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
Electricity electricity	2.7	4.4	2020	25.8 Days	CHF 1200	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit

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Published Papers (7 papers)

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All Energies Sustainability Electricity

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36 pages, 7055 KB  

Open AccessArticle

Multi-Feature Coordinated Adaptive ECMS with Fuzzy Logic for Low-Carbon Sustainable Fuel Cell Hybrid Electric Commercial Vehicles

by Xuening Zhang, Xiaodong Liu, Juan Du, Xiaorui Li and Xintian Jiang

Sustainability 2026, 18(13), 6729; https://doi.org/10.3390/su18136729 - 2 Jul 2026

Viewed by 96

Abstract

This paper introduces a multi-feature coordinated adaptive equivalent consumption minimization strategy (MFCA-ECMS) using fuzzy logic control (FLC) to enhance hydrogen efficiency in fuel cell hybrid electric commercial vehicles (FCHECVs) and extend the lifespan of the fuel cell system (FCS), contributing to sustainable, low-carbon [...] Read more.

This paper introduces a multi-feature coordinated adaptive equivalent consumption minimization strategy (MFCA-ECMS) using fuzzy logic control (FLC) to enhance hydrogen efficiency in fuel cell hybrid electric commercial vehicles (FCHECVs) and extend the lifespan of the fuel cell system (FCS), contributing to sustainable, low-carbon transport. First, a baseline ECMS model is established for the FCHECV, whilst the optimal equivalent factor (EF) is determined using a multi-island genetic algorithm (MIGA) based on representative driving cycles. Second, an adaptive EF framework is developed to overcome the inherent limitation of conventional ECMS—its reliance on a fixed EF—by dynamically integrating three operational features: variation in the battery’s state of charge (SOC), the rate of change in the FCS’s output power, and fluctuations in vehicle power demand. Third, feature-specific adaptive weights are assigned and updated in real time using a fuzzy inference system to regulate the EF online, incorporating multiple features. Simulations are conducted under different initial SOC levels ($90\%$ and $45\%$) across different driving cycles. The results demonstrate that the MFCA-ECMS consistently reduces hydrogen consumption (HC). Compared to the charge-depleting and charge-sustaining (CD-CS) strategy, it achieves HC reductions of $17.98\%$ on the stochastic driving cycle (Random-C) and $18.73\%$ on the urban dynamometer driving schedule (UDDS), outperforming both CD-CS and conventional ECMS in all tested scenarios. Furthermore, the MFCA-ECMS actively suppresses FCS power fluctuations. Regardless of the initial SOC, the proportion of power change rates within the reasonable range exceeds $97\%$, thereby contributing to extending the FCS lifespan. This reduces emissions and operating costs, enabling sustainable hydrogen-powered commercial vehicle deployment. Full article

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

20 pages, 2203 KB  

Open AccessArticle

A Simulated Annealing Approach for Electric Vehicle Routing with Time Windows

by Hanane El Hila, Fatima Bouyahia, Jaouad Boukachour and Abdelouahed Tajer

Sustainability 2026, 18(12), 6319; https://doi.org/10.3390/su18126319 - 19 Jun 2026

Viewed by 381

Abstract

Emerging economies face mounting pressure to adopt sustainable and cost-efficient methods for delivering products and services in urban areas. This study examines the Electric Vehicle Routing Problem with Time Windows (EVRPTW) within a pragmatic urban context. We concentrate on the short-haul delivery network [...] Read more.

Emerging economies face mounting pressure to adopt sustainable and cost-efficient methods for delivering products and services in urban areas. This study examines the Electric Vehicle Routing Problem with Time Windows (EVRPTW) within a pragmatic urban context. We concentrate on the short-haul delivery network in Marrakesh, Morocco, whose operational viability is influenced by climatic, infrastructural, and regulatory limitations. We present a simulated annealing (SA) metaheuristic, augmented with repair heuristics and a penalty-based cost function, to concurrently reduce routing costs and lateness fines, subject to time-window and battery capacity restrictions. The technique undergoes evaluation through extensive computer tests utilizing realistic instance sets that replicate local demand patterns and charging infrastructure. The penalty-calibrated model demonstrates delivery completion rates of up to 100%, significantly reducing route costs and the number of unserved clients relative to baseline setups. We thoroughly analyze the tuning parameters among several runs. This study intends to provide a useful tool for real-world decision support by fusing extensive literature synthesis with local context validation and by integrating a simulation module that evaluates time-window settings and charging patterns under realistic traffic. 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, 1375 KB  

