Overview of Integrated Packaging Single-Cell Technology for Hydrogen Proton Exchange Membrane Fuel Cells

2026-04-10T08:34:44+00:00

Proton exchange membrane fuel cells (PEMFCs) are gaining significant traction as a promising clean energy technology due to their high efficiency and low-temperature operation. Especially, the integrated single-cell technology is beginning to become the future trend in system applications. This paper provides a systematic review of the technological innovations and design optimizations in membrane electrodes, bipolar plates, and overall packaging for single-cell. It critically analyzes the advantages and limitations of current single-cell solutions from the perspectives of cost, performance, and durability. It provides theoretical support for the engineering application and large-scale production of PEMFC single-cell technology.They shall not contain displayed mathematical equations, numbered reference citations, nor footnotes. They should include three or four different keywords or phrases, as this will help readers to find it. It is important to avoid over-repetition of such phrases as this can result in a page being rejected by search engines. Ensure that your abstract reads well and is grammatically correct.

Performance comparison of PI and AI-based controllers for solar PV-fed fast electric vehicle battery charging systems

2026-04-17T07:18:24+00:00

The rapid growth of electric vehicles (EVs) has created a strong demand for efficient and fast charging solutions. However, conventional charging methods are time-consuming and place significant stress on the power grid when deployed on large scale. To address these challenges, this study proposes a standalone solar photovoltaic (PV)-based DC microgrid for fast EV charging. The system is designed to regulate charging using a DC-DC boost converter controlled by two strategies: a conventional Proportional-Integral (PI) controller and an Artificial Neural Network (ANN)-based controller. A detailed simulation model is developed in MATLAB/Simulink, including PV system parameters, converter specifications, and a lithium-ion battery modeled using a Thevenin equivalent circuit. The ANN controller is trained using real-time operating conditions such as irradiance, temperature, and state of charge (SoC). Performance is evaluated based on transient response, overshoot, settling time, steady-state error, and total harmonic distortion (THD). Results show that the ANN controller significantly improves system performance. Voltage overshoot is reduced from 10% to 2%, current overshoot from 20% to 4%, and THD from 6.8% to 2.1%. Additionally, the settling time is improved by approximately 57% compared to the PI controller. These findings demonstrate that AI-based control strategies provide a more efficient, stable, and reliable solution for renewable energy-based EV charging systems. The ANN controller reduced voltage overshoot from 10% to 2%, current overshoot from 20% to 4%, and THD from 6.8% to 2.1%, while improving settling time by up to 57%.

Energy Storage and Conversion

Overview of Integrated Packaging Single-Cell Technology for Hydrogen Proton Exchange Membrane Fuel Cells

Performance comparison of PI and AI-based controllers for solar PV-fed fast electric vehicle battery charging systems