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Yang, K., Zhang, Q., Wang, G., Chen, X. & Li, H. (2024). A New Simulation Framework for Vehicle-to-grid Adoption in Heterogeneous Trade Mechanism Scenarios. In: Energy Proceedings: . Paper presented at 15th International Conference on Applied Energy, ICAE 2023. Doha. 3 December 2023 through 7 December 2023. Scanditale AB, 43
Open this publication in new window or tab >>A New Simulation Framework for Vehicle-to-grid Adoption in Heterogeneous Trade Mechanism Scenarios
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2024 (English)In: Energy Proceedings, Scanditale AB , 2024, Vol. 43Conference paper, Published paper (Refereed)
Abstract [en]

The current vehicle-to-grid (V2G) project pilots generally face the problem of low user participation willingness, mainly due to the lack of detailed consideration of trade mechanisms and incentive policies. To address the potential threat posed by the large-scale application of electric vehicles (EVs) to the power grid system, an analysis of the promotion strategies of V2G technology among EV owners is deemed necessary. In this study, a new simulation framework for V2G adoption and heterogeneous trade mechanism evaluation based on social network theory is constructed. The diffusion process of V2G adoption and charging/discharging behavior is simulated under three trading mechanism scenarios: Time-of-Use (ToU) pricing + fixed service fee (ToU-F), regulated pricing + fixed service fee (Reg-F), and dynamic pricing + fixed service fee (Dyn-F). The research results indicate that (1) In terms of V2G adoption scale, both the Reg-F and Dyn-F scenarios have reached the maximum number of adopters, increasing by 41.8% compared to the ToU-F scenario. The main reason is that the former two trading mechanisms achieve a larger price difference, creating more opportunities for charge and discharge arbitrage. (2) Regarding EV load regulation, the discharge amount of EVs under the Reg-F and Dyn-F scenarios is much higher than that under the ToU-F scenario. The Dyn-F scenario further avoids drastic fluctuations in load. (3) In terms of benefit distribution, only under the Reg-F scenario have both the aggregator and V2G adopters gained higher profits.

Place, publisher, year, edition, pages
Scanditale AB, 2024
Keywords
Electric vehicle, Social network, Trade mechanism, Vehicle-to-grid (V2G)
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-66577 (URN)10.46855/energy-proceedings-11032 (DOI)2-s2.0-85190833243 (Scopus ID)
Conference
15th International Conference on Applied Energy, ICAE 2023. Doha. 3 December 2023 through 7 December 2023
Available from: 2024-05-08 Created: 2024-05-08 Last updated: 2024-05-08Bibliographically approved
Dong, X., Zhao, H., Li, H., Fucucci, G., Zheng, Q. & Pu, J. (2024). A novel design of a metal hydride reactor integrated with phase change material for H2 storage. Applied Energy, 367, Article ID 123321.
Open this publication in new window or tab >>A novel design of a metal hydride reactor integrated with phase change material for H2 storage
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2024 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 367, article id 123321Article in journal (Refereed) Published
Abstract [en]

Using metal hydride for hydrogen storage in stationary applications and for transportation is a promising technology due to its advantages of large hydrogen storage capacity, low pressure and low energy consumption. Combining the metal hydride reactor with PCM is expected to recover the heat generated during the hydrogen absorption and use it for hydrogen desorption, thus improving the energy efficiency of the system. This paper proposes a metal hydride reactor integrated with honeycomb fins and PCM to enhance heat transfer. Based on simulations, the results show that the achieved hydrogen storage capacity is 1.326 wt%, the gravimetric and volumetric storage densities are 0.411% and 14.76 kg of H2 per m3, respectively, and the mean saturated rates are 1.222 × 10−3 g s−1 and 0.773 × 10−3 g s−1 for absorption and desorption processes. Compared with the reactor without fins, the mass and volume of the reactor using honeycomb fins are increased, resulting in a decrease in gravimetric and volumetric storage density, but a increase in reaction rate during hydrogen absorption and desorption processes. Based on this structure, we also propose a honeycomb fin reactor filled with sandwich PCM to further accelerate the heat transfer in the reaction process. Compare to the reactor with PCM only filled on the periphery of the honeycomb fins, the hydrogen absorption and desorption times are shortened by about 86.4% and 81.1%, respectively. In addition, different reactor structures are compared using multiple KPIs to provide relevant suggestions for the reactor optimization. The obtained research results can provide a reference for effective thermal management methods in MH storage systems.

