4.7 Review

Artificial intelligence driven hydrogen and battery technologies-A review

Related references

Note: Only part of the references are listed.
Article Engineering, Chemical

Surface modification of TiO2 by adding V2O5 nanocatalytic system for hydrogen generation

Lalitha Gnanasekaran et al.

Summary: The surface modification of titanium-dioxide semiconductor with V2O5 enhanced its visible light activity for hydrogen generation. This research aimed to achieve visible light activity for hydrogen production through water splitting using the synthesized photocatalyst. The TiO2-V2O5 composite exhibited lower band gap, promoting its photocatalytic activity for hydrogen production.

CHEMICAL ENGINEERING RESEARCH & DESIGN (2022)

Review Computer Science, Artificial Intelligence

Artificial intelligence application in a renewable energy-driven desalination system: A critical review

Qian He et al.

Summary: The application of artificial intelligence in seawater desalination with renewable energy is mainly divided into four aspects: expert decision-making, optimization, prediction, and control by sequence. The features of artificial intelligence employed in the design of desalination systems not only realize the maximum of efficiency and minimum of cost, but also release human resources.

ENERGY AND AI (2022)

Article Computer Science, Artificial Intelligence

Generalised diagnostic framework for rapid battery degradation quantification with deep learning

Haijun Ruan et al.

Summary: This study develops a general and rapid degradation diagnostic method using deep learning technology, which can quantify the degradation modes of batteries under different conditions in a very short time without the need for feature engineering. By generating synthetic aging datasets for network training, the training cost and time are significantly reduced. The proposed method achieves a degradation diagnostic error less than 1.22% for three major commercial battery chemistries, demonstrating its high accuracy and fast nature compared to traditional methods. The quantification of degradation modes using discharge/charge data validates the feasibility of this approach in real-world scenarios.

ENERGY AND AI (2022)

Review Materials Science, Composites

Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage

Song-Jeng Huang et al.

Summary: The use of Mg-based compounds in solid-state hydrogen storage shows great potential due to its high availability, low cost, and ease of material understanding. However, finding the suitable material remains a challenge, and various synthetic approaches and artificial intelligence analysis can be applied to improve the performance of Mg-based compounds.

JOURNAL OF COMPOSITES SCIENCE (2021)

Article Energy & Fuels

Recent Progress and Emerging Application Areas for Lithium-Sulfur Battery Technology

Susanne Doerfler et al.

Summary: Electrification is making significant progress in various vehicle sectors, with high gravimetric energy density being a common requirement for extended range and increased payload capacity. Component-level developments and research in Li-S battery technology are essential for meeting the performance requirements of these emerging applications.

ENERGY TECHNOLOGY (2021)

Article Green & Sustainable Science & Technology

Comparative assessment of renewable energy-based hydrogen production methods

H. Ishaq et al.

Summary: The paper investigates three renewable energy based configurations for hydrogen production: solar PV, geothermal power generation, and biomass gasification. It suggests that biomass gasification technique provides higher hydrogen production efficiency compared to geothermal power generation and solar PV systems.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)

Review Green & Sustainable Science & Technology

Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

Tanveer Ahmad et al.

Summary: This study focuses on the use of AI techniques in the energy sector, exploring AI's advantages in solar and hydrogen power generation, supply and demand management control, and recent technological advances. The findings show that AI is becoming a key enabler in enhancing operational performance and efficiency in the energy industry to remain competitive in a cutthroat environment.

JOURNAL OF CLEANER PRODUCTION (2021)

Article Chemistry, Physical

Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement

Zhongbao Wei et al.

Summary: With advancements in technology, smart batteries and management systems have emerged as the new focus. Research is shifting towards smart batteries and smart cell management to address the limitations of traditional BMS. Emerging sensing techniques and system integration innovations present new opportunities and challenges for battery management.

JOURNAL OF POWER SOURCES (2021)

Article Electrochemistry

Perspective-Combining Physics and Machine Learning to Predict Battery Lifetime

Muratahan Aykol et al.

Summary: This article discusses several architectures for integrating physics-based and machine learning models to improve the ability to forecast battery lifetime. The ease of implementation, advantages, limitations, and viability of each architecture are analyzed based on the latest technology in the battery and machine learning fields.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2021)

Review Green & Sustainable Science & Technology

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

Md Mijanur Rahman et al.

