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Chirumalla, K., Kulkov, I., Parida, V., Dahlquist, E., Johansson, G. & Stefan, I. (2024). Enabling battery circularity: Unlocking circular business model archetypes and collaboration forms in the electric vehicle battery ecosystem. Technological forecasting & social change, 199, Article ID 123044.
Open this publication in new window or tab >>Enabling battery circularity: Unlocking circular business model archetypes and collaboration forms in the electric vehicle battery ecosystem
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2024 (English)In: Technological forecasting & social change, ISSN 0040-1625, E-ISSN 1873-5509, Vol. 199, article id 123044Article in journal (Refereed) Published
Abstract [en]

Achieving battery circularity is crucial for meeting the targets of net-zero emission vehicles by 2030 and enabling climate-neutral transportation by 2050. To facilitate this transition, firms operating in the electric vehicle (EV) battery ecosystem must reassess their value creation, capture, and delivery methods. Although EV battery second life presents a promising solution for circularity, many vehicle manufacturers and stakeholders in the battery ecosystem struggle to adapt their organizations internally and externally due to a lack of insights into suitable circular business models. The purpose of this study is to identify viable archetypes of circular business models for EV battery second life and examine their implications on company collaborations within the EV battery ecosystem. Three main archetypes of circular business models are identified (i.e., extending, sharing, and looping business models) and further divided into eight sub-archetypes. These models are elucidated in terms of key business model dimensions, including value proposition, value co-creation, value delivery, and value capture. The paper provides visual representations of the necessary interactions and collaborations among companies in the EV battery ecosystem to effectively implement the proposed business model archetypes. This research contributes to the theory of circular business models in general, with specific relevance to EV battery circularity.

Place, publisher, year, edition, pages
Elsevier Inc., 2024
Keywords
Battery second life, Business model innovation, Circular economy, Climate neutrality, EV batteries, Second life applications, Climate models, Electric vehicles, Secondary batteries, Business models, Electric vehicle batteries, Second Life, Second life application, Value delivery, business development, electric vehicle, emission control, environmental policy, innovation, theoretical study, Ecosystems
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-65148 (URN)10.1016/j.techfore.2023.123044 (DOI)001132740900001 ()2-s2.0-85179128201 (Scopus ID)
Available from: 2023-12-21 Created: 2023-12-21 Last updated: 2024-01-17Bibliographically approved
Chen, H., Dahlquist, E. & Kyprianidis, K. (2024). Retrofitting Biomass Combined Heat and Power Plant for Biofuel Production-A Detailed Techno-Economic Analysis. Energies, 17(2), Article ID 522.
Open this publication in new window or tab >>Retrofitting Biomass Combined Heat and Power Plant for Biofuel Production-A Detailed Techno-Economic Analysis
2024 (English)In: Energies, E-ISSN 1996-1073, Vol. 17, no 2, article id 522Article in journal (Refereed) Published
Abstract [en]

Existing combined heat and power plants usually operate on part-load conditions during low heating demand seasons. Similarly, there are boilers designated for winter use that remain inactive for much of the year. This brings a concern about the inefficiency of resource utilization. Retrofitting existing CHP plants (especially for those with spare boilers) for biofuel production could increase revenue and enhance resource efficiency. This study introduces a novel approach that combines biomass gasification and pyrolysis in a polygeneration process that is based on utilizing existing CHP facilities to produce biomethane, bio-oil, and hydrogen. In this work, a detailed analysis was undertaken of retrofitting an existing biomass combined heat and power plant for biofuel production. The biofuel production plant is designed to explore the polygeneration of hydrogen, biomethane, and bio-oil via the integration of gasification, pyrolysis, and renewable-powered electrolysis. An Aspen Plus model of the proposed biofuel production plant is established followed by a performance investigation of the biofuel production plant under various design conditions. An economic analysis is carried out to examine the profitability of the proposed polygeneration system. Results show that the proposed polygeneration system can achieve 40% carbon efficiency with a payback period of 9 years and an internal rate of return of 17.5%, without the integration of renewable hydrogen. When integrated with renewable-power electrolysis, the carbon efficiency could be significantly improved to approximately 90%; however, the high investment cost associated with the electrolyzer system makes this integration economically unfavorable.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
biofuel, biomass, existing CHP plants, process modeling, techno-economic analysis
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-65948 (URN)10.3390/en17020522 (DOI)001151936200001 ()2-s2.0-85183319309 (Scopus ID)
Available from: 2024-02-07 Created: 2024-02-07 Last updated: 2024-02-07Bibliographically approved
Shabani, M., Wallin, F., Dahlquist, E. & Yan, J. (2024). Smart and optimization-based operation scheduling strategies for maximizing battery profitability and longevity in grid-connected application. Energy Conversion and Management: X, 21, Article ID 100519.
Open this publication in new window or tab >>Smart and optimization-based operation scheduling strategies for maximizing battery profitability and longevity in grid-connected application
2024 (English)In: Energy Conversion and Management: X, ISSN 2590-1745, Vol. 21, article id 100519Article in journal (Refereed) Published
Abstract [en]