Place, publisher, year, edition, pages
Elsevier Ltd, 2024
Keywords
Honeycomb fins, Hydrogen absorption, Hydrogen desorption, Hydrogen storage, Metal hydride, Phase change material, Desorption, Energy efficiency, Energy utilization, Fins (heat exchange), Heat transfer, Honeycomb structures, Hydrides, Phase change materials, Absorption and desorptions, Desorption process, Honeycomb fin, Hydrogen storage capacities, Metal-hydrides, Novel design, Storage densities, Volumetrics
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:mdh:diva-66616 (URN)10.1016/j.apenergy.2024.123321 (DOI)2-s2.0-85192461845 (Scopus ID)
Available from: 2024-05-15 Created: 2024-05-15 Last updated: 2024-05-15Bibliographically approved
Yao, Y., Chen, Y., Luan, W. & Li, H. (2024). In-situ Observation of Crack Initiation and Propagation in the NCM811 Cathode particles. In: Energy Proceedings: . Paper presented at 15th International Conference on Applied Energy, ICAE 2023. Doha. 3 December 2023 through 7 December 2023. Scanditale AB, 43
Open this publication in new window or tab >>In-situ Observation of Crack Initiation and Propagation in the NCM811 Cathode particles
2024 (English)In: Energy Proceedings, Scanditale AB , 2024, Vol. 43Conference paper, Published paper (Refereed)
Abstract [en]

Layered nickel-rich oxide LiNixCoyMn1-x-yO2 (0.6 ≤x<1) is a highly promising positive electrode material. However, the cycling stability of nickel-rich positive electrode materials is limited by particle fracture and a series of side reactions. A comprehensive understanding of particle cracking mechanisms is paramount for material optimization, but crack initiation and propagation have received limited research attention. This paper uses a quasi in-situ SEM observation method and an in-situ optical microscopy observation method to observe crack evolution in real time. The results show rapid cracking behavior under hazardous operating conditions and cracking during cycling under mild conditions. Center cracks and surface cracks are observed during cycling. The observation methods and these insights into the crack behavior offer theoretical guidance for the structural engineering of NCM cathode particles.

Place, publisher, year, edition, pages
Scanditale AB, 2024
Keywords
crack, in-situ optical microscopy, NCM cathode particle, prolonged cycling, quasi in-situ SEM
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-66567 (URN)10.46855/energy-proceedings-11046 (DOI)2-s2.0-85190886732 (Scopus ID)
Conference
15th International Conference on Applied Energy, ICAE 2023. Doha. 3 December 2023 through 7 December 2023
Available from: 2024-05-14 Created: 2024-05-14 Last updated: 2024-05-14Bibliographically approved
Pan, S., Shi, X., Dong, B., Skvaril, J., Zhang, H., Liang, Y. & Li, H. (2024). Multivariate time series prediction for CO2 concentration and flowrate of flue gas from biomass-fired power plants. Fuel, 359, Article ID 130344.
Open this publication in new window or tab >>Multivariate time series prediction for CO2 concentration and flowrate of flue gas from biomass-fired power plants
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2024 (English)In: Fuel, ISSN 0016-2361, E-ISSN 1873-7153, Vol. 359, article id 130344Article in journal (Refereed) Published
Abstract [en]

