Ching Chuen Chan

Energy materials development remains experimentally intensive because relevant knowledge is dispersed across heterogeneous sources, material formulas and processing parameters are strongly coupled, and experimental data are often incomplete. Although artificial intelligence (AI), including large language models (LLMs), can support knowledge extraction, materials design, experiment planning and data analysis, these capabilities often operate as isolated tools rather than as a computational–experimental loop. This Perspective proposes a conceptual 4E framework—comprising Extraction, Execution, Evolution, and a Generative AI (GAI) Engine—for energy Materials Intelligence and Manufacturing. Extraction converts literature, experimental records, and multimodal data into structured knowledge. Execution translates encoded recipes into machine-executable protocols for robotic synthesis and characterization while returning real-time feedback. Evolution uses robotic experimental feedback to evaluate a domain-specific Recipe Language Model (RLM), which subsequently recommends new material recipes. The GAI Engine coordinates the RLM, materials database, robotic platform, characterization systems, and digital twin within a closed-loop architecture. The framework is further extended conceptually towards intelligent manufacturing through operational data, process monitoring, quality control, and staged translation from laboratory validation to pilot and industrial operation. The 4E framework therefore provides a pathway, grounded in experimentally demonstrated platforms, that links knowledge, formulas and parameters, robotic execution and feedback-driven learning for Materials Intelligence and Manufacturing.

Against the backdrop of the accelerating global low-carbon transition, the mismatch between intermittent renewable energy generation and inflexible energy demand has become a major bottleneck for sustainable energy development. To address this challenge, this paper proposes an AI-driven 4N4F (Four Networks and Four Flows) multi-network convergence system. The paper first clarifies the conceptual connotation and theoretical foundation of 4N4F integration and then systematically constructs an overall technical architecture built on three core pillars: intelligent connected electric vehicles as mobile energy nodes, intelligent networked building-integrated photovoltaics as stationary energy carriers, and an integrated energy management system as the global coordination hub. The effectiveness of the proposed framework is examined through representative engineering cases covering commercial buildings, hybrid energy storage, off-grid microgrids, and sustainable campuses. The results demonstrate that the 4N4F system breaks down traditional silos among energy, information, transportation, and human networks, significantly improving renewable energy utilization and overall energy efficiency. These findings highlight the framework’s scientific contribution as a new technical paradigm for modern energy-system construction and sustainable development.

Driven by global energy transformation and the progress of artificial intelligence technology, traditional automotive engineering is undergoing profound changes. Transportation is rapidly advancing toward electrification and intelligence. Against this background, this paper identifies three emerging paradigms for the development of electric vehicles: Heart Revolution, Brain Evolution, and Network Integration. This paper points out that automobiles are evolving from traditional one-way energy consumers to dynamic energy nodes in smart grids. With the support of artificial intelligence technology, the role of automobiles is also shifting from a simple means of transportation to an intelligent mobile terminal. At the same time, this paper focuses on analyzing the application of the integration theory of “Four Networks and Four Flows” in automobile upgrading. The theory does not focus on the optimization of a single node unit but emphasizes a systematic perspective to improve overall performance and support sustainable development. This paper suggests that the development of the automobile industry must be deeply integrated with the humanity world, information world and physical world. By building a five-in-one architecture of “Human–Vehicle–Road–Cloud–Satellite”, the automobile industry could follow a practical pathway toward coordinated development. At the same time, breakthroughs in core technologies such as solid-state batteries and wide-bandgap semiconductors are also imminent. This paper aims to provide a sustainable and high-performance automobile development path and integrate the concept of human-oriented design into it. Meanwhile, China’s new energy vehicle industry is used as a representative context to illustrate its engineering and industrial implementation.

Torque ripple mitigation is a critical topic in the field of permanent magnet machine drives, and current harmonic injection is regarded as an effective approach to address this issue. However, traditional harmonic injection methods heavily rely on model-based calculations that necessitate various precise motor equivalent parameters. Additionally, they struggle to account for the iron loss effect. Furthermore, due to the nonlinear nature of motor parameters, these approaches frequently result in suboptimal torque ripple mitigation and elevated injection losses. To overcome these limitations, this article proposes a data-driven-based harmonic injection method. In contrast to model-based techniques, the proposed method offers the advantages of independence from motor parameters, unaffected torque ripple reduction by magnetic saturation, and overall minimization of injection copper and iron losses. The key of the proposed method lies in establishing precise correlations between injected current harmonic, torque ripple, and losses through a meta-model. Moreover, a multiobjective optimization process is applied to identify the optimal injection currents, leading to minimizations in torque ripple and injection losses.

Electric vehicles (EVs) are one of the most important technological innovations that can save the environment. Over the years, there has been substantial EV research, which has been successfully transformed into EV products, leading to the recent commercialization and popularization of EVs. Nevertheless, the translation of EV research into EV education is lagging behind the technology transfer from EV research to EV products and is quite ad hoc in nature. In this paper, an overview of translating EV research into EV education is presented, which is systematically categorized into individual EV education, classroom EV education and professional EV education. Then, relevant surveys are conducted and discussed. Finally, some findings and suggestions are given to enhance the translation of EV research into EV education.

