| 1. |
- Amici, Julia, et al.
(författare)
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A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030
- 2022
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Ingår i: Advanced Energy Materials. - : John Wiley & Sons. - 1614-6832 .- 1614-6840. ; 12:17
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Forskningsöversikt (refereegranskat)abstract
- This roadmap presents the transformational research ideas proposed by "BATTERY 2030+," the European large-scale research initiative for future battery chemistries. A "chemistry-neutral" roadmap to advance battery research, particularly at low technology readiness levels, is outlined, with a time horizon of more than ten years. The roadmap is centered around six themes: 1) accelerated materials discovery platform, 2) battery interface genome, with the integration of smart functionalities such as 3) sensing and 4) self-healing processes. Beyond chemistry related aspects also include crosscutting research regarding 5) manufacturability and 6) recyclability. This roadmap should be seen as an enabling complement to the global battery roadmaps which focus on expected ultrahigh battery performance, especially for the future of transport. Batteries are used in many applications and are considered to be one technology necessary to reach the climate goals. Currently the market is dominated by lithium-ion batteries, which perform well, but despite new generations coming in the near future, they will soon approach their performance limits. Without major breakthroughs, battery performance and production requirements will not be sufficient to enable the building of a climate-neutral society. Through this "chemistry neutral" approach a generic toolbox transforming the way batteries are developed, designed and manufactured, will be created.
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| 2. |
- Behi, Hamidreza, et al.
(författare)
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Heat pipe air-cooled thermal management system for lithium-ion batteries : High power applications
- 2021
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Ingår i: Applied Thermal Engineering. - : Elsevier BV. - 1359-4311 .- 1873-5606. ; 183
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Tidskriftsartikel (refereegranskat)abstract
- Thermal management of lithium-ion (Li-ion) batteries in Electrical Vehicles (EVs) is important due to extreme heat generation during fast charging/discharging. In the current study, a sandwiched configuration of the heat pipes cooling system (SHCS) is suggested for the high current discharging of lithium-titanate (LTO) battery cell. The temperature of the LTO cell is experimentally evaluated in the 8C discharging rate by different cooling strategies. Results indicate that the maximum cell temperature in natural convection reaches 56.8 degrees C. In addition, maximum cell temperature embedded with SCHS for the cooling strategy using natural convection, forced convection for SHCS, and forced convection for cell and SHCS reach 49 degrees C, 38.8 degrees C, and 37.8 degrees C which can reduce the cell temperature by up to 13.7%, 31.6%, and 33.4% respectively. A computational fluid dynamic (CFD) model using COMSOL Multiphysics (R) is developed and comprehensively validated with experimental results. This model is then employed to investigate the thermal performance of the SHCS under different transient boundary conditions.
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| 3. |
- Behi, Hamidreza, et al.
(författare)
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Thermal management analysis using heat pipe in the high current discharging of lithium-ion battery in electric vehicles
- 2020
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Ingår i: Journal of Energy Storage. - : Elsevier Ltd. - 2352-152X .- 2352-1538. ; 32
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Tidskriftsartikel (refereegranskat)abstract
- Thermal management system (TMS) for commonly used lithium-ion (Li-ion) batteries is an essential requirement in electric vehicle operation due to the excessive heat generation of these batteries during fast charging/discharging. In the current study, a thermal model of lithium-titanate (LTO) cell and three cooling strategies comprising natural air cooling, forced fluid cooling, and a flat heat pipe-assisted method is proposed experimentally. A new thermal analysis of the single battery cell is conducted to identify the most critical zone of the cell in terms of heat generation. This analysis allowed us to maximize heat dissipation with only one heat pipe mounted on the vital region. For further evaluation of the proposed strategies, a computational fluid dynamic (CFD) model is built in COMSOL Multiphysics® and validated with surface temperature profile along the heat pipe and cell. For real applications, a numerical optimization computation is also conducted in the module level to investigate the cooling capacity of the liquid cooling system and liquid cooling system embedded heat pipe (LCHP). The results show that the single heat pipe provided up to 29.1% of the required cooling load in the 8C discharging rate. Moreover, in the module level, the liquid cooling system and LCHP show better performance compared with natural air cooling while reducing the module temperature by 29.9% and 32.6%, respectively.
