Intelligent Modeling, Predictive Control, and Decision-Making for Multi-Energy Transportation Systems

Topic Information

Dear Colleagues,

The global transition toward sustainable mobility is fundamentally reshaping transportation systems across all domains. Green marine vessels, electric vehicles, electrified railways, and next-generation aircraft are increasingly reliant on complex multi-energy power systems that integrate fuel cells, batteries, supercapacitors, and other novel energy sources. This paradigm shift introduces unprecedented challenges in system dynamics, durability, and operational efficiency. The core of these challenges lies in the cooperative modeling, prediction, control, and decision-making for these heterogeneous systems under real-world constraints.

Key issues such as the coupled degradation of fuel cells and batteries, accurate forecasting of highly dynamic propulsion loads, and the distributed coordination of multi-energy power systems demand solutions that go beyond traditional siloed approaches. The integration of data-driven artificial intelligence (AI) with physics-based models offers a transformative pathway. Techniques ranging from deep learning for state prediction and reinforcement learning for adaptive control to digital twins for system-level validation are proving critical for developing robust, efficient, and intelligent next-generation transportation platforms.

This Topic aims to compile cutting-edge research and comprehensive reviews that address the intersection of advanced informatics and multi-energy transportation systems engineering. We seek contributions that present novel methodologies, theoretical frameworks, high-fidelity simulations, and experimental validations focused on enhancing the performance, longevity, safety, and intelligence of electric and hybrid-electric transportation. We encourage submissions that demonstrate a system’s perspective, fostering cross-pollination of ideas between maritime, automotive, rail, and aerospace applications.

Areas of interest for this Topic include, but are not limited to, the following:

Detailed modeling and digital twin development for fuel cells and batteries; coupled degradation modeling, state-of-health (SOH) estimation, and remaining useful life (RUL) prediction for hybrid energy storage systems; physics-informed machine learning and hybrid models for vehicle and vessel dynamics, aerodynamics, and hydrodynamics; short-term and long-term forecasting of driving cycles, speed, torque, and power demand using AI; uncertainty quantification and robust forecasting for critical components in transportation power systems; distributed and decentralized cooperative control strategies for multi-stack fuel cell systems and multi-pack battery storage systems; adaptive and model predictive control for integrated power and propulsion management in ships, aircraft, and trains; reinforcement learning and deep reinforcement learning for real-time energy management strategies; robust and fault-tolerant control schemes to ensure system safety under component failure or extreme conditions; multi-objective optimization of energy consumption, component degradation, and trip time for complex transportation tasks; AI-powered energy and power routing algorithms for marine and aerospace hybrid electric systems; vehicle-to-grid (V2G) decision-making for coordinated charging, grid support, and resource pooling; hierarchical and federated learning architectures for collaborative learning across transportation fleets while preserving data privacy; task planning and re-planning under uncertainty for autonomous electric vehicles and vessels; cybersecurity and resilience strategies for the cyber–physical systems of connected electric transportation; co-simulation and hardware-in-the-loop (HIL) testing platforms for validating control and management strategies; lightweight AI algorithm deployment on embedded systems for real-time onboard decision-making; lifecycle analysis and techno-economic modeling of advanced multi-energy transportation systems; real-time simulation and testing platform architecture for multi-energy controller validation and performance evaluation; intelligent decision support system for fault detection and emergency operation modes.

We invite researchers and practitioners from academia, national laboratories, and industry to submit their original work to this Topic. By bringing together diverse perspectives, this collection aims to chart the course for the intelligent, efficient, and reliable multi-energy transportation systems of the future.

Dr. Yuji Zeng
Dr. Qinjin Zhang
Dr. Tianhao Qie
Dr. Xin Wang
Prof. Dr. Herbert Ho-Ching Iu
Dr. Grzegorz Karoń
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Automation automation	2.9	4.5	2020	24.8 Days	CHF 1200	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Eng eng	3.5	4.1	2020	18.8 Days	CHF 1400	Submit
Future Transportation futuretransp	2.5	3.4	2021	14.6 Days	CHF 1200	Submit
Vehicles vehicles	3.2	5.5	2019	19.7 Days	CHF 1800	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (2 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Energies Eng Future Transportation Vehicles Automation

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

35 pages, 36338 KB  

Open AccessArticle

Pumping Power Reduction in Crude-Oil Pipeline Transportation: CFD Validation and Kolmogorov–Arnold Network Surrogate Modelling

by Fazeel Ahmad, Georgios E. Stavroulakis, Amir H. Mohammadi and David Lokhat

Eng 2026, 7(9), 453; https://doi.org/10.3390/eng7090453 - 4 Sep 2026

Viewed by 399

Abstract

Precise prediction of pressure drop and drag-reduction performance is essential for improving the hydraulic efficiency and reducing the energy demand of crude-oil pipeline transportations. Therefore, this study aims to develop an integrated computational fluid dynamics (CFD)–machine learning (ML) framework for predicting pressure drop [...] Read more.

