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Novel Battery Management Systems Using AI in Automotive Applications
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Novel Battery Management Systems Using AI in Automotive Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 12103

Special Issue Editors

Dr. Stefano Feraco

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy
Interests: artificial intelligence; Lithium batteries; electric and hybrid vehicles; autonomous vehicles; virtual sensing.
Dr. Angelo Bonfitto

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: mechatronics systems for automation; electrified powertrains; assisted and autonomous driving; active and passive vibration control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nicola Amati

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: applied mechanics; automotive applications; electromagnetic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the automotive industry, increasing concerns about global warming as well as oil and resource depletion have created an incentive to focus efforts on alternative powertrain technologies. In this context, the development of novel battery systems has gained increasing attention due to their fundamental role in fully electric, hybrid and plug-in hybrid electric vehicles. Nevertheless, battery performance and health are severely affected by application and environmental factors such as temperature, charge/discharge rates, etc. Automotive batteries require constant and accurate monitoring to check their condition, specifically, the level of the remaining available energy and power capability, often indicated by the state of charge and state of health. An accurate and reliable knowledge of those parameter can significantly mitigate psychological factors such as the range anxiety associated with electric vehicles, while improving system performance and lifespan. However, the  battery state of charge and health cannot be directly measured, and these values can only be estimated from the measurement of other battery parameters via novel battery management systems.

The main aim of this Special Issue is to seek high-quality submissions that highlight emerging applications and address recent breakthroughs in the battery management systems using Artificial Intelligence for automotive applications. The topics of interest include, but are not limited to:

  • battery management systems in automotive applications with Artificial Intelligence
  • battery management systems for other applications with Artificial Intelligence
  • state of charge estimation with Artificial Intelligence
  • state of health estimation with Artificial Intelligence
  • prognostic and diagnostic of automotive batteries with Artificial Intelligence
  • application of Artificial Intelligence in novel battery management systems

Dr. Stefano Feraco
Dr. Angelo Bonfitto
Prof. Dr. Nicola Amati
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 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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • Automotive engineering
  • Electric and hybrid vehicles
  • Artificial intelligence
  • Battery management systems
  • State of charge
  • State of health
  • Virtual sensing

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Published Papers (2 papers)

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Research

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15 pages, 7754 KiB  
Article
Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles
by Sara Luciani, Stefano Feraco, Angelo Bonfitto and Andrea Tonoli
Electronics 2021, 10(22), 2828; https://doi.org/10.3390/electronics10222828 - 18 Nov 2021
Cited by 15 | Viewed by 4351
Abstract
This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (SOC) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion [...] Read more.
This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (SOC) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion battery that is numerically modeled via an RC equivalent circuit model that can also consider the environmental temperature influence. The proposed estimation technique relies on nonlinear autoregressive with exogenous input (NARX) artificial neural networks (ANNs) that are properly trained with multiple datasets. Those datasets include modeled current and voltage data, both for charge-sustaining and charge-depleting working conditions. The investigated method is then experimentally validated using a Raspberry Pi 4B card-sized board, on which the estimation algorithm is actually deployed, and real-time hardware, on which the battery model is developed, namely a Speedgoat baseline platform. These hardware platforms are used in a hardware-in-the-loop architecture via the UPD communication protocol, allowing the system to be validated in a proper testing environment. The resulting estimation algorithm can estimate the battery SOC in real-time, with 2% accuracy during real-time hardware testing. Full article
(This article belongs to the Special Issue Novel Battery Management Systems Using AI in Automotive Applications)
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Review

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24 pages, 3836 KiB  
Review
Digital Technology Implementation in Battery-Management Systems for Sustainable Energy Storage: Review, Challenges, and Recommendations
by Gopal Krishna, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, Neeraj Priyadarshi and Bhekisipho Twala
Electronics 2022, 11(17), 2695; https://doi.org/10.3390/electronics11172695 - 27 Aug 2022
Cited by 47 | Viewed by 6885
Abstract
Energy storage systems (ESS) are among the fastest-growing electrical power system due to the changing worldwide geography for electrical distribution and use. Traditionally, methods that are implemented to monitor, detect and optimize battery modules have limitations such as difficulty in balancing charging speed [...] Read more.
Energy storage systems (ESS) are among the fastest-growing electrical power system due to the changing worldwide geography for electrical distribution and use. Traditionally, methods that are implemented to monitor, detect and optimize battery modules have limitations such as difficulty in balancing charging speed and battery capacity usage. A battery-management system overcomes these traditional challenges and enhances the performance of managing battery modules. The integration of advancements and new technologies enables the provision of real-time monitoring with an inclination towards Industry 4.0. In the previous literature, it has been identified that limited studies have presented their reviews by combining the literature on different digital technologies for battery-management systems. With motivation from the above aspects, the study discussed here aims to provide a review of the significance of digital technologies like wireless sensor networks (WSN), the Internet of Things (IoT), artificial intelligence (AI), cloud computing, edge computing, blockchain, and digital twin and machine learning (ML) in the enhancement of battery-management systems. Finally, this article suggests significant recommendations such as edge computing with AI model-based devices, customized IoT-based devices, hybrid AI models and ML-based computing, digital twins for battery modeling, and blockchain for real-time data sharing. Full article
(This article belongs to the Special Issue Novel Battery Management Systems Using AI in Automotive Applications)
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