Summary: This paper reviews the application of machine learning, particularly artificial neural networks, in predicting time series data, focusing on renewable energy sources. With 80% of global energy coming from fuel or nuclear sources, impending fossil fuel shortages are inevitable and remote areas seek alternative energy solutions. The uncertain nature of renewable resources and the neural network's intelligent processing capabilities have driven their application in renewable energy forecasting.

SUSTAINABILITY (2021)

Article Energy & Fuels

Artificial intelligence to support the integration of variable renewable energy sources to the power system

Pal Boza et al.

Summary: The power sector is increasingly relying on variable renewable energy sources (VRE), but their high integration costs present a key challenge. While artificial intelligence (AI) solutions and data-intensive technologies have the potential to create significant value in the electricity value chain, uncertainties and lack of understanding often hinder investment in these technologies.

APPLIED ENERGY (2021)

Article Energy & Fuels

Impact of renewable energy utilization and artificial intelligence in achieving sustainable development goals

M. A. Hannan et al.

Summary: Many countries aim to achieve 100% renewable energy use by 2050, with insufficient evaluation of its impact on sustainable development goals. Renewable energy has a positive impact on achieving most SDGs, but may also have negative effects on some targets. Artificial intelligence can assist renewable energy in reaching certain sustainable development goals.

ENERGY REPORTS (2021)

Article Chemistry, Physical

Toward Better and Smarter Batteries by Combining AI with Multisensory and Self-Healing Approaches

Tejs Vegge et al.

Summary: Accelerating the development of battery technologies through a holistic infrastructure and autonomous workflows can significantly reduce the time needed for discovery. Future battery technologies with multisensory and self-healing capabilities, integrated with AI and physics-aware machine learning models, aim to predict and prevent potential degradation and failure modes.

ADVANCED ENERGY MATERIALS (2021)

Article Chemistry, Physical

How Machine Learning Will Revolutionize Electrochemical Sciences

Aashutosh Mistry et al.

Summary: The development time of electrochemical systems is limited by the identification of new materials and understanding their electrochemical response. To shorten this cycle, machine learning can be used for data-driven predictions.

ACS ENERGY LETTERS (2021)

Article Multidisciplinary Sciences

Current and future lithium-ion battery manufacturing

Yangtao Liu et al.

Summary: Lithium-ion batteries have seen rapid growth in their application fields and market share in modern society, with significant achievements in material research. However, there is a lag in research on manufacturing processes, leading to hidden issues in production.

ISCIENCE (2021)

Review Engineering, Mechanical

Application of Digital Twin in Smart Battery Management Systems

Wenwen Wang et al.

Summary: The paper explores the application of digital twin in addressing research challenges related to batteries and the development opportunities in the battery field. It also summarizes the development trends and challenges in smart battery management.

CHINESE JOURNAL OF MECHANICAL ENGINEERING (2021)

Review Chemistry, Physical

Machine Learning Boosting the Development of Advanced Lithium Batteries

Yangting Liu et al.

Summary: The application of machine learning in the field of lithium batteries can accelerate the discovery of materials and predict performances, promoting the development of advanced lithium batteries.

SMALL METHODS (2021)

Article Computer Science, Artificial Intelligence

Recovering large-scale battery aging dataset with machine

Xiaopeng Tang et al.

Summary: By combining industrial data with accelerated aging tests, high-quality battery aging datasets can be recovered to improve the assessment of battery aging with up to 90% experimental time saved.

PATTERNS (2021)

Article Computer Science, Artificial Intelligence

Machine learning pipeline for battery state-of-health estimation

Darius Roman et al.

Summary: The study presents a machine learning pipeline for estimating battery capacity fade on 179 cells cycled under various conditions. By utilizing charge voltage and current curves, the pipeline can estimate battery health and achieves good performance under fast-charging protocols. The methodology combines experimental data with machine learning modelling, showcasing the potential for real-time estimation of state of health for critical components.

NATURE MACHINE INTELLIGENCE (2021)

Article Computer Science, Interdisciplinary Applications

Smart battery controller using ANFIS for three phase grid connected PV array system

Mohamed M. Ismail et al.