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| 4. |
- Behi, Hamidreza, et al.
(författare)
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Thermal Management of the Li-Ion Batteries to Improve the Performance of the Electric Vehicles Applications
- 2022
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Ingår i: Modern Automotive Electrical Systems. - : Wiley. ; , s. 149-191
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In recent decades, clean energy has been introduced as an alternative energy source. In this respect, the automobile sector, one of the world’s most prominent industries, has significantly contributed to the problem. Lithium-ion (Li-ion) batteries are among the most acceptable power sources for the automobile sector. Li-ion batteries benefit from high energy storage density, high cycle lifetime, and minimal self-discharge. Despite this, Li-ion batteries generate a significant amount of heat during the charge/discharge process due to chemical reactions and ohmic resistance. At the same time, Li-ion battery lifespan, efficiency, and safety are all influenced by temperature and temperature uniformity, which has become a significant factor affecting Li-ion battery performance. According to the studies, the suitable operating temperature for Li-ion batteries is slightly from about 25 to 40 °C, with a maximum temperature variance of less than 5 °C between cells and modules. Consequently, designing a capable battery thermal management system (BTMS) has become a fundamental challenge in the automotive industry to control and remove the generated heat by the cells from the safety and reliability evaluation. In addition, investigating the different thermal management systems (TMSs), their advantages, and disadvantages are critical in the automotive study. Therefore, a variation of active, passive, and hybrid cooling systems have been developed during the past decade to reach the heat-dissipation necessity for Li-ion batteries. The following chapter presents a comprehensive review of different cooling methods for battery thermal management at the cell, module, and pack levels. The chapter is organised as follows. Section 4.2 represents the objective of the research. Section 4.3 presents electric vehicle (EV) trend. Section 4.4, presents the different TMSs of the Li-ion batteries. The lifetime performance of Li-ion batteries is described in Section 4.5. The fundamental aspects of safety and reliability evaluation of EVs are investigated in Section 4.6. Lastly, a relevant conclusion is drawn in Section 4.7.
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| 5. |
- Bhowmik, Arghya, et al.
(författare)
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Implications of the BATTERY 2030+ AI-Assisted Toolkit on Future Low-TRL Battery Discoveries and Chemistries
- 2022
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Ingår i: Advanced Energy Materials. - : John Wiley & Sons. - 1614-6832 .- 1614-6840. ; 12:17
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Forskningsöversikt (refereegranskat)abstract
- BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high-performance batteries. Here, a description is given of how the AI-assisted toolkits and methodologies developed in BATTERY 2030+ can be transferred and applied to representative examples of future battery chemistries, materials, and concepts. This perspective highlights some of the main scientific and technological challenges facing emerging low-technology readiness level (TRL) battery chemistries and concepts, and specifically how the AI-assisted toolkit developed within BIG-MAP and other BATTERY 2030+ projects can be applied to resolve these. The methodological perspectives and challenges in areas like predictive long time- and length-scale simulations of multi-species systems, dynamic processes at battery interfaces, deep learned multi-scaling and explainable AI, as well as AI-assisted materials characterization, self-driving labs, closed-loop optimization, and AI for advanced sensing and self-healing are introduced. A description is given of tools and modules can be transferred to be applied to a select set of emerging low-TRL battery chemistries and concepts covering multivalent anodes, metal-sulfur/oxygen systems, non-crystalline, nano-structured and disordered systems, organic battery materials, and bulk vs. interface-limited batteries.