Lithium-ion battery storage has emerged as a promising solution for various energy systems. However, complex degradation behavior, relatively short lifetime, high capital, and operational costs, and electricity market volatility are critical factors that challenge its practical viability. Thus, to ensure sustained profitability of Lithium-ion batteries in real-life applications, a smart and optimal management strategy considering key influencing factors is imperative for achieving efficient battery utilization. This study proposes two day-ahead battery-behavior-aware operation scheduling strategies to maximize profitability and longevity in residential grid-connected applications with dynamic electricity pricing. Each scenario employs unique approaches to make optimal decisions for optimal battery utilization. The first scenario optimizes short-term profitability by prioritizing revenue gains under three charge/discharge rates (high, moderate, low), considering daily charge and discharge timings as decision variables. Conversely, the second scenario proposes a smart strategy capable of making intelligent decisions on a wide range of variables to simultaneously maximize revenue and minimize degradation costs, ensuring short-term and long-term profitability. Decision variables include the cycle frequency for each specific day, timings as well as durations for charging and discharging per cycle. To ensure effective long-term assessment, both scenarios accurately estimate battery performance, calendric and cyclic capacity degradations, remaining-useful-lifetime, and internal states under real operational conditions until battery reaches its end-of-life criteria. The scenarios are assessed economically using various indicators. Furthermore, the impact of battery price and size on optimization outcomes are examined. The key findings indicate that, among the first set of scenarios, the strategy with low charge/discharge rate extends the battery lifetime most efficiently, estimated at 14.8 years. However, it proved to be the least profitable, resulting in negative profit of −3€/kWh/yr. On the other hand, strategies with high and moderate charge/discharge rates resulted in positive profit of 8.3 €/kWh/year and 9.2 €/kWh/year, despite having shorter battery lifetimes, estimated at 10.1 years and 13.6 years, respectively. Furthermore, from a payback perspective, the strategy with fast charge/discharge capability led to 1.5 years shorter payback period than that of the moderate rate strategy. The findings highlight that the first set of scenarios limits the strategy's flexibility in achieving both sustainability and profitability. In contrast, the second scenario achieves impressive profit (18 €/kWh/yr), shortest payback period (7.5 year), a commendable lifespan (12.5 years), contrasting revenue-focused scenarios, emphasizing the importance of striking optimal balance between revenue gain and degradation costs for charging/discharging actions, ensuring sustained profitability. The findings offer valuable insights for decision-makers, enabling informed strategic choices and effective solutions.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Day-ahead optimization-based battery operation scheduling, Degradation cost minimization, Price arbitrage within real-time electricity price tariff, Residential-grid connected battery application, Revenue maximization, Sustained profitability optimization, Battery management systems, Charging (batteries), Costs, Decision making, Housing, Investments, Lithium-ion batteries, Power markets, Battery applications, Battery operation, Cost minimization, Day-ahead, Electricity prices, Grid-connected, Operations scheduling, Optimisations, Real- time, Profitability
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-65372 (URN)10.1016/j.ecmx.2023.100519 (DOI)001155504000001 ()2-s2.0-85181971282 (Scopus ID)
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-02-14Bibliographically approved
Dahlquist, E., Wallin, F., Chirumalla, K., Toorajipour, R. & Johansson, G. (2023). Balancing Power in Sweden Using Different Renewable Resources, Varying Prices, and Storages Like Batteries in a Resilient Energy System. Energies, 16(12), 4734-4734
Open this publication in new window or tab >>Balancing Power in Sweden Using Different Renewable Resources, Varying Prices, and Storages Like Batteries in a Resilient Energy System
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2023 (English)In: Energies, E-ISSN 1996-1073, Vol. 16, no 12, p. 4734-4734Article in journal (Refereed) Published
Abstract [en]