MATHEMATICS AND COMPUTERS IN SIMULATION (2020)

Article Green & Sustainable Science & Technology

Optimal Investment of Electrolyzers and Seasonal Storages in Hydrogen Supply Chains Incorporated With Renewable Electric Networks

Jiarong Li et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2020)

Article Energy & Fuels

Intelligent optimization methodology of battery pack for electric vehicles: A multidisciplinary perspective

Wei Li et al.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2020)

Editorial Material Nanoscience & Nanotechnology

Machine learning for continuous innovation in battery technologies

Muratahan Aykol et al.

NATURE REVIEWS MATERIALS (2020)

Article Engineering, Manufacturing

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Jorge F. Arinez et al.

JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME (2020)

Article Computer Science, Artificial Intelligence

Distributed computing based on AI algorithms in battery early warning and SoH prediction of the intelligent connected vehicles

Haibo Xiao et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Chemistry, Physical

Machine learning assisted materials design and discovery for rechargeable batteries

Yue Liu et al.

ENERGY STORAGE MATERIALS (2020)

Review Computer Science, Artificial Intelligence

Predicting the state of charge and health of batteries using data-driven machine learning

Man-Fai Ng et al.

NATURE MACHINE INTELLIGENCE (2020)

Article Computer Science, Artificial Intelligence

Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines

Ailing Yang et al.

KNOWLEDGE-BASED SYSTEMS (2019)

Article Energy & Fuels

Deep learning framework to forecast electricity demand

Jatin Bedi et al.

APPLIED ENERGY (2019)

Article Chemistry, Multidisciplinary

Recent Progress in Rechargeable Sodium-Ion Batteries: toward High-Power Applications

Xiangjun Pu et al.

SMALL (2019)

Review Energy & Fuels

A review on battery management system from the modeling efforts to its multiapplication and integration

Ming Shen et al.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2019)

Review Chemistry, Physical

A review study on software-based modeling of hydrogen-fueled solid oxide fuel cells

Babak Ghorbani et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2019)

Editorial Material Multidisciplinary Sciences

Accurate predictions of lithium-ion battery life

Maitane Berecibar

NATURE (2019)

Article Energy & Fuels

The role of renewable energy in the global energy transformation

Dolf Gielen et al.

ENERGY STRATEGY REVIEWS (2019)

Review Chemistry, Physical

Hydrogen Storage for Mobility: A Review

Etienne Rivard et al.

MATERIALS (2019)

Review Energy & Fuels

Hydrogen Fuel Cell Technology for the Sustainable Future of Stationary Applications

Raluca-Andreea Felseghi et al.

ENERGIES (2019)

Review Engineering, Multidisciplinary

Computational intelligence approach for modeling hydrogen production: a review

Sina Faizollahzadeh Ardabili et al.

ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS (2018)

Review Energy & Fuels

A review on bio-hydrogen production technology

Hanxi Wang et al.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2018)

Article Chemistry, Physical

Smart energy solutions with hydrogen options

Ibrahim Dincer et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2018)

Article Chemistry, Physical

Maximum conversion efficiency of hydrogen fuel cells

Y. Haseli

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2018)

Article Multidisciplinary Sciences

Hydrogen storage of Li4&B36 cluster

Jiguang Du et al.

SCIENTIFIC REPORTS (2018)

Article Construction & Building Technology

Predictive artificial neural network models to forecast the seasonal hourly electricity consumption for a University Campus

Jihui Yuan et al.

SUSTAINABLE CITIES AND SOCIETY (2018)

Review Nanoscience & Nanotechnology

Material design and engineering of next-generation flow-battery technologies

Minjoon Park et al.

