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Future Smart Battery Management Systems

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A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 18019

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Special Issue Editor

Dr. Farshid Naseri

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AAU Energy, Aalborg University, Pontoppidanstræde 111, 1-110, 9220 Aalborg Ø, Danmark
Interests: battery modelling; battery algorithms; design and development of energy storage systems; modular multilevel converters and their applications; condition monitoring of electric vehicles

Special Issue Information

Dear Colleagues,

Recent advancements in battery manufacturing technologies that have new battery capabilities and chemistries are very promising. Aside from the battery advancement itself, complementary technologies must be developed and enhanced to adapt to new battery requirements so that one can use batteries to their full advantage. One such technology is the battery management system or BMS, which is responsible for monitoring, controlling and protecting battery packs. The BMS estimates a battery’s status and its dynamic operating limits and uses this information to operate the battery within a safe and optimum operating window.

Within the electric vehicle context, BMS performance crucially affects the driving range, lifetime, and safety of the car. Thus, it is important to use the best-in-class hardware and software algorithms to achieve a higher performance. This Special Issue focuses on the topic of the smart BMSs to enable an improved battery performance, safety, and resiliency through smart functionalities, such as using artificial intelligence for state-of-X estimation, smart thermal management strategies, and reconfigurable and fault-tolerant topologies. Thus, this Special Issue covers all software and hardware aspects of the BMSs, including but not limited to the following:

Battery modelling techniques;
Smart thermal management;
State-of-charge estimation;
State-of-health estimation;
State-of-power estimation;
Advanced sensing units;
Energy management;
Battery fault diagnosis;
Battery fault prognosis;
Remaining useful life estimation;
Battery cell and module balancing;
Battery smart charging;
Modular and reconfigurable battery systems;
Fault-tolerant battery systems.

Dr. Farshid Naseri
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

battery management system (BMS)
electric vehicle (EV)
smart functionalities

Published Papers (5 papers)

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Research

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20 pages, 1400 KiB

Open AccessEditor’s ChoiceArticle

Maximizing Efficiency in Smart Adjustable DC Link Powertrains with IGBTs and SiC MOSFETs via Optimized DC-Link Voltage Control

by Yu Xu, Anton Kersten, Simon Klacar and David Sedarsky

Batteries 2023, 9(6), 302; https://doi.org/10.3390/batteries9060302 - 31 May 2023

Cited by 4 | Viewed by 1804

Abstract

In recent years, the push towards electrifying transportation has gained significant traction, with battery-electric vehicles (BEVs) emerging as a viable alternative. However, the widespread adoption of BEVs faces multiple challenges, such as limited driving range, making powertrain efficiency improvements crucial. One approach to improve powertrain energy efficiency is to adjust the DC-link voltage using a DC-DC converter between the battery and inverter. Here, it is necessary to address the losses introduced by the DC-DC converter. This paper presents a dynamic programming approach to optimize the DC-link voltage, taking into account the battery terminal voltage variation and its impact on the overall powertrain losses. We also examine the energy efficiency gains of IGBT-based and silicon carbide (SiC) MOSFET-based adjustable DC-link voltage powertrains during WLTC driving cycles through PLECS and Matlab/Simulink simulations. The findings indicate that both IGBT and MOSFET-based adjustable DC-link voltage powertrains can enhance the WLTC drive-cycle efficiency up to

2.51 %

and

3.25 %

compared to conventional IGBT and MOSFET-based powertrains, respectively. Full article

(This article belongs to the Special Issue Future Smart Battery Management Systems)

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14 pages, 1673 KiB

Open AccessArticle

Control-Oriented Electrochemical Model and Parameter Estimation for an All-Copper Redox Flow Battery

by Wouter Badenhorst, Christian M. Jensen, Uffe Jakobsen, Zahra Esfahani and Lasse Murtomäki

Batteries 2023, 9(5), 272; https://doi.org/10.3390/batteries9050272 - 15 May 2023

Viewed by 1407

Abstract

Redox flow batteries are an emergent technology in the field of energy storage for power grids with high renewable generator penetration. The copper redox flow battery (CuRFB) could play a significant role in the future of electrochemical energy storage systems due to the numerous advantages of its all-copper chemistry. Furthermore, like the more mature vanadium RFB technology, CuRFBs have the ability to independently scale power and capacity while displaying very fast response times that make the technology attractive for a variety of grid-supporting applications. As with most batteries, the efficient operation of a CuRFB is dependent on high-quality control of both the charging and discharging process. In RFBs, this is typically complicated by highly nonlinear behaviour, particularly at either extreme of the state of charge. Therefore, the focus of this paper is the development and validation of a first-principle, control-appropriate model of the CuRFBs electrochemistry that includes the impact of the flow, charging current, and capacity fading due to diffusion and subsequent comproportionation. Parameters for the proposed model are identified using a genetic algorithm, and the proposed model is validated along with its identified parameters using data obtained from a single-cell CuRFB flow battery as well as a simpler diffusion cell design. The proposed model yields good qualitative fits to experimental data and physically plausible concentration estimates and appears able to quantify the long-term state of health due to changes in the diffusion coefficient. Full article

(This article belongs to the Special Issue Future Smart Battery Management Systems)