Integrating CO2 capture with biomass-fired combined heat and power (bio-CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass, including waste, and the dynamic operation of bio-CHP plants leads to large fluctuations in the flowrate and CO2 concentration of the flue gas (FG), which further affect the operation of post-combustion CO2 capture. To optimize the dynamic operation of CO2 capture, a reliable model to predict the FG flowrate and CO2 concentration in real time is essential. In this paper, a data-driven model based on the Transformer architecture is developed. The model validation shows that the root mean squared error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (PPMCC) of Transformer are 0.3553, 0.0189, and 0.8099 respectively for the prediction of FG flowrate; and 13.137, 0.0318, and 0.8336 respectively for the prediction of CO2 concentration. The potential impact of various meteorological parameters on model accuracy is also assessed by analyzing the Shapley value. It is found that temperature and direct horizontal irradiance (DHI) are the most important factors, which should be selected as input features. In addition, using the near-infrared (NIR) spectral data as input features is also found to be an effective way to improve the prediction accuracy. It can reduce RMSE and MAPE for CO2 concentration from 0.2982 to 0.2887 and 0.0158 to 0.0157 respectively, and RMSE and MAPE for FG flowrate from 4.9854 to 4.7537 and 0.0141 to 0.0121 respectively. The Transformer model is also compared to other models, including long short-term memory network (LSTM) and artificial neural network (ANN), which results show that the Transformer model is superior in predicting complex dynamic patterns and nonlinear relationships.

Keywords
Biomass fired combined heat and power plants, CO2 capture, Flue gas flowrate, CO2 concentration, Transformer model, Deep learning
National Category
Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-64937 (URN)10.1016/j.fuel.2023.130344 (DOI)001125955300001 ()2-s2.0-85178313710 (Scopus ID)
Projects
AI-BECCS
Funder
Swedish Energy AgencySwedish Energy Agency
Available from: 2023-12-05 Created: 2023-12-05 Last updated: 2024-01-24Bibliographically approved
Xiong, R., Li, X., Li, H., Zhu, B. & Avelin, A. (2024). Neural network and physical enable one sensor to estimate the temperature for all cells in the battery pack. Journal of Energy Storage, 80, Article ID 110387.
Open this publication in new window or tab >>Neural network and physical enable one sensor to estimate the temperature for all cells in the battery pack
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2024 (English)In: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 80, article id 110387Article in journal (Refereed) Published
Abstract [en]

The performance of lithium-ion batteries (LIBs) is sensitive to the operating temperature, and the design and operation of battery thermal management systems reply on accurate information of LIBs' temperature. This study proposes a data-driven model based on neural network (NN) for estimating the temperature profile of a LIB module. Only one temperature measurement is needed for the battery module, which can assure a low cost. The method has been tested for battery modules consisting of prismatic and cylindrical batteries. In general, a good accuracy can be observed that the root mean square error (RMSE) of esitmated temperatures is less than 0.8 °C regardless of the different operating conditions, ambient temperatures, and heat dissipation conditions.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Battery energy storage, Lithium-ion battery, Neural network, Temperature estimation, Thermal model, Battery management systems, Battery Pack, Digital storage, Information management, Mean square error, Temperature, Temperature measurement, Battery modules, Battery thermal managements, Design and operations, Neural-networks, Operating temperature, Performance, Lithium-ion batteries
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-65797 (URN)10.1016/j.est.2023.110387 (DOI)001155780900001 ()2-s2.0-85182875892 (Scopus ID)
Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-02-14Bibliographically approved
Xiong, R., Sun, Y., Wang, C., Tian, J., Chen, X., Li, H. & Zhang, Q. (2023). A data-driven method for extracting aging features to accurately predict the battery health. Energy Storage Materials, 57, 460-470
Open this publication in new window or tab >>A data-driven method for extracting aging features to accurately predict the battery health
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2023 (English)In: Energy Storage Materials, ISSN 2405-8289, E-ISSN 2405-8297, Vol. 57, p. 460-470Article in journal (Refereed) Published
Abstract [en]

Data-driven methods have been widely used for estimating the state of health (SOH) of lithium-ion batteries (LiBs). The aging process can be characterized by degrading features. To achieve high accuracy, a novel method combining four algorithms, i.e. the correlation coefficient, least absolute shrinkage and selection operator regression, neighborhood component analysis, and ReliefF algorithm, is proposed to select the most important features, which are derived from the measured and calculated parameters. To demonstrate the effectiveness of the proposed method, it is adopted to estimate the SOH of two types of LiBs: i.e. NCA and LFP batteries. Compared to the case using all features, using the selected features can improve the accuracy of SOH estimation by 63.5% and 71.1% for the NCA and LFP batteries, respectively. The method can also enable the use of data obtained in partial voltage ranges, based on which the minimum root mean square errors on SOH estimation are 1.2% and 1.6% for the studied NCA and LFP batteries, respectively. It demonstrates the capability for onboard applications. 