In this paper, an axial-field flux-modulated (AFFM) machine with multiple permanent-magnet (PM) excitations for electric vehicle applications is investigated. This study addresses the torque limitation of axial-field rotor-PM machines by investigating the strategic placement of stator PMs with specific magnetization directions, rather than solely relying on increased rotor PM quantity which becomes ineffective beyond certain thresholds. Then, taking axial-field magnetic gears with a gear ratio of 19/5 as the foundation, the evolution processes into corresponding stator-PM and rotor-PM axial-field motors are explored. The physical structures and necessary magnetic coupling conditions of these two topologies are discussed in detail to enhance the performance of the proposed topology. Then machine characteristics, including the flux linkage, back-EMF, torque capability, self-and mutual-inductance, power factor and cogging torque are researched, wherein the torque output of the proposed one is comparable to the sum of stator-PM and rotorPM motors. The effects of specific parameters influenced by the assembly process are also investigated. To demonstrate the competitiveness, the proposed machine was compared against four regular AFFMPM topologies, achieving advancements in torque and PM utilization aspects. Finally, a prototype is manufactured and tested, verifying the FE predictions.

A new graduate degree programme, Master of Science in Electric Vehicles (MScEV), for engineering students is presented, which is timely and vital for modern society. The purpose of this programme is to provide graduate students with up-to-date knowledge and skills that can enhance their career prospects in the fast-growing electric vehicle (EV) community. The programme not only provides technological knowledge in system design, operation, and management of EVs, but also involves research training in specific EV topics. This paper first outlines the rationale of the programme and reveals the shortcomings of existing EV education. Then, the curriculum structure of the newly developed MScEV programme as well as the corresponding core and elective courses are discussed. Finally, the findings of this programme are evaluated, indicating that the programme is attractive to an overwhelming number of students from diverse engineering backgrounds, as evidenced by the applicants’ and admittees’ degree qualifications and work experiences.

Due to flux modulation (FM) effect, vernier machines (VMs) achieve high torque density, marking them as promising for variable speed applications. However, their practical implementation is hindered by the challenge of a low power factor (PF), which leads to the poor generation power quality, the increased converter capacity, and the limited overload capability. This paper offers a comprehensive review, summarizing relevant emerging technologies aimed at breaking through the limitation. It delves into the mechanisms, calculation methods, and optimal design strategies for PF from three core angles: the excitation source, the modulator, and the filter. By examining these pertinent technologies, the main findings reveal that optimizing the three elements can reduce leakage flux and enhance the working harmonic ratio of VMs, thereby improving the PF of VMs. By examining pertinent technologies, this paper outlines a strategic roadmap and aims to spark future research endeavors to overcome the PF challenge in VMs.

Modular parallel inverter technology can enhance the power level and redundancy of wireless power transfer (WPT) systems, contributing to standardized production. It serves as an effective method for realizing high-power systems. However, inappropriate selection of compensation components can negatively affect the system’s efficiency, power factor, and the flexibility of modularization. This paper analyzes two key properties of modular-parallel-inverter WPT (MPI-WPT) systems: module number flexibility and modular deviation suppression. Firstly, to assess the modular deviation suppression of the system, its definition and calculation method are provided. Secondly, the expansion condition to achieve modular flexibility is examined, highlighting that the modular system needs to be a fully resonant system. Subsequently, an expansion design methodology from a single WPT system to an MPI-WPT system is proposed, ensuring the preservation of the original properties of the system during its extension. Finally, the parallel characteristics of MPI-WPT systems were experimentally verified.

The metaverse gradually evolves into a virtual world containing a series of interconnected sub-metaverses. Diverse digital resources, including identities, contents, services, and supporting data, are key components of the sub-metaverse. Therefore, a Domain Name System (DNS)-like system is necessary for efficient management and resolution. However, the legacy DNS was designed with security vulnerabilities and trust risks due to centralized issues. Blockchain is used to mitigate these concerns due to its decentralized features. Additionally, it supports identity management as a default feature, making it a natural fit for the metaverse. While there are several DNS alternatives based on the blockchain, they either manage only a single type of identifiers or isolate identities from other sorts of identifiers, making it difficult for sub-metaverses to coexist and connect with each other. This article proposes a M ulti- I dentifier management and resolution S ystem (MIS) in the metaverse, supporting the registration, resolution, and inter-translation functions. The basic MIS is portrayed as a four-tier architecture on a consortium blockchain due to its manageability, enhanced security, and efficiency properties. On-chain data is lightweight and compressed to save on storage while accelerating reading and writing operations. The resource data is encrypted based on the attributes of the sub-metaverse in the storage tier for privacy protection and access control. For users with decentralization priorities, a modification named EMIS is built on top of Ethereum. Finally, MIS is implemented on two testbeds and is available online as the open-source system. The first testbed consists of 4 physical servers located in the UK and Malaysia while the second is made up of 200 virtual machines (VMs) spread over 26 countries across all 5 continents on Google Cloud. Experiments indicate that MIS provides efficient reading and writing performance than the legacy DNS and other public blockchain-based workarounds including EMIS and Ethereum Name Service (ENS).