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Xiong, R., Li, Z., Li, H., Wang, J. & Liu, G. (2025). A novel method for state of charge estimation of lithium-ion batteries at low-temperatures. Applied Energy, 377, Article ID 124514.
Open this publication in new window or tab >>A novel method for state of charge estimation of lithium-ion batteries at low-temperatures
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2025 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 377, article id 124514Article in journal (Refereed) Published
Abstract [en]

The low temperature environment poses a significant challenge to the application of electric vehicles (EVs). At low temperatures, the dynamic characteristics inside the battery become significantly different from those in the temperature range of 10–40 °C, resulting in high uncertainties in the estimation of state of charge (SOC). Experimental studies on two types of lithium-ion batteries have found that due to changes in battery polarization characteristics at low temperatures, the open circuit voltage (OCV) identified by the commonly used equivalent circuit models and parameter identification methods becomes more distorted. This is the reason for the failure of most SOC estimation methods based on OCV-SOC mapping. A part of polarization voltage is incorrectly involved in the OCV by online parameter identification. Based on this phenomenon, a novel method is proposed to achieve accurate SOC estimation at low temperatures by compensating this part of polarization voltage. The compensation voltage is calculated by a function, which is identified from experimental data using genetic algorithm. The validation against experimental results demonstrates that the proposed method can achieve a root mean square error and mean absolute error of less than 3 % for the SOC estimation in temperatures down to −20 °C. Moreover, this method only needs experimental data of dynamic operating conditions measured at two temperatures which cover most of the battery's working temperature range. And its computational complexity is low, making it suitable for onboard applications. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2025
Keywords
Equivalent circuit model, Lithium-ion batteries, Low temperature, Polarization characteristic, State of charge, Ion batteries, Lithium ions, Lows-temperatures, Novel methods, Open-circuit voltages, Polarization characteristics, State-of-charge estimation, States of charges, Temperature range, electric vehicle, equipment component, error analysis, estimation method, experimental study, failure analysis, lithium, polarization
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-68584 (URN)10.1016/j.apenergy.2024.124514 (DOI)001324573800001 ()2-s2.0-85204529199 (Scopus ID)
Available from: 2024-10-02 Created: 2024-10-02 Last updated: 2024-10-10Bibliographically approved
Tian, Y., Lin, C., Meng, X., Yu, X., Li, H. & Xiong, R. (2025). Accelerated commercial battery electrode-level degradation diagnosis via only 11-point charging segments. eScience, 5(1), Article ID 100325.
Open this publication in new window or tab >>Accelerated commercial battery electrode-level degradation diagnosis via only 11-point charging segments
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2025 (English)In: eScience, ISSN 2667-1417, Vol. 5, no 1, article id 100325Article in journal (Refereed) Published
Abstract [en]

Accelerated and accurate degradation diagnosis is imperative for the management and reutilization of commercial lithium-ion batteries in the upcoming TWh era. Different from traditional methods, this work proposes a hybrid framework for rapid and accurate degradation diagnosis at the electrode level combining both deep learning, which is used to rapidly and robustly predict polarization-free incremental capacity analysis (ICA) curves in minutes, and physical modeling, which is used to quantitatively reveal the electrode-level degradation modes by decoupling them from the ICA curves. Only measured charging current and voltage signals are used. Results demonstrates that 11 points collected at any starting state-of-charge (SOC) in a minimum of 2.5 ​minutes are sufficient to obtain reliable ICA curves with a mean root mean square error (RMSE) of 0.2774 Ah/V. Accordingly, battery status can be accurately elevated based on their degradation at both macro and electrode levels. Through transfer learning, such a method can also be adapted to different battery chemistries, indicating the enticing potential for wide applications.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-69346 (URN)10.1016/j.esci.2024.100325 (DOI)001392126300001 ()2-s2.0-85207165380 (Scopus ID)
Available from: 2024-12-06 Created: 2024-12-06 Last updated: 2025-01-15Bibliographically approved
Li, H., Shi, X., Kong, W., Kong, L., Hu, Y., Wu, X., . . . Yan, J. (2025). Advanced wave energy conversion technologies for sustainable and smart sea: A comprehensive review. Renewable energy, 238, Article ID 121980.
Open this publication in new window or tab >>Advanced wave energy conversion technologies for sustainable and smart sea: A comprehensive review
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2025 (English)In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 238, article id 121980Article in journal (Refereed) Published
Abstract [en]