Place, publisher, year, edition, pages
Elsevier B.V., 2023
Keywords
Battery degradation, Feature selection, Lithium-ion battery, Machine learning, State of health, Battery management systems, Mean square error, Ageing features, Ageing process, Battery health, Data-driven methods, Features selection, High-accuracy, Machine-learning, Novel methods, Lithium-ion batteries
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-62092 (URN)10.1016/j.ensm.2023.02.034 (DOI)000964316900001 ()2-s2.0-85149277928 (Scopus ID)
Available from: 2023-03-15 Created: 2023-03-15 Last updated: 2023-04-19Bibliographically approved
Paris, B., Michas, D., Balafoutis, A. T., Nibbi, L., Skvaril, J., Li, H., . . . Apostolopoulos, N. (2023). A Review of the Current Practices of Bioeconomy Education and Training in the EU. Sustainability, 15(2), Article ID 954.
Open this publication in new window or tab >>A Review of the Current Practices of Bioeconomy Education and Training in the EU
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2023 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 15, no 2, article id 954Article, review/survey (Refereed) Published
Abstract [en]

This study conducts a review of the current practices of bioeconomy education and training in the EU; as well as the associated methodologies; techniques and approaches. In recent years; considerable efforts have been made towards developing appropriate bioeconomy education and training programs in order to support a transition towards a circular bioeconomy. This review separates bioeconomy education approaches along: higher education and academic approaches, vocational education and training (VET) and practical approaches, short-term training and education approaches, and other approaches. A range of training methodologies and techniques and pedagogical approaches are identified. The main commonalities found amongst these approaches are that they are generally problem based and interdisciplinary, and combine academic and experiential. Higher education approaches are generally based on traditional lecture/campus-based formats with some experiential approaches integrated. In contrast, VET approaches often combine academic and practical learning methods while focusing on developing practical skills. A range of short-term courses and other approaches to bioeconomy education are also reviewed.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
bioeconomy, bioeconomy education, bioeconomy learning, higher education, vocational education and training
National Category
Educational Sciences
Identifiers
urn:nbn:se:mdh:diva-61918 (URN)10.3390/su15020954 (DOI)000918839000001 ()
Available from: 2023-02-15 Created: 2023-02-15 Last updated: 2023-02-15Bibliographically approved
Dong, B., Chen, J., Shi, X. & Li, H. (2023). AI-based Dynamic Modelling for CO2 Capture. In: Energy Proceedings: . Paper presented at The International Conference on Energy, Ecology and Environment. , 37
Open this publication in new window or tab >>AI-based Dynamic Modelling for CO2 Capture
2023 (English)In: Energy Proceedings, 2023, Vol. 37Conference paper, Published paper (Refereed)
Abstract [en]

Integrating CO2 capture with biomass/waste fired combined heat and power plants (CHPs) is a promising method to achieve negative emission. However, the use of versatile biomass/waste and dynamic operation of CHPs result in big fluctuations in the flue gas (FG) and heat input to CO2 capture. Dynamic modelling is essential to investigate the interactions between key process parameters in producing the dynamic response of the CO2 capture process. In order to facilitate developing robust control strategies for flexible operation in CO2 capture plants and optimizing the operation of CO2 capture plants, artificial intelligence (AI) models are superior to mechanical models due to the easy implementation into the control and optimization. This paper aims to develop an AI model, Informer, to predict the dynamic responses of MEA based CO2 capture performance from waste-fired CHP plants. Dynamic modelling was first developed in Aspen HYSYS software and validated against the reference. The operation data from the simulated CO2 capture process was then used to develop and verify Informer. The following variables were employed as inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, lean solvent flow rate, heat input to CO2 capture. It was found that Informer could predict CO2 capture rate and energy consumption with the mean absolute percentage error of 6.2% and 2.7% respectively.

