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Energies
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Modeling and Optimization Control of Power Battery
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Modeling and Optimization Control of Power Battery

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 9721

Special Issue Editors

Dr. Qi Zhang

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan 250061, China
Interests: electric vehicle; electrochemical energy storage system; battery system; battery management system; lithium-ion battery
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bin Duan

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan 250061, China
Interests: power electronics; battery technology; modeling; the optimal control of complex nonlinear systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yunlong Shang

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan 250061, China
Interests: power battery modeling and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As important energy-storage components, batteries have been widely used in portable electronics, electric vehicles, smart grids, electrical transportations, etc. According to research data from Research and Markets, the global lithium-ion battery market is worth about 40.5 billion U.S. dollars in 2020. It is estimated that the global lithium-ion battery market will exceed USD 100 billion in 2025. The modeling and optimization control of power batteries has been an emerging and challenging research topic in recent years. The battery management system (BMS) is essential for the safe, reliable and efficient operation of power batteries. The key technologies of BMSs include battery modelling, state estimation, life prediction, fault prognosis and diagnosis, balancing control, heating technology at low temperatures, etc. The development of efficient, reliable, and low-cost intelligent BMSs can effectively facilitate batteries’ operation in highly efficient regions, and significantly improve the fault-tolerant characteristics of the system.

This Special Issue will provide a platform for presenting the latest research results on technology development for the modeling and optimization control of power batteries. We welcome research papers about theoretical, methodological and empirical studies, as well as review papers, that provide critical overviews on the state of the art of technologies. Manuscripts from cross-disciplinary fields, such as battery electrochemistry, power electronics, and control technology, as well as algorithmic and hardware design, that can provide timely and effective solutions for emerging challenges in the modeling and optimization control of power batteries, are strongly encouraged.

The topics of interest for this Special Issue include but are not limited to:

  • Battery mechanism analysis;
  • Battery modeling and state estimation;
  • Battery remaining-life prediction;
  • Battery-fault prognosis and diagnosis;
  • Battery balancing control;
  • Battery optimization and charging control;
  • Battery heating technology at low temperatures;
  • Battery electrochemistry;
  • Other related topics.

Dr. Qi Zhang
Prof. Dr. Bin Duan
Prof. Dr. Yunlong Shang
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. Energies 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 2600 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

  • electric vehicles
  • power batteries
  • battery energy-storage system
  • battery-management system (BMS)
  • modeling and state estimation
  • remaining-life prediction
  • fault diagnosis
  • battery equalization
  • heating

Published Papers (5 papers)