The world's oceans, covering approximately 71 % of the Earth's surface, harbor vast wave energy resources, offering a potential solution to the pressing energy crisis and environmental pollution caused by fossil fuel combustion. In recent years, there has been a global surge in exploration and development of wave energy conversion technologies, aimed at effectively harnessing wave energy to realizing sustainable and intelligent sea solutions. This comprehensive review examines the advancements, challenges and future research directions of current mainstream wave energy conversion technologies. Firstly, the distribution of global wave resources and energy conversion process involved in wave energy extraction are analyzed. Subsequently, various wave energy conversion technologies are meticulously classified based on their power take-off systems, and the strengths and challenges of each category are comprehensively investigated. Especially, a universal standard consisting of 5 key indicators has been established to evaluate and compare the characteristics of various wave energy conversion technologies based on different transduction mechanisms, providing comprehensive and intuitive valid references for developers with different needs. The evaluation reveals that the wave energy converters based on hybrid systems demonstrate significant promise as conversion technologies. Moreover, the review presents a summary and analysis of the latest advancements in the application of artificial intelligence within wave energy conversion technologies. This emerging integration of artificial intelligence showcases promising development and potential for further enhancing wave energy conversion systems. Lastly, the review explores the application and future research directions of wave energy conversion technologies. Notably, the investigation highlights the potential of developing a multi-energy complementary power generation system that can concurrently harness multiple renewable energy sources coexisting at sea. This concept represents a promising avenue for future research and development.

Place, publisher, year, edition, pages
Elsevier Ltd, 2025
Keywords
Artificial intelligence integration, Power take-off system, Sustainable and smart sea, Technical comparison and analysis, Wave energy, Wave energy conversion technology, Wave energy conversion, Comparison and analysis, Energy conversion technologies, Intelligence integration, Power take-off systems, Technical comparison and analyze, alternative energy, artificial intelligence, exploration, fossil fuel, power generation, Wave power
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-69258 (URN)10.1016/j.renene.2024.121980 (DOI)2-s2.0-85210142055 (Scopus ID)
Available from: 2024-12-04 Created: 2024-12-04 Last updated: 2024-12-04Bibliographically approved
Yang, K., Zhang, Q., Wang, G., Li, H. & McLellan, B. (2024). A new model for comprehensively evaluating the economic and environmental effects of vehicle-to-grid(V2G) towards carbon neutrality. Journal of Energy Storage, 98, Article ID 113067.
Open this publication in new window or tab >>A new model for comprehensively evaluating the economic and environmental effects of vehicle-to-grid(V2G) towards carbon neutrality
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2024 (English)In: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 98, article id 113067Article in journal (Refereed) Published
Abstract [en]

Vehicle-to-grid (V2G) technology enables electric vehicles (EVs) to serve as flexible load storage resources, which is expected to play a pivotal role in pursuing carbon neutrality. However, existing studies on the effect of V2G at different stages of carbon neutrality is not sufficient, and there is a lack of discussion on the optimal adoption period for V2G in the context of electricity marketization trading. To fill this gap, a new methodology is proposed in this study to analyze multi-dimension effects of V2G towards carbon neutrality. The model is a novel attempt of applying partial market equilibrium model to depict the interaction between electricity suppliers and V2G adopters. By applying in a China's case, the results demonstrate that: (1) EVs with V2G can substitute 22.2 %–30.1 % energy storage and accelerate the phase-out of coal-fired power. (2) V2G can effectively mitigate electricity price fluctuations, moreover, more fast charging infrastructure will strengthen such effect. (3) V2G only become attractive when renewable energy penetration rate reaches 80 %, otherwise, it cannot effectively reduce the total social cost and carbon emission. (4) In the carbon neutrality scenario with limited emission, the emission reduction effect of V2G is weakened, however, its economic benefit keeps increasing.

Place, publisher, year, edition, pages
Elsevier Ltd, 2024
Keywords
Carbon neutrality, EV charging mode, Partial market equilibrium model, Vehicle-to-grid, Charging (batteries), Commerce, Economic and social effects, Electric loads, Electric utilities, Emission control, Vehicles, Carbon neutralities, Charging modes, Electric vehicle charging, Electric vehicle charging mode, Equilibrium modelling, Flexible loads, Market equilibrium, Vehicle to Grid (V2G), Vehicle to grids, Carbon
National Category
Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-68143 (URN)10.1016/j.est.2024.113067 (DOI)001285879000001 ()2-s2.0-85199910310 (Scopus ID)
Available from: 2024-08-07 Created: 2024-08-07 Last updated: 2024-08-21Bibliographically approved
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
Series
Energy Proceedings, ISSN 2004-2965
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: 2024-11-28Bibliographically 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)001240292100001 ()2-s2.0-85192461845 (Scopus ID)
Available from: 2024-05-15 Created: 2024-05-15 Last updated: 2024-06-19Bibliographically approved
Li, H., Wu, J., Shi, X., Kong, L., Kong, W., Zhang, Z., . . . Yan, J. (2024). A self-powered smart wave energy converter for sustainable sea. Mechanical systems and signal processing, 220, Article ID 111641.
Open this publication in new window or tab >>A self-powered smart wave energy converter for sustainable sea
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2024 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 220, article id 111641Article in journal (Refereed) Published
Abstract [en]