NATURE REVIEWS MATERIALS (2017)

Article Engineering, Electrical & Electronic

Artificial Intelligence Techniques in Smart Grid and Renewable Energy Systems-Some Example Applications

Bimal K. Bose

PROCEEDINGS OF THE IEEE (2017)

Review Engineering, Electrical & Electronic

Power Electronics, Smart Grid, and Renewable Energy Systems

Bimal K. Bose

PROCEEDINGS OF THE IEEE (2017)

Review Green & Sustainable Science & Technology

Emerging renewable and sustainable energy technologies: State of the art

Akhtar Hussain et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2017)

Review Green & Sustainable Science & Technology

Renewable energy: Present research and future scope of Artificial Intelligence

Sunil Kr. Jha et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2017)

Review Chemistry, Physical

Fuel cell and hydrogen technologies research, development and demonstration activities in Singapore - An update

Siew Hwa Chan et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2016)

Review Chemistry, Physical

The survey of key technologies in hydrogen energy storage

Fan Zhang et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2016)

Review Green & Sustainable Science & Technology

The prospects for hydrogen as an energy carrier: an overview of hydrogen energy and hydrogen energy systems

Marc A. Rosen et al.

ENERGY ECOLOGY AND ENVIRONMENT (2016)

Article Automation & Control Systems

State of charge estimation for Li-ion battery based on model from extreme learning machine

Jiani Du et al.

CONTROL ENGINEERING PRACTICE (2014)

Review Computer Science, Artificial Intelligence

Extreme learning machine and its applications

Shifei Ding et al.

NEURAL COMPUTING & APPLICATIONS (2014)

Review Green & Sustainable Science & Technology

Optimum estimation and forecasting of renewable energy consumption by artificial neural networks

A. Azadeh et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2013)

Review Green & Sustainable Science & Technology

What is the global potential for renewable energy?

Patrick Moriarty et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2012)

Article Agricultural Engineering

Modeling and optimization of fermentative hydrogen production

Kaushik Nath et al.

BIORESOURCE TECHNOLOGY (2011)

Review Green & Sustainable Science & Technology

Optimization methods applied to renewable and sustainable energy: A review

R. Banos et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2011)

Article Chemistry, Physical

On-line fuzzy energy management for hybrid fuel cell systems

S. Caux et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2010)

Article Chemistry, Physical

High power fuel cell simulator based on artificial neural network

Abraham U. Chavez-Ramirez et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2010)

Review Chemistry, Applied

An overview of hydrogen production technologies

J. D. Holladay et al.

CATALYSIS TODAY (2009)

Article Chemistry, Physical

Dynamic neural network controller model of PEM fuel cell system

Mustapha Hatti et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2009)

Article Multidisciplinary Sciences

Battery materials for ultrafast charging and discharging

Byoungwoo Kang et al.

NATURE (2009)

Review Green & Sustainable Science & Technology

Assessment of sustainability indicators for renewable energy technologies

Annette Evans et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2009)

Article Thermodynamics

21st Century's energy: Hydrogen energy system

T. Nejat Veziroglu et al.

ENERGY CONVERSION AND MANAGEMENT (2008)

Article Economics

Hydrogen and fuel cells: Towards a sustainable energy future

P. P. Edwards et al.

ENERGY POLICY (2008)

Article Engineering, Electrical & Electronic

Battery management system based on battery nonlinear dynamics modeling

Antoni Szumanowski et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2008)

Article Chemistry, Physical

Artificial neural networks and neuro-fuzzy inference systems as virtual sensors for hydrogen safety prediction

Vishy Karri et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2008)

Article Chemistry, Physical

Syntrophic co-culture of aerobic Bacillus and anaerobic Clostridium for bio-fuels and bio-hydrogen production

Jui-Jen Chang et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2008)

Article Multidisciplinary Sciences

Hydrogen energy

P. P. Edwards et al.

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2007)

Article Automation & Control Systems

Designing a new generalized battery management system

J Chatzakis et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2003)

Article Chemistry, Physical

Realizing the hydrogen future:: the International Energy Agency's efforts to advance hydrogen energy technologies

CC Elam et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2003)

Review Green & Sustainable Science & Technology

Artificial neural networks in renewable energy systems applications: a review

SA Kalogirou

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2001)

Article Computer Science, Interdisciplinary Applications

Design of CO2 hydrogenation catalyst by an artificial neural network

Y Liu et al.

COMPUTERS & CHEMICAL ENGINEERING (2001)

Article Engineering, Electrical & Electronic

Renewable energy today and tomorrow

SR Bull

PROCEEDINGS OF THE IEEE (2001)

Article Computer Science, Artificial Intelligence

Improvements to the SMO algorithm for SVM regression

SK Shevade et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2000)