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Electronics
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Intelligent Energy Management System for Electric Vehicles
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Intelligent Energy Management System for Electric Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 6956

Special Issue Editors

Dr. Lulu Guo

E-Mail Website
Guest Editor
Department of Control Science and Engineering, and Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, China
Interests: electric vehicles; energy management systems; model predictive control; optimal control theory; vehicle cybersecurity
Dr. Liang Du

E-Mail Website
Co-Guest Editor
Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA 19122, USA
Interests: electric power grid modernization; energy systems integration; de-centralized and autonomous power architectures; data-driven analytics; renewable integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electric vehicles (EVs) have received increasing attention in recent years with stringent standards and regulations on emissions. Compared with an internal combustion engine (ICE) vehicles, new-energy EVs are a promising renewable technology for a sustainable energy future. In particular, battery EVs with a large energy storage system (e.g., lithium iron phosphate (LiFePO4) battery), are expected to provide efficiencies of up to 80%. However, the relatively short driving range has been the main barrier for prospective customers.

Today, onboard navigation systems, vehicle-to-vehicle (V2V), and vehicle-to-x (V2X) in modern connected vehicles help to gain traffic information over the preview route segment, which opens up unprecedented opportunities for improving energy efficiency. Thus, the combination of eco-driving and energy management in powertrains makes intelligent energy management systems (EMS) for EVs a hot topic in academia and the automotive industry. In particular, establishing a unitized system architecture consisting of external traffic, geographic information, and the powertrain system for the best energy efficiency is one of the most important emerging problems to be studied.

The main aim of this Special Issue is to seek high-quality submissions that highlight intelligent EMS. The topics of interest include but are not limited to the power/torque split for EVs and hybrid electric vehicles (HEVs), velocity profile optimization and eco-driving considering upcoming traffic, EMS considering battery health and driving range, energy-efficient control for electric drives in EVs, EMS concerning the uncertainty of energy models (e.g., time-varying efficiency maps), connoted and automated EVs, learning-based EMS, and real-time model predictive control in EMS. We invite contributions from experimentalists and theorists to submit their high-quality manuscripts for publication in this Special Issue.

Dr. Lulu Guo
Dr. Liang Du
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. 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

  • Power/torque split for EVs and HEVs
  • Eco-driving considering upcoming traffic
  • EMS considering battery health and driving range
  • Energy-efficient control for electric drives in EVs
  • EMS concerning the uncertainty of energy models
  • Connoted and automated EVs
  • Learning-based EMS
  • Real-time model predictive control in EMS

Published Papers (2 papers)

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Research

15 pages, 4089 KiB  
Article
Research on Co-Estimation Algorithm of SOC and SOH for Lithium-Ion Batteries in Electric Vehicles
by Chang-Qing Du, Jian-Bo Shao, Dong-Mei Wu, Zhong Ren, Zhong-Yi Wu and Wei-Qun Ren
Electronics 2022, 11(2), 181; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11020181 - 07 Jan 2022
Cited by 12 | Viewed by 2671
Abstract
The accurate estimation of the state of charge (SOC) and state of health (SOH) is of great significance to energy management and safety in electric vehicles. To achieve a good trade-off between real-time capability and estimation accuracy, a collaborative estimation algorithm for SOC [...] Read more.
The accurate estimation of the state of charge (SOC) and state of health (SOH) is of great significance to energy management and safety in electric vehicles. To achieve a good trade-off between real-time capability and estimation accuracy, a collaborative estimation algorithm for SOC and SOH is presented based on the Thevenin equivalent circuit model, which combines the recursive least squares method with a forgetting factor and the extended Kalman filter. First, the parameter identification accuracy is studied under a dynamic stress test (DST) and the federal urban driving schedule (FUDS) test at different ambient temperatures (0 °C, 25 °C, and 45 °C). Secondly, the FUDS test is used to verify the SOC estimation accuracy. Thirdly, two batteries with different aging degrees are used to validate the proposed SOH estimation algorithm. Subsequently, the accuracy of the SOC estimation algorithm is studied, considering the influence of updating the SOH. The proposed SOC estimation algorithm can achieve good performance at different ambient temperatures (0 °C, 25 °C, and 45 °C), with a maximum error of less than 2.3%. The maximum error for the SOH is less than 4.3% for two aged batteries at 25 °C, and it can be reduced to 1.4% after optimization. Furthermore, calibrating the capacity as the SOH changes can effectively improve the SOC estimation accuracy over the whole battery life. Full article
(This article belongs to the Special Issue Intelligent Energy Management System for Electric Vehicles)
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16 pages, 6094 KiB  
Article
The Role of Front-End AC/DC Converters in Hybrid AC/DC Smart Homes: Analysis and Experimental Validation
by Vitor Monteiro, Luis F. C. Monteiro, Francesco Lo Franco, Riccardo Mandrioli, Mattia Ricco, Gabriele Grandi and João L. Afonso
Electronics 2021, 10(21), 2601; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10212601 - 25 Oct 2021
Cited by 12 | Viewed by 3344
Abstract
Electrical power grids are rapidly evolving into smart grids, with smart homes also making an important contribution to this. In fact, the well-known and emerging technologies of renewables, energy storage systems and electric mobility are each time more distributed throughout the power grid [...] Read more.
Electrical power grids are rapidly evolving into smart grids, with smart homes also making an important contribution to this. In fact, the well-known and emerging technologies of renewables, energy storage systems and electric mobility are each time more distributed throughout the power grid and included in smart homes. In such circumstances, since these technologies are natively operating in DC, it is predictable for a revolution in the electrical grid craving a convergence to DC grids. Nevertheless, traditional loads natively operating in AC will continue to be used, highlighting the importance of hybrid AC/DC grids. Considering this new paradigm, this paper has as main innovation points the proposed control algorithms regarding the role of front-end AC/DC converters in hybrid AC/DC smart homes, demonstrating their importance for providing unipolar or bipolar DC grids for interfacing native DC technologies, such as renewables and electric mobility, including concerns regarding the power quality from a smart grid point of view. Furthermore, the paper presents a clear description of the proposed control algorithms, aligned with distinct possibilities of complementary operation of front-end AC/DC converters in the perspective of smart homes framed within smart grids, e.g., enabling the control of smart homes in a coordinated way. The analysis and experimental results confirmed the suitability of the proposed innovative operation modes for hybrid AC/DC smart homes, based on two different AC/DC converters in the experimental validation. Full article
(This article belongs to the Special Issue Intelligent Energy Management System for Electric Vehicles)
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