Keywords
artificial intelligence (AI), dynamic modelling, bioenergy with carbon capture and storage (BECCS), combined heat and power (CHP) plants, energy consumption
National Category
Environmental Engineering Energy Systems
Identifiers
urn:nbn:se:mdh:diva-66187 (URN)10.46855/energy-proceedings-10770 (DOI)2-s2.0-85190650594 (Scopus ID)
Conference
The International Conference on Energy, Ecology and Environment
Funder
Swedish Energy Agency, 51592-1
Available from: 2024-03-08 Created: 2024-03-08 Last updated: 2024-05-08Bibliographically approved
Bao, Z., Li, J., Bai, X., Xie, C., Chen, Z., Xu, M., . . . Li, H. (2023). An optimal charging scheduling model and algorithm for electric buses. Applied Energy, 332, Article ID 120512.
Open this publication in new window or tab >>An optimal charging scheduling model and algorithm for electric buses
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2023 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 332, article id 120512Article in journal (Refereed) Published
Abstract [en]

Electrification poses a promising low-carbon or even zero-carbon transportation solution, serving as a strategic approach to reducing carbon emissions and promoting carbon neutrality in the transportation sector. Along the transportation electrification pathway, the goal of carbon neutrality can be further accelerated with an increasing amount of electricity being generated from renewable energies. The past decade observed the rapid development of battery technologies and deployment of electricity infrastructure worldwide, fostering transportation electrification to expand from railways to light and then heavy vehicles on roadways. In China, a massive number of electric buses have been employed and operated in dozens of metropolises. An important daily operations issue with these urban electric buses is how to coordinate their charging activities in a cost-effective manner, considering various physical, financial, institutional, and managerial constraints. This paper addresses a general charging scheduling problem for an electric bus fleet operated across multiple bus lines and charging depots and terminals, aiming at finding an optimal set of charging location and time decisions given the available charging windows. The charging windows for each bus are predetermined in terms of its layovers at depots and terminals and each of them is discretized into a number of charging slots with the same time duration. A mixed linear integer programming model with binary charging slot choice and continuous state-of-charge (SOC) variables is constructed for minimizing the total charging cost of the bus fleet subject to individual electricity consumption rates, electricity charging rates, time-based charging windows, battery SOC bounds, time-of-use (TOU) charging tariffs, and station-specific electricity load capacities. A Lagrangian relaxation framework is employed to decouple the joint charging schedule of a bus fleet into a number of independent single-bus charging schedules, which can be efficiently addressed by a bi-criterion dynamic programming algorithm. A real-world regional electric bus fleet of 122 buses in Shanghai, China is selected for validating the effectiveness and practicability of the proposed charging scheduling model and algorithm. The optimization results numerically reveal the impacts of TOU tariffs, station load capacities, charging infrastructure configurations, and battery capacities on the bus system performance as well as individual recharging behaviors, and justify the superior solution efficiency of our algorithm against a state-of-the-art commercial solver. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2023
Keywords
Bi-criterion dynamic programming, Charging scheduling, Charging windows, Electric buses, Electricity load capacity, Time-of-use tariffs, Carbon, Charging (batteries), Cost effectiveness, Dynamic programming, Electric lines, Electric loads, Electric utilities, Fleet operations, Integer programming, Scheduling algorithms, Secondary batteries, Bi-criteria, Bi-criteria dynamic programming, Bus fleets, Charging window, Electric bus, Electricity load, Load capacity
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:mdh:diva-61423 (URN)10.1016/j.apenergy.2022.120512 (DOI)000910935400001 ()2-s2.0-85144328290 (Scopus ID)
Available from: 2023-01-04 Created: 2023-01-04 Last updated: 2023-02-08Bibliographically approved
Xiong, R., Li, H., Yu, Q., Romagnoli, A., Jurasz, J. & Yang, X.-G. -. (2023). Applications of AI in advanced energy storage technologies. Energy and AI, Article ID 100268.
Open this publication in new window or tab >>Applications of AI in advanced energy storage technologies
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2023 (English)In: Energy and AI, ISSN 2666-5468, article id 100268Article in journal, Editorial material (Refereed) Published
Place, publisher, year, edition, pages
Elsevier B.V., 2023
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-62704 (URN)10.1016/j.egyai.2023.100268 (DOI)001146406000001 ()2-s2.0-85159174519 (Scopus ID)
Available from: 2023-05-31 Created: 2023-05-31 Last updated: 2024-01-31Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6279-4446

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