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| 1 | Review on supercapacitors:Technologies and performance evaluation显示文摘The development of electrochemical capacitors(i.e.supercapacitors)have attracted a lot of attention in recent years because of the increasing demand for efficient,high-power energy storage.Electrochemical capacitors(ECs)are particularly attractive for transportation and renewable energy generation applications,taking advantage of their superior power capability and outstanding cycle life.Over the past decade,various advanced electrode materials and cell design are being studied to improve the energy density of ECs.Hybrid Li-ion capacitors and pseudo-capacitors that utilize fast surface redox reactions of metal oxide and doped polymers are the prime candidates being considered.This paper is concerned with the metrics being used to describe the performance of ECs and how the metrics are evaluated by testing devices and how the data from the testing are best interpreted.Emphasize is on relating testing of advanced ECs using materials more complex than activated carbons to testing electric double-layer capacitors(EDLCs)using carbon in both electrodes.A second focus of the paper is projecting the potential of the advanced materials and ionic liquid electrolytes for the development of complete EC cells having an energy density more than a factor of ten greater the energy density of the EDLC devices currently on the market.This potential was evaluated by calculating the performance(energy and power)of a series of ECs that utilize the advanced materials that have been studied by electrochemists over the past 10-15 years.The capacitance and resistance of the advanced ECs were calculated utilizing specific capacitance(F/g or F/cm^(3))and porosity data for the electrode materials and ionic conductivity of the electrolytes.It was concluded that hybrid ECs can be developed with energy densities of at least 50 Wh/kg,70 Wh/L with efficient power greater than 3 k W/kg.Continued research on micro-porous carbons with specific capacitance of 200 F/g and greater is needed.to achieve these EC performance goals. | Jingyuan Zhao Andrew F.Burke | 2021 | Journal of Energy Chemistry2021，30，8： | 2 |
| 2 | Measuring patent assessment quality-Analyzing the degree and kind of (in) consistency in patent ofrices' decision making显示文摘 | Paul F.Burke Markus Reitzig | | 0，，36： | 1 |
| 3 | Specialized deep neural networks for battery health prognostics:Opportunities and challenges显示文摘Lithium-ion batteries are key drivers of the renewable energy revolution,bolstered by progress in battery design,modelling,and management.Yet,achieving high-performance battery health prognostics is a significant challenge.With the availability of open data and software,coupled with automated simulations,deep learning has become an integral component of battery health prognostics.We offer a comprehensive overview of potential deep learning techniques specifically designed for modeling and forecasting the dynamics of multiphysics and multiscale battery systems.Following this,we provide a concise summary of publicly available lithium-ion battery test and cycle datasets.By providing illustrative examples,we emphasize the efficacy of five techniques capable of enhancing deep learning for accurate battery state prediction and health-focused management.Each of these techniques offers unique benefits.(1)Transformer models address challenges using self-attention mechanisms and positional encoding methods.(2) Transfer learning improves learning tasks within a target domain by leveraging knowledge from a source domain.(3) Physics-informed learning uses prior knowledge to enhance learning algorithms.(4)Generative adversarial networks(GANs) earn praise for their ability to generate diverse and high-quality outputs,exhibiting outstanding performance with complex datasets.(5) Deep reinforcement learning enables an agent to make optimal decisions through continuous interactions with its environment,thus maximizing cumulative rewards.In this Review,we highlight examples that employ these techniques for battery health prognostics,summarizing both their challenges and opportunities.These methodologies offer promising prospects for researchers and industry professionals,enabling the creation of specialized network architectures that autonomously extract features,especially for long-range spatial-temporal connections across extended timescales.The outcomes could include improved accuracy,faster training,and enhanced generalization. | Jingyuan Zhao Xuebing Han Minggao Ouyang Andrew F.Burke | 2023 | Journal of Energy Chemistry2023，，12： | 0 |
| 4 | Battery prognostics and health management for electric vehicles under industry 4.0显示文摘Transportation electrification is essential for decarbonizing transport. Currently, lithium-ion batteries are the primary power source for electric vehicles (EVs). However, there is still a significant journey ahead before EVs can establish themselves as the dominant force in the global automotive market. Concerns such as range anxiety, battery aging, and safety issues remain significant challenges. | Jingyuan Zhao Andrew F.Burke | 2023 | Journal of Energy Chemistry2023，，9： | 0 |
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