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Dr Zhe Liu | 刘哲博士

Professor | 教授 - 西北工业大学材料学院
Northwestern Polytechnical University (NPU) /Massachusetts Institute of Technology (MIT) | 西北工业大学材料学院 / 美国麻省理工学院
Zhe Liu photo

Dr Zhe Liu | 刘哲博士

Professor | 教授 - 西北工业大学材料学院
Northwestern Polytechnical University (NPU) /Massachusetts Institute of Technology (MIT) | 西北工业大学材料学院 / 美国麻省理工学院

Biography

Dr. Zhe Liu completed his bachelor’s degree (2012) and Ph.D. degree (2016) in Electrical and Computer Engineering at National University of Singapore (NUS). After graduation, he continued his postdoctoral research at Massachusetts Institute of Technology (MIT) from 2017 to 2019 and obtained Research Scientist position at MIT and Singapore-MIT Alliance for Research and Technology (SMART) from 2019 to 2021. He recently joined Northwestern Polytechnical University (NPU) in Xi’an, Shaanxi Province in China as a professor in Materials Science and Engineering. Dr. Liu has published 35+ journal articles and conference papers, delivered 10+ talks at international conferences, and filed two U.S. patents. He is known for conducting industrially relevant research and tackle high-priority challenges in PV manufacturing. He is currently serving in the technical program committee of the IEEE PVSC and being a guest editor for scientific journals (e.g., IEEE JPV).

报告人简介:刘哲,2012年和2016年在新加坡国大学取得了电子工程专业学士、博士学位。曾在美国麻省理工学院担任博士后研究员和研究员(2017-2020年);在新加坡-麻省理工学院联合研究中心担任研究员(2020-2021年)。2021年2月,加入西北工业大学材料学院, 任教授。在光伏工程领域已发表学术论文35篇,主要研究包括利用人工智能方法的创新,突破传统材料研究速度慢、周期长的瓶颈,开展“机器学习方法加速光伏材料优化”的研究。曾先后获得了麻省理工学院道达尔能源学者、陕西省人才计划青年项目、西北工业大学翱翔海外学者等。目前担任美国IEEE光伏专家大会光伏组件领域主席(2022年)、IEEE J Photovolt等期刊客座编委。

Topic: Innovative Opportunities for Next Generation PV Technologies: Thin Silicon Wafer and Tandem Solar Cells
题目: 新一代光伏技术的创新机遇——薄硅片技术及叠层太阳能电池的前瞻

Photovoltaics (PV) technology has been advanced so rapidly in the past decade by both academic and industrial research efforts. Now, solar PV has become the cheapest source for electricity generation in many parts of the world. However, to facilitate the carbon-neutral goal in mid-century, there is a continuous driving force to reduce the levelized cost of electricity (LCOE). In this talk, we discuss the innovative opportunities in two emerging areas of PV devices: thin silicon wafer solar cells, and perovskite-silicon tandem solar cells. Thinning silicon wafer has been known as one of the most effective levers for cost reduction. However, significantly technological challenges are still present during manufacturing. In the first part, we revisit the benefits of utilizing thinner wafers via technoeconomic modeling. We discuss some of the technological innovations required to make thin wafer manufacturing possible. Specifically, we introduce some of our recent research on microcrack detection in the Si wafers during manufacturing and the low-stress interconnection design for thin-wafer solar cells in modules. Beyond single-junction silicon wafers, the perovskite-Si tandem technologies have been deemed as a promising next-generation technology. It could boost the device efficiency beyond 30%, and possibly achieve lower LCOE of the PV systems. Given the pressing challenges of the climate change, the development of perovskite solar cells to commercialization needs to be accelerated. In the second part, we discuss the opportunities of machine learning tools that could help the R&D on perovskite stability and manufacturing scale-up. Particularly, we demonstrate two examples of leveraging the active machine learning approach to accelerate the materials and process optimization for perovskites.

在过去十年中,基于学术界和工业界的共同努力,光伏技术取得了迅猛的发展。如今,在世界上的许多地区,太阳能光伏已经成为最便宜的发电技术。随着“碳达峰,碳中和”目标的提出,意味着大规模广泛布局光伏电站的能源需求,要求着光伏技术持续降低光伏系统电力均等化成本(LCOE)。在本次报告中,我们将聚焦讨论光伏器件领域的两个创新机会:薄硅片太阳能电池和钙钛矿硅串联太阳能电池。首先,降低硅片厚度一直被认为是降低成本最有效的手段之一,然而利用超薄的硅片(如小于100 mm)的制造技术工艺仍然存在重大的技术挑战。在第一部分中,我们通过技术经济模型重温利用薄硅片的成本优势和技术优点,并讨论在薄硅片光伏制造中所需的一些技术创新。其中将着重探讨我们近年来研发的一些工程技术研究,包括在硅片制造过程中的微裂纹检测技术和太阳能电池组件中的低应力互连设计。展望未来,更高效的光伏器件则能够有效降低光伏发电系统LCOE,其中钙钛矿-硅叠层电池有望实现器件效率超过30%,被认为是具有产业化前景的高效率光伏技术之一。因此,我们将在第二部分主要讨论利用机器学习工具来加速钙钛矿稳定性和大面积制造技术的研发。通过介绍采用机器学习方法进行钙钛矿材料表征和优化的案例,深入探讨机器学习在新一代光伏技术中的应用,以及机器学习加速光伏叠层电池研发的前景。

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