Self-powered smart buoys are widely used in sustainable sea, such as marine environmental monitoring. The article designs a self-powered and self-sensing point-absorber wave energy converter based on the two-arm mechanism. The system consists of the wave energy capture module, the power take-off module, the generator module and the energy storage module. As the core component of the wave energy converter, the power take-off module is mainly composed of a two-arm mechanism, which can convert the oscillation heave motion into unidirectional rotary motion. To evaluate the power generation performance of the system, the kinematic and dynamic models of the wave energy converter with the flywheel are established, and the disengagement and engagement phenomena of the flywheel are analyzed. The effectiveness of the prototype in capturing wave energy is verified through dry experiments in lab and field tests. The dry experiment reveals that the maximum output power of the system is 5.67 W, and the maximum and average mechanical efficiency are 66.63 % and 48.35 %, respectively. Additionally, the field test demonstrates that the peak output power can reach 92 W. Meanwhile, the generated electrical signals can be processed by deep learning algorithms to accurately identify different wave states. This high performance confirms that the proposed wave energy converter can meet its own energy needs by capturing wave energy in the marine environment, while also achieving self-sensing for wave condition monitoring. The system has great potential for promoting the development of intelligent sustainable sea in the future. 

Place, publisher, year, edition, pages
Academic Press, 2024
Keywords
Power take-off, Self-powered and self-sensing, Smart wave energy converter, Sustainable sea, Two-arm mechanism, Condition monitoring, Deep learning, Power takeoffs, Sustainable development, Wave energy conversion, Wheels, Performance, Power take-offs, Self-powered, Self-powered sensing, Self-sensing, Wave energy, Wave energy converters, Flywheels
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-67901 (URN)10.1016/j.ymssp.2024.111641 (DOI)001258892700001 ()2-s2.0-85196099739 (Scopus ID)
Available from: 2024-06-26 Created: 2024-06-26 Last updated: 2024-07-10Bibliographically approved
Huang, Q., Wang, W., Ma, C., Feng, B., Luan, J., Sun, Q., . . . Wennersten, R. (2024). Assessment of the arbitrage by a compressed CO2 energy storage system-based on dynamic characteristics. Journal of Energy Storage, 95, Article ID 112391.
Open this publication in new window or tab >>Assessment of the arbitrage by a compressed CO2 energy storage system-based on dynamic characteristics
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2024 (English)In: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 95, article id 112391Article in journal (Refereed) Published
Abstract [en]

Fluctuations in electricity price create arbitrage opportunities for compressed CO2 energy storage (CCES) systems. However, previous studies often neglected the dynamic characteristics of CCES systems, leading to inaccurate assessments. This paper addresses this gap by evaluating the CCES system arbitrage considering its dynamic characteristics. We introduce a novel indicator, state of charge (SOC), into a mixed-integer linear programming (MILP) optimization model to capture the dynamics. Utilizing real electricity prices, the model optimizes the CCES operation strategy for a maximum profit. The results demonstrate that a CCES system with a 267 MWh capacity could achieve a total income of 22.5 MEUR in 2022, with a net present value (NPV) of 258.1 MEUR over 35 years, a payback time of 2 years, and an average round-trip efficiency (ARTE) of 77.0 %. Sensitivity analysis reveals that the sizes of the compressor, the expander, and the high-pressure gas tank significantly impact the arbitrage potential. In contrast, the steady-state model-based results demonstrate that the CCES system could yield a higher NPV of 573.7 MEUR, a shorter payback time of 1 year, and a higher ARTE of 87.0 %. This emphasizes the pivotal importance of integrating dynamic characteristics into the design and assessment of CCES systems for arbitrage assessment. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2024
Keywords
Arbitrage, Compressed CO<sub>2</sub> energy storage system, Dynamic characteristics, Optimization, State of charge, Compressibility of gases, Electric energy storage, Gas compressors, Integer programming, Sensitivity analysis, Compressed CO, Compressed CO2 energy storage system, Dynamics characteristic, Electricity prices, Optimisations, Payback time, States of charges, Storage systems, The net present value (NPV), Carbon dioxide
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-67903 (URN)10.1016/j.est.2024.112391 (DOI)001258510900001 ()2-s2.0-85196018460 (Scopus ID)
Available from: 2024-06-26 Created: 2024-06-26 Last updated: 2024-07-10Bibliographically approved
Sun, Y., Xiong, R., Meng, X., Deng, X., Li, H. & Sun, F. (2024). Battery degradation evaluation based on impedance spectra using a limited number of voltage-capacity curves. eTransporation, 22, Article ID 100347.
Open this publication in new window or tab >>Battery degradation evaluation based on impedance spectra using a limited number of voltage-capacity curves
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2024 (English)In: eTransporation, E-ISSN 2590-1168, Vol. 22, article id 100347Article in journal (Refereed) Published
Abstract [en]