In this paper, balancing electricity production using renewable energy such as wind power, PV cells, hydropower, and CHP (combined heat and power) with biomass is carried out in relation to electricity consumption in primarily one major region in Sweden, SE-3, which contains 75% of the country's population. The time perspective is hours and days. Statistics with respect to power production and consumption are analyzed and used as input for power-balance calculations. How long periods are with low or high production, as well as the energy for charge and discharge that is needed to maintain a generally constant power production, is analyzed. One conclusion is that if the difference in production were to be completely covered with battery capacity it would be expensive, but if a large part of the difference were met by a shifting load it would be possible to cover the rest with battery storage in an economical way. To enhance the economy with battery storage, second-life batteries are proposed to reduce the capital cost in particular. Batteries are compared to hydrogen as an energy carrier. The efficiency of a battery system is higher than that of hydrogen plus fuel cells, but in general much fewer precious materials are needed with an H-2/fuel-cell system than with batteries. The paper discusses how to make the energy system more robust and resilient.

Keywords
electric power, balancing, batteries, load shift, resilience
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-63867 (URN)10.3390/en16124734 (DOI)001017053600001 ()2-s2.0-85163894100 (Scopus ID)
Available from: 2023-07-13 Created: 2023-07-13 Last updated: 2023-08-28Bibliographically approved
Martinsen, M., Fentaye, A. D., Dahlquist, E. & Zhou, Y. (2023). Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks. Machines, 11(10), Article ID 940.
Open this publication in new window or tab >>Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks
2023 (English)In: Machines, E-ISSN 2075-1702, Vol. 11, no 10, article id 940Article in journal (Refereed) Published
Abstract [en]

In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in mining operations must have meticulous approaches and uncompromised security. By ensuring a secure transition, the mining industry can navigate the transformative shift towards autonomy while upholding the highest standards of safety and operational reliability. Experiments involving autonomous pathways for mining machinery that utilize AI for route optimization demonstrate a higher speed capacity than manually operated approaches; this translates to enhanced productivity, subsequently fostering increased production capacity to meet the rising demand for metals. Nonetheless, accelerated wear on crucial elements like tires, brakes, and bearings on mining machines has been observed. Autonomous mining processes will require smarter machines without humans that guide and support actions prior to a hazardous situation occurring. This paper will delve into a comprehensive perspective on the safety of autonomous mining machines by using Bayesian networks (BN) to detect possible hazard fires. The BN is tuned with a combination of empirical field data and laboratory data. Various faults have been recognized, and their correlation with the measurements has been established.

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI), 2023
Keywords
artificial intelligence, autonomous, bayesian networks, machine learning, mining machines, predictive maintenance, safety, smart sensing
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-64701 (URN)10.3390/machines11100940 (DOI)001093749100001 ()2-s2.0-85175038225 (Scopus ID)
Available from: 2023-11-09 Created: 2023-11-09 Last updated: 2023-11-15Bibliographically approved
Toorajipour, R., Chirumalla, K., Johansson, G., Dahlquist, E. & Wallin, F. (2023). Implementing circular business models for electric vehicle battery second life: Challenges and enablers from an ecosystem perspective.
Open this publication in new window or tab >>Implementing circular business models for electric vehicle battery second life: Challenges and enablers from an ecosystem perspective
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2023 (English)Manuscript (preprint) (Other academic)
Keywords
EV batteries;
National Category
Business Administration Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Innovation and Design
Identifiers
urn:nbn:se:mdh:diva-61437 (URN)
Available from: 2023-01-05 Created: 2023-01-05 Last updated: 2023-05-17Bibliographically approved
Martinsen, M., Zhou, Y., Dahlquist, E., Yan, J. & Kyprianidis, K. (2023). Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future. Applied Energy, 339, Article ID 120988.
Open this publication in new window or tab >>Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future
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2023 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 339, article id 120988Article in journal (Refereed) Published
Abstract [en]

