Yipeng Sun | Battery Technology | Research Excellence Award

Assoc. Prof. Dr. Yipeng Sun | Battery Technology | Research Excellence Award

Associate Professor | Eastern Institute of Technology, Ningbo | China

Assoc. Prof. Dr.Yipeng Sun is a highly cited researcher specializing in advanced energy storage materials, with a strong focus on interfacial engineering for next-generation lithium-ion and solid-state batteries. Professional experience spans academic research leadership and postdoctoral innovation, contributing to high-impact studies on atomic and molecular layer deposition, electrode–electrolyte interfaces, and degradation mechanisms in high-energy-density battery systems. Research interests include durable cathode and anode materials, halide and sulfide electrolytes, synchrotron-based characterization, and scalable surface modification strategies for safe, long-life batteries. Research skills encompass atomic-scale fabrication, electrochemical analysis, in situ diagnostics, and materials design. Awards and honors reflect recognition at national and international levels for research excellence and innovation. Overall, the work demonstrates sustained contributions to battery science and practical energy solutions. He has achieved 3847 Citations 45 Documents 34h-index.

 

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Featured Publications

Ayşe Tuğba Yapıcı | Electric Vehicles | Best Research Article Award

Ms. Ayşe Tuğba Yapıcı | Electric Vehicles | Best Research Article Award

Doctoral Researcher  |  Kocaeli University  |  Turkey

Ms. Ayşe Tuğba Yapıcı is a dedicated doctoral researcher whose academic journey is strongly rooted in cutting-edge technologies for electric vehicles, smart energy systems, and intelligent power electronics. She has cultivated significant professional experience through her active involvement in research addressing real-world problems such as electric vehicle charging optimization, grid-integrated charging infrastructures, and advanced modeling of power converter systems. Throughout her career, she has contributed to impactful scientific studies focusing on induction heating systems, charging time prediction using deep learning, and data-driven forecasting on electric vehicle adoption and infrastructure planning. Her research interests include electric vehicle technologies, charging station design, renewable-integrated smart grids, artificial intelligence–based energy forecasting, machine learning and deep learning applications in power systems, and IoT-enhanced smart mobility frameworks. She possesses strong research skills in Python-based deep learning toolkits, MATLAB/Simulink, DigSilent PowerFactory modeling, statistical evaluation metrics, time-series forecasting, optimization algorithms, and performance analysis of intelligent systems. She has published multiple peer-reviewed articles in international SCI/Scopus-indexed journals, delivering innovative research outcomes that offer comprehensive and practical solutions for the sustainable development of electric transportation. Her research achievements include proposing an intelligent deep learning–based framework for EV charging time prediction, integrating spatial–temporal mobility parameters, and enhancing operational efficiency for fast-charging infrastructures. Her work stands out for its interdisciplinary approach and technological significance, supporting the transition toward cleaner mobility, optimized charging networks, and smart energy management. In addition to research excellence, she continues to contribute to academic and scientific communities through conference participation, collaborations, and knowledge dissemination. She aims to advance secure, intelligent, and scalable charging automation systems that support next-generation autonomous electric mobility. Her long-term vision is to shape energy-efficient transportation ecosystems, reduce environmental impacts, and contribute to the global sustainability agenda through innovation and scientific leadership. She has achieved  3 Citations , 2 Documents,  1 h-index.

Featured Publications

Yapıcı, A. T., & Abut, N. (2025, November 23). An intelligent and secure IoT-based framework for predicting charging and travel duration in autonomous electric taxi systems. Applied Sciences.

Yapıcı, A. T., Abut, N., & Yıldırım, A. (2025, October 27). Estimation of future number of electric vehicles and charging stations: Analysis of Sakarya Province with LSTM, GRU and multiple linear regression approaches. Applied Sciences.

Yapıcı, A. T., & Abut, N. (2025, August 21). Geleceğe yönelik elektrikli araç ve şarj istasyonu sayılarının LSTM ve GRU derin öğrenme yöntemleri kullanılarak tahmin edilmesi: Kocaeli ili örneği. Politeknik Dergisi.

Yapıcı, A. T., Abut, N., & Erfidan, T. (2025, April 11). Comparing the effectiveness of deep learning approaches for charging time prediction in electric vehicles: Kocaeli example. Energies.

Yapıcı, A. T., & Abut, N. (2024, September 15). Elektrikli araç şarj istasyonu konum tasarımında, Digsilent yazılımı kullanılarak Kocaeli Üniversitesi Umuttepe Kampüsü için örnek uygulama. Black Sea Journal of Engineering and Science.

Ayşe Tuğba Yapıcı’s research advances intelligent and sustainable electric mobility by integrating deep learning, smart grid technologies, and IoT-based predictive frameworks to optimize charging infrastructure and energy management. Her work supports the transition toward autonomous electric transportation, reducing environmental impacts, improving urban mobility planning, and contributing to global innovation in smart energy systems. She envisions scalable, reliable, and human-centered smart mobility ecosystems that accelerate the adoption of clean transportation worldwide.