Sabri Baazouzi

Mr. Sabri Baazouzi

orcid

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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3 June 2023 in Batteries
Verified
Journal Article
Open access
PDF file
SBSabri Baazouzi
Sabri Baazouzi
NFNiklas Feistel
Niklas Feistel
JWJohannes Wanner
Johannes Wanner
ILInga Landwehr
Inga Landwehr
AFAlexander Fill
Alexander Fill
Kai Peter BirkeKai Peter Birke
Kai Peter Birke
Battery cells are the main components of a battery system for electric vehicle batteries. Depending on the manufacturer, three different cell formats are used in the automotive sector (pouch, prismatic, and cylindrical). In the last 3 years, cylindrical cells have gained strong relevance and popularity among automotive manufacturers, mainly driven by innovative cell designs, such as the Tesla tabless design. This paper investigates 19 Li-ion cylindrical battery cells from four cell manufacturers in four formats (18650, 20700, 21700, and 4680). We aim to systematically capture the design features, such as tab design and quality parameters, such as manufacturing tolerances and generically describe cylindrical cells. We identified the basic designs and assigned example cells to them. In addition, we show a comprehensive definition of a tabless design considering the current and heat transport paths. Our findings show that the Tesla 4680 design is quasi-tabless. In addition, we found that 25% of the cathode and 30% of the anode are not notched, resulting in long electrical and thermal transport paths. Based on CT and post-mortem analyses, we show that jelly rolls can be approximated very well with the Archimedean spiral. Furthermore, we compare the gravimetric and volumetric energy density, the impedance, and the heating behavior at the surface and in the center of the jelly rolls. From the generic description, we present and discuss production processes focusing on format and design flexible manufacturing of jelly rolls.

1 November 2021 in Resources, Conservation and Recycling
Verified
Journal Article
Closed access
SGSimon Glöser-Chahoud
Simon Glöser-Chahoud
SHSandra Huster
Sandra Huster
SRSonja Rosenberg
Sonja Rosenberg
SBSabri Baazouzi
Sabri Baazouzi
SKSteffen Kiemel
Steffen Kiemel
Frank SchultmannFrank Schultmann
Frank Schultmann

7 November 2021 in Batteries
Verified
Journal Article
Open access
PDF file
SBSabri Baazouzi
Sabri Baazouzi
FRFelix Paul Rist
Felix Paul Rist
MWMax Weeber
Max Weeber
Kai Peter BirkeKai Peter Birke
Kai Peter Birke
Various studies show that electrification, integrated into a circular economy, is crucial to reach sustainable mobility solutions. In this context, the circular use of electric vehicle batteries (EVBs) is particularly relevant because of the resource intensity during manufacturing. After reaching the end-of-life phase, EVBs can be subjected to various circular economy strategies, all of which require the previous disassembly. Today, disassembly is carried out manually and represents a bottleneck process. At the same time, extremely high return volumes have been forecast for the next few years, and manual disassembly is associated with safety risks. That is why automated disassembly is identified as being a key enabler of highly efficient circularity. However, several challenges need to be addressed to ensure secure, economic, and ecological disassembly processes. One of these is ensuring that optimal disassembly strategies are determined, considering the uncertainties during disassembly. This paper introduces our design for an adaptive disassembly planner with an integrated disassembly strategy optimizer. Furthermore, we present our optimization method for obtaining optimal disassembly strategies as a combination of three decisions: (1) the optimal disassembly sequence, (2) the optimal disassembly depth, and (3) the optimal circular economy strategy at the component level. Finally, we apply the proposed method to derive optimal disassembly strategies for one selected battery system for two condition scenarios. The results show that the optimization of disassembly strategies must also be used as a tool in the design phase of battery systems to boost the disassembly automation and thus contribute to achieving profitable circular economy solutions for EVBs.

22 July 2022 in Energies
Verified
Journal Article
Open access
PDF file
SRSonja Rosenberg
Sonja Rosenberg
SHSandra Huster
Sandra Huster
SBSabri Baazouzi
Sabri Baazouzi
SGSimon Glöser
Simon Glöser
AAAnwar Al Assadi
Anwar Al Assadi
Frank SchultmannFrank Schultmann
Frank Schultmann
In the coming decades, the number of end-of-life (EoL) traction battery systems will increase sharply. The disassembly of the system to the battery module is necessary to recycle the battery modules or to be able to use them for further second-life applications. These different recovery paths are important pathways to archive a circular battery supply chain. So far, little knowledge about the disassembling of EoL batteries exists. Based on a disassembly experiment of a plug-in hybrid battery system, we present results regarding the battery set-up, including their fasteners, the necessary disassembly steps, and the sequence. Upon the experimental data, we assess the disassembly duration of the battery system under uncertainty with a fuzzy logic approach. The results indicate that a disassembling time of about 22 min is expected for the battery system in the field study if one worker conducts the process. An estimation for disassembling costs per battery system is performed for a plant in Germany. Depending on the plant capacity, the disassembling to battery module level is associated with costs between EUR 80 and 100 per battery system.

12 December 2023 in Batteries
Verified
Journal Article
Open access
PDF file
SBSabri Baazouzi
Sabri Baazouzi
JGJulian Grimm
Julian Grimm
Kai Peter BirkeKai Peter Birke
Kai Peter Birke
Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries through the application of circular economy strategies to extend the life cycle of battery components through solutions such as remanufacturng, repurposing, and efficient recycling, ultimately reintegrating gained materials into the production of new battery systems. This paper aims to develop a multi-method self-configuring simulation model to investigate disassembly scenarios, taking into account battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a Mercedes–Benz battery and the automated disassembly station of the DeMoBat project at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios: component-oriented and accessibility-oriented disassembly. These scenarios are compared using the simulation model to determine several indicators, including the frequency of tool change, the number and distribution of robot routes, tool utilization, and disassembly time.

23 October 2023 in Preprints.org
Verified
Preprint
Open access
PDF file
SBSabri Baazouzi
Sabri Baazouzi
JGJulian Joël Grimm
Julian Joël Grimm
Kai Peter BirkeKai Peter Birke
Kai Peter Birke
Disassembly is a pivotal technology to enable the circularity of electric vehicle batteries by applying circular economy strategies that extend the life cycle of their components, like remanufacturing and repurposing, or by using efficient recycling to reintegrate the gained materials in producing new battery systems. This paper aims to develop a multi-method self-configuring simulation model for investigating disassembly scenarios while considering the battery design as well as the configuration and layout of the disassembly station. We demonstrate the developed model in a case study using a battery of Mercedes-Benz and the automated disassembly station of the project DeMoBat at Fraunhofer IPA. Furthermore, we introduce two disassembly scenarios, the component-oriented and the accessibility-oriented disassembly, and compare them using the simulation model to determine several indicators, such as the frequency of tool change, the number and distribution of robot routes, the tool utilization, and the disassembly time.