Initially, Artificial Intelligence (AI) focused on diagnostics during the 70s and 80s. Unfortunately, it did not gain trust and few industries embraced it, mostly due to the extensive manual programming effort that AI required for interpreting data and act. In addition, the computer capacity, for handling the amounts of data necessary to train AI, was lacking the disc dimensions we are used to today, which made it go slowly. Not until the 2000 s con-fidence in AI was established in parallel with the introduction of new tools that was paving the way for PLS, PCA, ANN and soft sensors. Year 2011, IBM Watson (an AI application) was developed and won over the jeopardy champion. Today's machine learning (ML) such as "deep learning" and artificial neural networks (ANN) have created interesting use cases. AI has therefore regained confidence and industries are beginning to embrace where they see appropriate uses. Simultaneously, Internet of Things (IoT) tools have been introduced and made it possible to develop new capabilities such as virtual reality (VR), augmented reality (AR), mixed reality (MR) and extended reality (XR). These technologies are maturing and could be used in several application areas for the industries and form part of their digitalization journey. Furthermore, it is not only the industries that could benefit from introducing these technologies. Studies also show several areas and use cases where augmented reality has a positive impact, such as on students' learning ability. Yet few teachers know or use this technology. This paper evaluates and analyze AR, remote assistance tool for industrial purposes. The potential of the tool is discussed for frequent maintenance cases in the mining industry. Further on, if we look into the future, it is not surprising if we will be able to see that today's concepts of reality tools have evolved to become smarter by being trained by multimedia recognition and from people who have thus created an AI expert. Where the AI expert will support customers and be able to solve simple errors but also those that occur rarely and thus be a natural part of the solution for future completely autonomous processes for the industry. The article demonstrates a framework for creating smarter tools by combining AR, ML and AI and forms part of the basis creating the smarter industry of the future. Natural Language Processing (NLP) toolbox has been utilized to train and test an AI expert to give suitable resolutions to a specific maintenance request. The motivation for AR is the possible energy savings and reduction of CO2 emissions in the maintenance field for all business trips that can be avoided. At the same time saving money for the industries and expert manhours that are spent on traveling and finally enhancing the productivity for the industries. Tests cases have verified that with AR, the resolution time could be significantly reduced, minimizing production stoppages by more than 50% of the time, which ultimately has a positive effect on a country's GDP. How much energy can be saved is predicted by the fact that 50% of all the world's business flights are replaced by one of the reality concepts and are estimated to amount to at least 50 Mton CO2 per year. This figure is probably slightly higher as business trips also take place by other means of transport such as trains, buses, and cars. With today's volatile employees changing jobs more frequently, industry experts are becoming fewer and fewer. Since new employee stays for a maximum of 3-5 years per workplace, they will not stay long enough to become experts. Introducing an AI expert trained by today's experts, there is a chance that this knowledge can be maintained.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD, 2023
Keywords
Intelligent systems, Augmented reality (AR), CO 2 emissions, Digitalization, Internet of things (IoT), Artificial Intelligence
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-62686 (URN)10.1016/j.apenergy.2023.120988 (DOI)000982738400001 ()2-s2.0-85151040196 (Scopus ID)
Available from: 2023-05-31 Created: 2023-05-31 Last updated: 2023-05-31Bibliographically approved
Neshat, M., Majidi Nezhad, M., Mirjalili, S., Garcia, D. A., Dahlquist, E. & Gandomi, A. H. (2023). Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy. Energy, 278, Article ID 127701.
Open this publication in new window or tab >>Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy
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2023 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 278, article id 127701Article in journal (Refereed) Published
Abstract [en]

Developing an accurate and robust prediction of long-term average global solar irradiation plays a crucial role in industries such as renewable energy, agribusiness, and hydrology. However, forecasting solar radiation with a high level of precision is historically challenging due to the nature of this source of energy. Challenges may be due to the location constraints, stochastic atmospheric parameters, and discrete sequential data. This paper reports on a new hybrid deep residual learning and gated long short-term memory recurrent network boosted by a differential covariance matrix adaptation evolution strategy (ADCMA) to forecast solar radiation one hour-ahead. The efficiency of the proposed hybrid model was enriched using an adaptive multivariate empirical mode decomposition (MEMD) algorithm and 1+1EA-Nelder–Mead simplex search algorithm. To compare the performance of the hybrid model to previous models, a comprehensive comparative deep learning framework was developed consisting of five modern machine learning algorithms, three stacked recurrent neural networks, 13 hybrid convolutional (CNN) recurrent deep learning models, and five evolutionary CNN recurrent models. The developed forecasting model was trained and validated using real meteorological and Shortwave Radiation (SRAD1) data from an installed offshore buoy station located in Lake Michigan, Chicago, United States, supported by the National Data Buoy Centre (NDBC). As a part of pre-processing, we applied an autoencoder to detect the outliers in improving the accuracy of solar radiation prediction. The experimental results demonstrate that, firstly, the hybrid deep residual learning model performed best compared with other machine learning and hybrid deep learning methods. Secondly, a cooperative architecture of gated recurrent units (GRU) and long short-term memory (LSTM) recurrent models can enhance the performance of Xception and ResNet. Finally, using an effective evolutionary hyper-parameters tuner (ADCMA) reinforces the prediction accuracy of solar radiation.