Open AccessArticle

Multi-Objective BESS Siting and Sizing via NSGA-II and PTDF-Constrained DC Optimal Power Flow: Application to the Mali Transmission Network

by Adrián Alarcón Becerra, Gregorio Fernández, Aritz Rubio Egaña, Francesco Roncallo, Mario Mihetec, Alberto Júlio Tsamba, Nikola Matak and Gilberto Mahumane

Electricity 2026, 7(2), 57; https://doi.org/10.3390/electricity7020057 - 18 Jun 2026

Viewed by 206

Abstract

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied [...] Read more.

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs NSGA-II to simultaneously maximize daily price arbitrage revenue and minimize active power losses; the lower level solves a network-constrained DC optimal power flow with thermal branch limits enforced as hard linear inequalities via the Power Transfer Distribution Factor (PTDF) matrix. Over 500 generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving a maximum daily revenue of approximately €10,033 at a marginal loss increment of $6.7\times {10}^{-3}$ MWh. The resulting Pareto front gives Mali system planners a quantitative tool for trading off private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is technically tractable and economically viable in the resource-constrained context of sub-Saharan energy transitions. Full article

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

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16 pages, 2104 KB  

Open AccessArticle

Evaluation and Comparison of Multi-Power Source Coupling Technologies for Vehicles Based on Driving Dynamics

by Haoyi Zhang, Hong Tan, Linjie Ren and Xinglong Liu

Sustainability 2026, 18(2), 602; https://doi.org/10.3390/su18020602 - 7 Jan 2026

Viewed by 485

Abstract

With the growing consumer demand for enhanced driving dynamics in vehicles, optimizing powertrain configurations to balance performance, energy efficiency, and cost has become a critical challenge. Traditional internal combustion engine vehicles (ICEVs) suffer from significant energy consumption and cost penalties when improving acceleration [...] Read more.

With the growing consumer demand for enhanced driving dynamics in vehicles, optimizing powertrain configurations to balance performance, energy efficiency, and cost has become a critical challenge. Traditional internal combustion engine vehicles (ICEVs) suffer from significant energy consumption and cost penalties when improving acceleration performance. This study systematically evaluates the trade-offs between dynamic performance, energy consumption, and direct manufacturing costs across six powertrain configurations: ICEV, 48 V mild hybrid (48 V), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), range-extended electric vehicle (REV), and battery electric vehicle (BEV). By developing a comprehensive parameterized model, we quantify the impacts of acceleration improvement on vehicle mass, energy consumption, and costs. Key findings reveal that electrified powertrains (PHEV, REV, BEV) exhibit superior cost-effectiveness and energy efficiency. For instance, improving 0–100 km/h acceleration time from 9 to 5 s reduces direct manufacturing costs by only 5.72% for BEV versus 13.38% for ICEV, while PHEV achieves a balanced compromise with 3.40% lower fuel consumption and 10.43% cost increase compared to conventional counterparts. Mechanistic analysis attributes these advantages to higher power density of electric motors and simplified energy transmission in electrified systems. This work provides data-driven insights for consumers and automakers to prioritize powertrain technologies under dynamic performance requirements, highlighting PHEV with driving range of 50 km as the optimal choice for harmonizing driving experience, energy economy, and affordability. The results of this study assist automakers in optimizing the technology pathways of vehicle powertrain, within the consumer demand for dynamic performance. This plays a crucial role in advancing the automotive industry’s overall fuel consumption and energy consumption, thereby contributing to sustainable development. 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, 815 KB  

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

Cited by 3 | Viewed by 1232

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 KB  

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 4 | Viewed by 1292

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 KB  

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

Cited by 4 | Viewed by 2380

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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