Advanced Technology of Smart Battery and Energy Management System of Transportation Electrification

Topic Information

Dear Colleagues,

Environmental pollution and the energy crisis have acted as catalysts for the energy revolution, particularly driving the rapid progression of transportation electrification. Electrochemical energy storage plays a fundamental role as a pivotal component in electric vehicles, boats, aircrafts, etc. Consequently, it is necessary to advance the technology of smart battery and energy management systems in real time to ensure their safe and efficient operation. In light of this, smart algorithms for lithium-ion battery/fuel cell/flow battery control, including battery modeling and state estimation, battery thermal management and fault diagnosis, and optimization of energy management systems, have gradually attracted more attention. This Topic intends to provide a platform to share the latest findings on this subject (either research or review articles).

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

• Battery modeling and state estimation;
• Battery thermal management and fault diagnosis;
• Battery smart charging technology;
• Battery sorting, regrouping, and echelon utilization;
• Optimization of energy management systems.

Dr. Longxing Wu
Dr. Zhiqiang Lyu
Prof. Dr. Renjing Gao
Dr. Muyao Wu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit
Electricity electricity	-	4.8	2020	27.9 Days	CHF 1000	Submit

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Published Papers (3 papers)

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All Energies Sustainability Electricity

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21 pages, 815 KiB  

Open AccessArticle

A Novel Construction Method and Prediction Framework of Periodic Time Series: Application to State of Health Prediction of Lithium-Ion Batteries

by Chunsheng Cui, Guangshu Xia, Chenyu Jia and Jie Wen

Energies 2025, 18(6), 1438; https://doi.org/10.3390/en18061438 - 14 Mar 2025

Viewed by 401

Abstract

Due to the time property of natural phenomena and human activities, time series are very common in our lives. The analysis and study of time series can help us to better understand the world, predict the future and make scientific decisions. Focusing on [...] Read more.

Due to the time property of natural phenomena and human activities, time series are very common in our lives. The analysis and study of time series can help us to better understand the world, predict the future and make scientific decisions. Focusing on time series prediction, in this paper we propose a method of constructing non-periodic time series into periodic time series and design a framework for time series prediction based on the constructed periodic time series. The proposed construction method and prediction framework for the periodic time series are then applied to predict the state of health (SOH) of lithium-ion (Li-ion) batteries. The effectiveness of the proposed approach is verified and evaluated on publicly available datasets from the National Aeronautics and Space Administration (NASA), Ames Prognostics Center of Excellence (PCoE), and Center for Advanced Life Cycle Engineering (CALCE) of University of Maryland. The experimental results show that the early SOH prediction of Li-ion batteries can be improved by at least one order of magnitude on both the NASA and CALCE battery datasets when using the method proposed in this paper. Full article

(This article belongs to the Topic Advanced Technology of Smart Battery and Energy Management System of Transportation Electrification)

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21 pages, 11884 KiB  

Open AccessArticle

The State of Health Estimation of Retired Lithium-Ion Batteries Using a Multi-Input Metabolic Gated Recurrent Unit

by Yu He, Norasage Pattanadech, Kasiean Sukemoke, Minling Pan and Lin Chen

Energies 2025, 18(5), 1035; https://doi.org/10.3390/en18051035 - 20 Feb 2025

Cited by 1 | Viewed by 467

Abstract

With the increasing adoption of lithium-ion batteries in energy storage systems, accurately monitoring the State of Health (SoH) of retired batteries has become a pivotal technology for ensuring their safe utilization and maximizing their economic value. In response to this need, this paper [...] Read more.

With the increasing adoption of lithium-ion batteries in energy storage systems, accurately monitoring the State of Health (SoH) of retired batteries has become a pivotal technology for ensuring their safe utilization and maximizing their economic value. In response to this need, this paper presents a highly efficient estimation model based on the multi-input metabolic gated recurrent unit (MM-GRU). The model leverages constant-current charging time, charging current area, and the 1800 s voltage drop as input features and dynamically updates these features through a metabolic mechanism. It requires only four cycles of historical data to reliably predict the SoH of subsequent cycles. Experimental validation conducted on retired Samsung and Panasonic battery cells and packs under constant-current and dynamic operating conditions demonstrates that the MM-GRU model effectively tracks SoH degradation trajectories, achieving a root mean square error of less than 1.2% and a mean absolute error of less than 1%. Compared to traditional machine learning algorithms such as SVM, BPNN, and GRU, the MM-GRU model delivers superior estimation accuracy and generalization performance. The findings suggest that the MM-GRU model not only significantly enhances the breadth and precision of SoH monitoring for retired batteries but also offers robust technical support for their safe deployment and asset optimization in energy storage systems. Full article

(This article belongs to the Topic Advanced Technology of Smart Battery and Energy Management System of Transportation Electrification)

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21 pages, 6620 KiB  

Open AccessArticle

Prediction of Short-Term Solar Irradiance Using the ProbSparse Attention Mechanism for a Sustainable Energy Development Strategy

by Zhenyuan Zhuang, Huaizhi Wang and Cilong Yu

Sustainability 2025, 17(3), 1075; https://doi.org/10.3390/su17031075 - 28 Jan 2025

Viewed by 994

Abstract

Sustainability refers to a development approach that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. Solar energy is an inexhaustible and renewable resource. From the perspective of resource utilization, solar power generation [...] Read more.

Sustainability refers to a development approach that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. Solar energy is an inexhaustible and renewable resource. From the perspective of resource utilization, solar power generation has a high degree of sustainability. Therefore, solar power generation is one of the most important ways to transform the energy structure and promote the sustainable development of the economy and society, and it is of great significance for promoting the construction of a resource-conserving and environmentally friendly society. However, solar energy resources also exhibit strong unpredictability; therefore, this paper proposes a novel artificial intelligence (AI) model for short-term solar irradiance prediction in photovoltaic power generation. Leveraging the ProbSparse attention mechanism within an encoder-decoder architecture, the AI model efficiently captures both short- and long-term dependencies in the input sequence. The dingo algorithm is innovatively redesigned to optimize the hyperparameters of the proposed AI model, enhancing model convergence. Data preprocessing involves feature selection based on mutual information, multiple imputations for data cleaning, and median filtering. Evaluation metrics include the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The proposed AI model demonstrates improved efficiency and robust performance in solar irradiance prediction, contributing to advancements in energy management for electrical power and energy systems. Full article

(This article belongs to the Topic Advanced Technology of Smart Battery and Energy Management System of Transportation Electrification)

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