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20 pages, 12758 KiB

Open AccessArticle

General Decoupling and Sampling Technique for Reduced-Sensor Battery Management Systems in Modular Reconfigurable Batteries

by Nima Tashakor, Janvier Dusengimana, Mahdi Bayati, Anton Kersten, Hans Schotten and Stefan Götz

Batteries 2023, 9(2), 99; https://doi.org/10.3390/batteries9020099 - 1 Feb 2023

Cited by 6 | Viewed by 1955

Abstract

The capacity and voltage rating of battery packs for electric vehicles or stationary energy storages are increasing, which challenge battery management and monitoring. Breaking the larger pack into smaller modules and using power electronics to achieve dynamic reconfiguration can be a solution. Reconfigurable batteries come with their own set of problems, including many sensors and complex monitoring systems, high-bandwidth communication interfaces, and additional costs. Online parameter estimation methods can simplify or omit many of these problems and reduce the cost and footprint of the system. However, most methods require many sensors or can only estimate a subset of the elements in the module’s equivalent circuit model (ECM). This paper proposes a simple decoupling technique to derive individual modules’ voltage and current profiles from the output measurements without direct measurement at the modules. The determined profiles can achieve a high sampling rate with minimum communication between the battery management system (BMS) and the modules. With accurate profiles, an estimation technique can easily determine the parameters of the modules. Provided simulations and experiments confirm this claim by estimating the parameters of a first-order ECM with a parallel capacitor. The proposed technique reduces the number of sensors from 2N + 2 to only two at the pack’s output terminals. Full article

(This article belongs to the Special Issue Future Smart Battery Management Systems)

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Review

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33 pages, 6371 KiB

Open AccessReview

Cyber-Physical Cloud Battery Management Systems: Review of Security Aspects

by Farshid Naseri, Zahra Kazemi, Peter Gorm Larsen, Mohammad Mehdi Arefi and Erik Schaltz

Batteries 2023, 9(7), 382; https://doi.org/10.3390/batteries9070382 - 18 Jul 2023

Cited by 1 | Viewed by 2976

Abstract

Battery management systems (BMSs) are critical to ensure the efficiency and safety of high-power battery energy storage systems (BESSs) in vehicular and stationary applications. Recently, the proliferation of battery big data and cloud computing advancements has led to the development of a new generation of BMSs, named Cloud BMS (CBMS), aiming to improve the performance and safety of BESSs. The CBMS is a cyber-physical system with connectivity between the physical BMS and a cloud-based virtual BMS, which is realized through a communication channel such as Internet of Things. Compared to the traditional BMS, the CBMS offers significantly higher computational resources, leveraging the implementation of advanced digital twin models and best-in-class algorithms in the BMS software, which will provide superior performances. However, as for any other CPS, the CBMS creates vulnerabilities against cyberattacks and if not properly secured, could end up damaging the BESS and/or causing dangerous, expensive, and life-threatening situations. Cybersecurity of the CBMSs has thus become a trending topic and several works have been published in this area in recent years. This paper conducts a scoping review to address different topics related to BMS cybersecurity. The CBMS architecture is presented, and the potential cyberattack surfaces are identified. Different possible attack scenarios, including attack points, attack types, and their impact at the component level (BMS and BESS) and system level (vehicle or grid), are discussed. In addition, the paper provides a review of potential countermeasures to protect the CBMS against cyberattacks. The paper also includes a review of the applicable standards and regulations that relate to this trending topic. Finally, based on the reviewed gaps, potential future research domains on BMS cybersecurity topics are identified and presented at the end of the paper. Full article

(This article belongs to the Special Issue Future Smart Battery Management Systems)

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59 pages, 7089 KiB

Open AccessReview

Smart Battery Management Technology in Electric Vehicle Applications: Analytical and Technical Assessment toward Emerging Future Directions

by Molla Shahadat Hossain Lipu, Md. Sazal Miah, Shaheer Ansari, Safat B. Wali, Taskin Jamal, Rajvikram Madurai Elavarasan, Sachin Kumar, M. M. Naushad Ali, Mahidur R. Sarker, A. Aljanad and Nadia M. L. Tan

Batteries 2022, 8(11), 219; https://doi.org/10.3390/batteries8110219 - 5 Nov 2022

Cited by 11 | Viewed by 8068

Abstract

Electric vehicles (EVs) have received widespread attention in the automotive industry as the most promising solution for lowering CO₂ emissions and mitigating worldwide environmental concerns. However, the effectiveness of EVs can be affected due to battery health degradation and performance deterioration with lifespan. Therefore, an advanced and smart battery management technology is essential for accurate state estimation, charge balancing, thermal management, and fault diagnosis in enhancing safety and reliability as well as optimizing an EV’s performance effectively. This paper presents an analytical and technical evaluation of the smart battery management system (BMS) in EVs. The analytical study is based on 110 highly influential articles using the Scopus database from the year 2010 to 2020. The analytical analysis evaluates vital indicators, including current research trends, keyword assessment, publishers, research categorization, country analysis, authorship, and collaboration. The technical assessment examines the key components and functions of BMS technology as well as state-of-the-art methods, algorithms, optimization, and control surgeries used in EVs. Furthermore, various key issues and challenges along with several essential guidelines and suggestions are delivered for future improvement. The analytical analysis can guide future researchers in enhancing the technologies of battery energy storage and management for EV applications toward achieving sustainable development goals. Full article

(This article belongs to the Special Issue Future Smart Battery Management Systems)

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