Place, publisher, year, edition, pages
Elsevier Ltd, 2023
Keywords
Deep residual learning, Gated recurrent unit, Hybrid deep learning models, Recurrent neural network, Short-term forecasting, Solar radiation, Xception
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-62698 (URN)10.1016/j.energy.2023.127701 (DOI)001002398700001 ()2-s2.0-85159392056 (Scopus ID)
Available from: 2023-05-31 Created: 2023-05-31 Last updated: 2023-06-21Bibliographically approved
Shabani, M., Wallin, F., Dahlquist, E. & Yan, J. (2023). The impact of battery operating management strategies on life cycle cost assessment in real power market for a grid-connected residential battery application. Energy, 270, Article ID 126829.
Open this publication in new window or tab >>The impact of battery operating management strategies on life cycle cost assessment in real power market for a grid-connected residential battery application
2023 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 270, article id 126829Article in journal (Refereed) Published
Abstract [en]

The relatively short lifetime of batteries is one of the crucial factors that affects its economic viability in current electricity markets. Thus, to make batteries a more viable technology in real power market from life cycle cost assessment perspective, full understanding of battery ageing parameters and which operating control strategies cause slower degradation rate is essential and still an open problem. This study deals with the 32 different battery operating control strategies to evaluate their importance on cyclic and calendric degradation, lifetime, and life cycle cost assessment of a battery system in a grid-connected residential application. In other words, it is evaluated that at which operating control strategy the system simulation results in a more beneficial system from techno-economic perspective. A battery modelling scenario is proposed to accurately estimate battery performance, degradation, and lifetime under real operational condition given different operating control strategies. An operational strategy, which benefits from the dynamic real-time electricity price scheme, is conducted to simulate the system operation. The key results show that selecting a proper state-of-charge control strategy positively affects the battery lifetime and consequently its net-present-value, in which the best strategy led to 30% improvement in net-present-value compared to the worst strategy.

Place, publisher, year, edition, pages
Elsevier Ltd, 2023
Keywords
Arbitrage application, Battery lifetime improvement, Battery SOC control strategies, Calendric and cyclic ageing, Life cycle cost assessments under real power market, Stationary battery storage
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-61958 (URN)10.1016/j.energy.2023.126829 (DOI)000944897100001 ()2-s2.0-85147883187 (Scopus ID)
Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-11-13Bibliographically approved
Chirumalla, K., Toorajipour, R., Dahlquist, E., Johansson, G. & Wallin, F. (2022). Configurations for second-life operations of electric vehicle batteries: A guiding framework for ecosystem management. In: : . Paper presented at 29th International EurOMA Conference, 1-6 July 2022, Berlin. Berlin: EurOMA
Open this publication in new window or tab >>Configurations for second-life operations of electric vehicle batteries: A guiding framework for ecosystem management
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2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Firms need multi-stakeholder ecosystems to create successful second-life business models for electric vehicle (EV) batteries. However, there is a lack of guiding instrumentsto support the process of strategizing and managing the EV battery ecosystem for secondlife operations. The purpose of this study is to propose a guiding framework that could support firms in the EV battery ecosystem to establish and manage various configurations for second-life operations. The study developed a framework with four configuration phases—namely, firm-level initiation, ecosystem construction, firm-level optimization, and ecosystem orchestration. Based on these phases, the paper describes three configuration pathways to establish and manage second-life operations

Place, publisher, year, edition, pages
Berlin: EurOMA, 2022
Keywords
EV battery ecosystem, Circular business models, Battery second life
National Category
Business Administration
Research subject
Innovation and Design
Identifiers
urn:nbn:se:mdh:diva-61436 (URN)
Conference
29th International EurOMA Conference, 1-6 July 2022, Berlin
Available from: 2023-01-05 Created: 2023-01-05 Last updated: 2023-01-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7233-6916

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