Degradation prediction is crucial for ensuring safe and reliable operation of batteries. However, relying solely on capacity to characterize aging cannot comprehensively represent the health status of the battery. This work explores the potential of using a limited number of partial voltage-capacity curves to evaluate battery degradation with the aid of deep learning approaches, which can be used for onboard applications. A sequence-to-sequence model is proposed to predict the electrochemical impedance spectra during battery degradation. It only uses capacity sequences within a specific voltage range at fixed voltage increments from a limited number of cycles, which can be flexibly adapted to different life stages in an end-to-end manner. The proposed method has been validated based on the developed degradation dataset. The root mean square errors for the prediction of impedance spectra are less than 1.48 mΩ. Capacities and resistances associated with electrochemical processes can be further extracted from the obtained impedance spectra, facilitating a comprehensive evaluation of battery degradation. As a limited number of measured data are needed, the proposed method can reduce data storage requirements and computational demands, which enables fast and comprehensive aging diagnosis.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Aging diagnosis, Battery degradation, Deep learning, Impedance spectra, Digital storage, Electric batteries, Mean square error, Capacity curves, Degradation predictions, Health status, Impedance spectrum, Partial voltage, Reliable operation, Safe operation, Forecasting
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-68071 (URN)10.1016/j.etran.2024.100347 (DOI)001402993000001 ()2-s2.0-85198007902 (Scopus ID)
Available from: 2024-07-17 Created: 2024-07-17 Last updated: 2025-02-06Bibliographically approved
Yang, S., Luo, X., Li, X., Nian, V., Liu, S., Wang, Y. & Li, H. (2024). Comparing different battery thermal management systems for suppressing thermal runaway propagation. Journal of Energy Storage, 101, Article ID 114005.
Open this publication in new window or tab >>Comparing different battery thermal management systems for suppressing thermal runaway propagation
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2024 (English)In: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 101, article id 114005Article in journal (Refereed) Published
Abstract [en]

Thermal runaway (TR) stands as a critical risk in battery applications. Even though various battery thermal management systems (BTMSs) have been proposed to mitigate thermal runaway propagation, a comprehensive comparison remains elusive. This study evaluates the performance of three types of BTMSs with 5 configurations, which include: liquid cooling with cold plates added on the bottom (BTMS-1a), liquid cooling with cold plates added on the sides (BTMS-1b), liquid cooling with cold plates added between batteries (BTMS-1c), integrating thermal insulation materials between batteries (BTMS-2), and implementing phase change materials between batteries (BTMS-3). The highest temperature, propagation time, temperature uniformity, cooling rate, mass energy density, and volume energy density are used as key performance indicators for comparison. In general, BTMS-2 and BTMS-3 show advantages in energy density, however, their performances on TR suppression and battery thermal management are poor. BTMS-1c can suppress TR effectively at high flowrates, whereas it can lead to poor temperature uniformity. Suggestions are also provided regarding the selection of BTMSs for different applications: BTMS-1b, BTMS-1c, and BTMS-3 are recommended for small EVs, large EVs and large scale battery energy storage systems (BESSs), and small BESSs, respectively.

Place, publisher, year, edition, pages
Elsevier Ltd, 2024
Keywords
Battery thermal management, Liquid cooling, Performance comparison, Phase change material, Simulations, Thermal runaway propagation, Battery storage, Thermal insulation, Battery thermal managements, Cold plates, Performance, Phase Change, Simulation, Thermal management systems, Thermal runaways, Battery management systems
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-68650 (URN)10.1016/j.est.2024.114005 (DOI)2-s2.0-85205377975 (Scopus ID)
Available from: 2024-10-10 Created: 2024-10-10 Last updated: 2024-10-10Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6279-4446

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