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Research

18 pages, 5514 KiB  
Article
Three Voltage Vector Duty Cycle Optimization Strategy of the Permanent Magnet Synchronous Motor Driving System for New Energy Electric Vehicles Based on Finite Set Model Predictive Control
by Chi Zhang, Binyue Xu, Jasronita Jasni, Mohd Amran Mohd Radzi, Norhafiz Azis and Qi Zhang
Energies 2023, 16(6), 2684; https://0-doi-org.brum.beds.ac.uk/10.3390/en16062684 - 13 Mar 2023
Cited by 1 | Viewed by 1504
Abstract
Faced with the increasingly serious energy crisis and environmental pollution problems, traditional internal combustion engine vehicles are receiving more and more resistance, which has rapidly promoted the development of new energy electric vehicles. Permanent magnet synchronous motors are widely used in new energy [...] Read more.
Faced with the increasingly serious energy crisis and environmental pollution problems, traditional internal combustion engine vehicles are receiving more and more resistance, which has rapidly promoted the development of new energy electric vehicles. Permanent magnet synchronous motors are widely used in new energy electric vehicles and in other fields because of their simple structure, light weight, small size, and high power density. With the continuous advancement of production technology, the requirements of accuracy, rapidity, and stability in permanent magnet synchronous motor systems have gradually increased. Among many advanced control technologies, this paper proposes an optimized model predictive torque control strategy based on voltage vector expansion. This strategy involves the construction of a reference stator flux linkage vector based on the analytical relationship between electromagnetic torque, reference stator flux linkage amplitude, and rotor flux linkage and the transfer of the separate control of electromagnetic torque and flux linkage amplitude into flux linkage vector control. At the same time, the optimal duty cycle corresponding to the two adjacent extended voltage vectors and the zero vector is calculated according to geometric relationships so as to realize the three voltage vector duty cycle optimization control. Experimental results show the effectiveness and superiority of the proposed strategy. Full article
(This article belongs to the Special Issue Modeling and Optimization Control of Power Battery)
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24 pages, 9847 KiB  
Article
Model Control and Digital Implementation of the Three Phase Interleaved Parallel Bidirectional Buck–Boost Converter for New Energy Electric Vehicles
by Chi Zhang, Binyue Xu, Jasronita Jasni, Mohd Amran Mohd Radzi, Norhafiz Azis and Qi Zhang
Energies 2022, 15(19), 7178; https://0-doi-org.brum.beds.ac.uk/10.3390/en15197178 - 29 Sep 2022
Cited by 4 | Viewed by 1871
Abstract
In recent years, the imminent environmental problems and increasing attention to the global energy crisis have prompted the need for new opportunities and technologies to meet higher demands for clean and sustainable energy systems. As a result, new energy electric vehicles have been [...] Read more.
In recent years, the imminent environmental problems and increasing attention to the global energy crisis have prompted the need for new opportunities and technologies to meet higher demands for clean and sustainable energy systems. As a result, new energy electric vehicles have been developed to replace fossil fuel cars. Therefore, this paper presents a three-phase interleaved parallel bidirectional buck–boost converter, which is the core factor of electrical energy flow regulation and management between the battery pack and motor drive inverter within the high voltage direct current bus and converts the voltage from two directions. Corresponding circuit topology, mathematical model, and control strategy are analyzed in three operation states: charge buck, discharge boost, and electric energy interaction modes. The digital implementation with double closed loop, power feedforward compensation, and bidirectional switching logic are realized by XDPTM Digital Power Controllers XDPP1100-Q040 of Infineon Technologies AG. Finally, the experimental results of the proposed converter clearly show that it achieves the objectives, namely, the feasibility and practicality of the system. Full article
(This article belongs to the Special Issue Modeling and Optimization Control of Power Battery)
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25 pages, 10351 KiB  
Article
Improved Whale Optimization Algorithm Based on Hybrid Strategy and Its Application in Location Selection for Electric Vehicle Charging Stations
by Yongjing Li, Wenhui Pei and Qi Zhang
Energies 2022, 15(19), 7035; https://0-doi-org.brum.beds.ac.uk/10.3390/en15197035 - 25 Sep 2022
Cited by 5 | Viewed by 1449
Abstract
The charging station location model is a nonlinear programming model with complex constraints. In order to solve the problems of weak search ability and low solution accuracy of the whale optimization algorithm (WOA) in solving location models or high-dimensional problems, this paper proposes [...] Read more.
The charging station location model is a nonlinear programming model with complex constraints. In order to solve the problems of weak search ability and low solution accuracy of the whale optimization algorithm (WOA) in solving location models or high-dimensional problems, this paper proposes an improved whale optimization algorithm (IWOA) based on hybrid strategies. Chaos mapping and reverse learning mechanism are introduced in the original algorithm, and the change mode of convergence factor and probability threshold is improved. Through optimization experiments on 18 benchmark functions, the test results show that IWOA has the best solution ability. Finally, IWOA is used to solve a site selection optimization model aiming at the minimum comprehensive cost. The results show that the proposed algorithm and model can effectively reduce the comprehensive cost of site selection. This provides a necessary decision-making reference for the scientific site selection for electric vehicle charging stations. Full article
(This article belongs to the Special Issue Modeling and Optimization Control of Power Battery)
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20 pages, 3423 KiB  
Article
Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm
by Shuo Sun, Qianli Zhang, Junzhong Sun, Wei Cai, Zhiyong Zhou, Zhanlu Yang and Zongliang Wang
Energies 2022, 15(16), 5842; https://0-doi-org.brum.beds.ac.uk/10.3390/en15165842 - 11 Aug 2022
Cited by 7 | Viewed by 1450
Abstract
Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost [...] Read more.
Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model. Full article
(This article belongs to the Special Issue Modeling and Optimization Control of Power Battery)
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16 pages, 3588 KiB  
Article
Optimal Planning of Electric Vehicle Charging Stations Considering User Satisfaction and Charging Convenience
by Di Xu, Wenhui Pei and Qi Zhang
Energies 2022, 15(14), 5027; https://0-doi-org.brum.beds.ac.uk/10.3390/en15145027 - 09 Jul 2022
Cited by 19 | Viewed by 2039
Abstract
To solve the problem of layout design of charging stations in the early stage of the electric vehicle industry, the user’s satisfaction and the charging convenience are considered. An electric vehicle charging station site-selection model is established based on the kernel density analysis [...] Read more.
To solve the problem of layout design of charging stations in the early stage of the electric vehicle industry, the user’s satisfaction and the charging convenience are considered. An electric vehicle charging station site-selection model is established based on the kernel density analysis of the urban population. The goal of this model is maximum electric vehicle user satisfaction and the highest charging convenience. Then, according to model characteristics, the immune algorithm is designed and optimized to solve the model. The optimization of the immune algorithm includes two aspects. On the one aspect, judging that the stop condition is added in the mutation link. On the other aspect, two mutation operators are designed in the optimized immune algorithm. Finally, the simulation example is determined by a three-step method in Jinan City. The results show that the electric vehicle charging station site-selection model in this paper can better meet user needs compared with traditional models. Compared with the traditional immune algorithm, the convergence speed of the optimized immune algorithm is improved, and the proposed algorithm is superior to the traditional immune algorithm in terms of stability and accuracy. Full article
(This article belongs to the Special Issue Modeling and Optimization Control of Power Battery)
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