtini, Francesco Lo Franco and Mattia Ricco
Batteries 2025, 11(11), 422; https://doi.org/10.3390/batteries11110422 - 17 Nov 2025
Viewed by 601
Abstract
The growing adoption of electric vehicles (EVs) is driving the expansion of charging networks. Local photovoltaic (PV) systems combined with battery energy storage systems (BESS) have emerged as promising solutions to mitigate the impact of charging demand on the grid and reduce the [...] Read more.
The growing adoption of electric vehicles (EVs) is driving the expansion of charging networks. Local photovoltaic (PV) systems combined with battery energy storage systems (BESS) have emerged as promising solutions to mitigate the impact of charging demand on the grid and reduce the environmental impact of EV charging. In this context, proper sizing of PV-BESS systems is crucial to maximize their integration with charging hubs (CHs) and ensure optimal performance. This paper proposes a multi-objective sizing method to optimize the energy and economic performance of PV-BESS systems in EV charging hubs. Sizing optimization is performed using a Non-Dominated Sorting Genetic Algorithm-II. The method is applied to four CH scenarios characterized by variations in energy demand, user behavior, and location. Results indicate that while optimal PV size remains relatively consistent across scenarios, the ideal BESS configuration varies with each scenario’s characteristics. Optimized PV-BESS integration significantly improves energy performance, increasing system self-sufficiency by up to +72%. From an economic point of view, results show that in some cases, smaller BESS capacities are more advantageous due to lower capital costs, while in others, larger BESS sizes reduce overall costs by up to −50%, significantly cutting utility expenses despite higher initial investment. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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15 pages, 969 KB  
Article
Techno-Economic and Environmental Viability of Second-Life EV Batteries in Commercial Buildings: An Analysis Using Real-World Data
by Zhi Cao, Naser Vosoughi Kurdkandi and Chris Mi
Batteries 2025, 11(11), 412; https://doi.org/10.3390/batteries11110412 - 7 Nov 2025
Viewed by 602
Abstract
The rapid growth of electric vehicle markets is producing large volumes of retired lithium-ion batteries retaining 70–80% of their original capacity, suitable for stationary energy storage. This study assesses the techno-economic and environmental viability of second-life battery energy storage systems (SLBESS) in a [...] Read more.
The rapid growth of electric vehicle markets is producing large volumes of retired lithium-ion batteries retaining 70–80% of their original capacity, suitable for stationary energy storage. This study assesses the techno-economic and environmental viability of second-life battery energy storage systems (SLBESS) in a California commercial building, using one year of operational data. SLBESS performance is compared with equivalent new battery systems under identical dispatch strategies, building load profiles, and time-of-use tariff structures. A dispatch-aware framework integrates multi-year battery simulations, degradation modeling, electricity cost analysis, and life cycle assessment based on marginal grid emissions. The economic analysis quantifies the net present value (NPV), internal rate of return (IRR), and operational levelized cost of storage (LCOSop). Results show that SLBESS achieve 49.2% higher NPV, 41.9% higher IRR, and 13.8% lower LCOSop than new batteries, despite their lower round-trip efficiency. SLBESS reduce embodied emissions by 41% and achieve 8% lower carbon intensity than new batteries. Sensitivity analysis identifies that economic outcomes are driven primarily by financial parameters (incentives, acquisition cost) rather than technical factors (degradation, initial health), providing a clear rationale for policies that reduce upfront costs. Environmentally, grid emission factors are the dominant driver. Battery degradation rate and initial state of health have minimal impact, suggesting that technical concerns may be overstated. These findings provide actionable insights for deploying cost-effective, low-carbon storage in commercial buildings. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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10 pages, 1979 KB  
Article
A Novel Approach to Day-Ahead Forecasting of Battery Discharge Profiles in Grid Applications Using Historical Daily
by Marek Bobček, Róbert Štefko, Július Šimčák and Zsolt Čonka
Batteries 2025, 11(10), 370; https://doi.org/10.3390/batteries11100370 - 6 Oct 2025
Viewed by 494
Abstract
This paper presents a day-ahead forecasting approach for discharge profiles of a 0.5 MW battery energy storage system connected to the power grid, utilizing historical daily discharge profiles collected over one year to capture key operational patterns and variability. Two forecasting techniques are [...] Read more.
This paper presents a day-ahead forecasting approach for discharge profiles of a 0.5 MW battery energy storage system connected to the power grid, utilizing historical daily discharge profiles collected over one year to capture key operational patterns and variability. Two forecasting techniques are employed: a Kalman filter for dynamic state estimation and Holt’s exponential smoothing method enhanced with adaptive alpha to capture trend changes more responsively. These methods are applied to generate next-day discharge forecasts, aiming to support better battery scheduling, improve grid interaction, and enhance overall energy management. The accuracy and robustness of the forecasts are evaluated against real operational data. The results confirm that combining model-based and statistical techniques offers a reliable and flexible solution for short-term battery discharge prediction in real-world grid applications. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)
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