Hong Kong, China

The rapid development of fuel cell electric vehicles (FCEVs) has highlighted the critical importance of optimizing energy management strategies to improve vehicle performance energy efficiency durability and reduce hydrogen consumption and operational costs. However existing approaches often face limitations in real-time applicability adaptability to varying driving conditions and computational efficiency. This paper aims to provide a comprehensive review of the current state of FCEV energy management strategies systematically classifying methods and evaluating their technical principles advantages and practical limitations. Key techniques including optimization-based methods (dynamic programming model predictive control) and machine learning-based approaches (reinforcement learning deep neural networks) are analyzed and compared in terms of energy distribution efficiency computational demand system complexity and real-time performance. The review also addresses emerging technologies such as artificial intelligence vehicle-to-everything (V2X) communication and multi-energy collaborative control. The outcomes highlight the main bottlenecks in current strategies their engineering applicability and potential for improvement. This study provides theoretical guidance and practical reference for the design implementation and advancement of intelligent and adaptive energy management systems in FCEVs contributing to the broader goal of efficient and low-carbon vehicle operation.

Recent advancements in membrane-assisted seawater electrolysis powered by renewable energy offer a sustainable path to green hydrogen production. However its large-scale implementation faces challenges due to slow powerto-hydrogen (P2H) conversion rates. Here we report a modular forward osmosis-water splitting (FOWS) system that integrates a thin-film composite FO membrane for water extraction with alkaline water electrolysis (AWE) denoted as FOWSAWE. This system generates high-purity hydrogen directly from wastewater at a rate of 448 Nm3 day−1 m−2 of membrane area over 14 times faster than the state-of-the-art practice with specific energy consumption as low as 3.96 kWh Nm−3 . The rapid hydrogen production rate results from the utilisation of 1 M potassium hydroxide as a draw solution to extract water from wastewater and as the electrolyte of AWE to split water and produce hydrogen. The current system enables this through the use of a potassium hydroxide-tolerant and hydrophilic FO membrane. The established waterhydrogen balance model can be applied to design modular FO and AWE units to meet demands at various scales from households to cities and from different water sources. The FOWSAWE system is a sustainable and an economical approach for producing hydrogen at a record-high rate directly from wastewater marking a significant leap in P2H practice.

Hydrogen fuel cell electric vehicles (HFCEVs) provide significant environmental benefits. Integrating dual-motor coupling powertrains (DMCPs) further enhances efficiency and dynamic performance. This article proposes an energy management strategy (EMS) for the hydrogen fuel cell/battery/super-capacitor system in an HFCEV with DMCP. Model predictive control (MPC) is adopted as the framework to optimize economic performance defined in this study as the hydrogen consumption cost and fuel cell degradation cost. To improve the prediction horizon and accuracy the torque split ratio for two varying permanent magnet synchronous motors (PMSMs) and the corresponding mode switching rules of the vehicle are initially established. Subsequently a combination of Dynamic Programming (DP) and MPC is selected as the framework utilizing a Dung Beetle Optimizer (DBO)-optimized Bidirectional Long Short-Term Memory (BiLSTM) network to refine the predictive model. Finally comparisons with other predictive models and commonly used control strategies demonstrate that the proposed EMS notably improves economic performance.

Solar-driven water electrolysis has emerged as a prominent technology for the production of green hydrogen facilitated by advancements in both water electrolyzers and solar cells. Nevertheless the majority of integrated solar-to-hydrogen systems still struggle to exceed 20% efficiency particularly in large-scale applications. This limitation arises from suboptimal coupling methodologies and system-level inefficiencies that have rarely been analyzed. To address these challenges this study investigates the fundamental principles of solar hydrogen production and examines key energy losses in photovoltaic-electrolyzer systems. Subsequently it systematically discusses optimization strategies across three dimensions: (1) enhancing photovoltaic (PV) system output under variable irradiance (2) tailoring electrocatalysts and electrolyzer architectures for high-performance operation and (3) minimizing coupling losses through voltage-matching technologies and energy storage devices. Finally we review existing large-scale solar hydrogen infrastructure and propose strategies to overcome barriers related to cost durability and scalability. By integrating material innovation with system engineering this work offers insights to advance solar-powered electrolysis toward industrial applications.