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| 6. |
- Edström, Kristina, Professor, 1958-
(författare)
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Battery 2030+ Roadmap
- 2020
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Climate change is the biggest challenge facing the world today. Europe is committed to achieving a climate-neutral society by 2050, as stated in the European Green Deal.1 The transition towards a climate-neutral Europe requires fundamental changes in the way we generate and use energy. If batteries can be made simultaneously more sustainable, safe, ultrahigh performing, and affordable, they will be true enablers, “accelerating the shift towards sustainable and smart mobility; supplying clean, affordable and secure energy; and mobilizing industry for a clean and circular economy” - all of which are important elements of the UN Sustainable Development Goals.In other words, batteries are a key technology for battling carbon dioxide emissions from the transport, power, and industry sectors. However, to reach our sustainability goals, batteries must exhibit ultra-high performance beyond their capabilities today. Ultra-high performance includes energy and power performance approaching theoretical limits, outstanding lifetime and reliability, and enhanced safety and environmental sustainability. Furthermore, to be commercially successful, these batteries must support scalability that enables cost-effective large-scale production.BATTERY 2030+, is the large-scale, long-term European research initiative with the vision of inventing the sustainable batteries of the future, to enable Europe to reach the goals envisaged in the European Green Deal. BATTERY 2030+ is at the heart of a green and connected society.BATTERY 2030+ will contribute to create a vibrant battery research and development (R&D) community in Europe, focusing on long-term research that will continuously feed new knowledge and technologies throughout the value chain, resulting in new products and innovations. In addition, the initiative will attract talent from across Europe and contribute to ensure access to competences needed for ongoing societal transformation.The BATTERY 2030+ aims are:• to invent ultra-high performance batteries that are safe, affordable, and sustainable, witha long lifetime.• to provide new tools and breakthrough technologies to the European battery industrythroughout the value chain.• to enable long-term European leadership in both existing markets (e.g., transport andstationary storage) and future emerging sectors (e.g., robotics, aerospace, medical devices, and Internet of things)With this roadmap, BATTERY 2030+ advocates research directions based on a chemistry-neutral approach that will allow Europe to reach or even surpass its ambitious battery performance targets set in the European Strategic Energy Technology Plan (SET-Plan)3 and foster innovation throughout the battery value chain.
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| 7. |
- Fichtner, Maximilian, et al.
(författare)
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Rechargeable Batteries of the Future-The State of the Art from a BATTERY 2030+Perspective
- 2022
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Ingår i: Advanced Energy Materials. - : John Wiley & Sons. - 1614-6832 .- 1614-6840. ; 12:17
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Forskningsöversikt (refereegranskat)abstract
- The development of new batteries has historically been achieved through discovery and development cycles based on the intuition of the researcher, followed by experimental trial and error-often helped along by serendipitous breakthroughs. Meanwhile, it is evident that new strategies are needed to master the ever-growing complexity in the development of battery systems, and to fast-track the transfer of findings from the laboratory into commercially viable products. This review gives an overview over the future needs and the current state-of-the art of five research pillars of the European Large-Scale Research Initiative BATTERY 2030+, namely 1) Battery Interface Genome in combination with a Materials Acceleration Platform (BIG-MAP), progress toward the development of 2) self-healing battery materials, and methods for operando, 3) sensing to monitor battery health. These subjects are complemented by an overview over current and up-coming strategies to optimize 4) manufacturability of batteries and efforts toward development of a circular battery economy through implementation of 5) recyclability aspects in the design of the battery.
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| 8. |
- Li, Yi, et al.
(författare)
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Random forest regression for online capacity estimation of lithium-ion batteries
- 2018
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Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 232, s. 197-210
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Tidskriftsartikel (refereegranskat)abstract
- Machine-learning based methods have been widely used for battery health state monitoring. However, the existing studies require sophisticated data processing for feature extraction, thereby complicating the implementation in battery management systems. This paper proposes a machine-learning technique, random forest regression, for battery capacity estimation. The proposed technique is able to learn the dependency of the battery capacity on the features that are extracted from the charging voltage and capacity measurements. The random forest regression is solely based on signals, such as the measured current, voltage and time, that are available onboard during typical battery operation. The collected raw data can be directly fed into the trained model without any pre-processing, leading to a low computational cost. The incremental capacity analysis is employed for the feature selection. The developed method is applied and validated on lithium nickel manganese cobalt oxide batteries with different ageing patterns. Experimental results show that the proposed technique is able to evaluate the health states of different batteries under varied cycling conditions with a root-mean-square error of less than 1.3% and a low computational requirement. Therefore, the proposed method is promising for online battery capacity estimation.
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