Precise prediction of pressure drop and drag-reduction performance is essential for improving the hydraulic efficiency and reducing the energy demand of crude-oil pipeline transportations. Therefore, this study aims to develop an integrated computational fluid dynamics (CFD)–machine learning (ML) framework for predicting pressure drop (∆p), drag reduction (DR), pumping power reduction (PPR), energy savings (ES), and flow-rate enhancement (Q) in turbulent crude-oil pipeline flow containing drag-reducing agents (DRAs). The investigated system considers the effect of pipeline length (L), diameter (D), surface roughness (ε), operating temperature (T), and DRA concentration (25–200 ppm). The Reynolds-average Navier–Stokes equations (RANS) were solved using the shear stress transport (SST) k-ω turbulence model approaching near-wall resolution of y⁺ ≈ 1 for DRA3 at 20 ppm. The CFD modelling was first used to validate an experimental benchmark and subsequently used to expand the available dataset over the investigated operating conditions. The combined experimental–CFD dataset was then employed to develop a multi-output Kolmogorov–Arnold network (KAN) surrogate model. The proposed framework predicted DR up to 44.2%, PPR of approximately 55 W, ES of 30%, and flow-rate enhancement up to 5–10(${\displaystyle \frac{\mathrm{L}}{\mathrm{d}\mathrm{a}\mathrm{y}}}$). The KAN model effectively captured the nonlinear relationships among DRA characteristics, pipeline geometry, and operating conditions, achieving R² = 0.9318 for PPR prediction. The novelty of the proposed work lies in integrating a validated, near-wall-resolved SST k-ω CFD model with a multi-output KAN surrogate model, combining physics-based flow analysis with rapid data-driven prediction of hydraulic and energy-performance indicators. Full article

(This article belongs to the Topic Intelligent Modeling, Predictive Control, and Decision-Making for Multi-Energy Transportation Systems)

►▼ Show Figures

Figure 1

27 pages, 7511 KB  

Open AccessArticle

From Prediction to Decision: A Unified Dual-Stage LLM-Driven Framework for Intelligent Energy Management with Unstructured Information

by Yong Chen, Guo Chen and Fang Yao

Energies 2026, 19(16), 3935; https://doi.org/10.3390/en19163935 - 21 Aug 2026

Viewed by 235

Abstract

Modern energy systems, including those supporting transportation electrification, are increasingly exposed to volatile market conditions and external events. Effective decision-making therefore requires the integration of structured operational data with unstructured contextual information. Existing studies on Large Language Model (LLM)-assisted energy systems have mainly [...] Read more.

Modern energy systems, including those supporting transportation electrification, are increasingly exposed to volatile market conditions and external events. Effective decision-making therefore requires the integration of structured operational data with unstructured contextual information. Existing studies on Large Language Model (LLM)-assisted energy systems have mainly applied LLMs to individual tasks such as forecasting, scheduling, or decision support, while forecasting and control are typically treated separately. As a result, semantic information extracted from external events is not consistently propagated from market prediction to operational decision-making. This paper proposes a unified dual-stage framework in which the LLM functions as a shared semantic information processor, converting raw event data into structured representations used by both forecasting and control modules. In the forecasting stage, these representations improve price prediction under non-stationary conditions. In the control stage, the same information provides an event-aware contextual action prior for reinforcement learning-based energy management. This design allows external event information to inform both future-state estimation and subsequent control decisions, establishing a consistent connection between prediction and decision-making. The framework is evaluated using real-world electricity market data and a battery energy management environment. The results show that the proposed framework achieves the highest average cumulative reward among the evaluated methods while maintaining greater robustness than the forecasting-only LLM configuration. Overall, this work demonstrates the benefit of consistently propagating structured semantic information across forecasting and control and provides a viable approach to event-aware intelligent energy management, with potential extensions to multi-energy transportation systems and electrified mobility applications. Full article

(This article belongs to the Topic Intelligent Modeling, Predictive Control, and Decision-Making for Multi-Energy Transportation Systems)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-2