News

Researchers at University of Agder (UiA) are working on the automated sorting and dismantling of lithium-ion batteries (LIBs) that facilitates their reuse for second life applications.

During the first reporting period, UiA designed a simulator within a virtual environment, which allowed researchers to collect necessary data and parameters, and additionally identify potential bottlenecks that may occur in the actual disassembly process. Beyond collecting data without any physical experiment, the simulation environment brings the benefit of being cost- and time-efficient, allowing for safe and flexible robotic programming without disrupting the production.

According to the simulation environment that covers the entire disassembly process, from automated discharging to sorting, the entire process can run with a total duration spanning between 12 and 14 minutes. The detailed results of this activity will soon be publicly available in a new scientific paper titled addressing  the evaluation of deep reinforcement learning for job shop problems.

During the past six months, UiA researchers have constructed a virtual simulator to train the Machine Learning (ML) algorithms. Deep learning methods have already been applied for the Job Shop problem for finding the optimal disassembly sequence when the dependence matrix is known. Next development steps will entail training the algorithms to enable automatic disassembly of Electric Vehicle (EV) batteries without prior knowledge, while optimising procedures and enhancing safety.

© AdobeStock Photos

Benefits of “offline programming”

Simulation environments have been widely used in robotics for demonstration and planning purposes. This typically takes place within a simulation software or any other platform that can replicate the robot’s dynamics, workspace and surrounding environment, and enable robotic programming. This replication system has proved to be cost- and time-efficient due to a series of advantages: no risk of disrupting the production by removing the robot from the production line, high flexibility allowing infinite number of configurations on a virtual model of the robot, reduced risk of equipment damage due to high predictability of malfunctions. For instance, operational industrial robots can be tested in a simulation environment before deployment. This process is often referred to as “offline programming”.

Researchers at Department of Engineering Sciences, University of Agder have been designing a simulator within a virtual environment to visualise and test various demanufacturing approaches for battery packs, allowing them to collect necessary data such as process duration, disassembly tools – all without the need of physical experiments. This innovative exploration not only streamlines data gathering but can also help identify and remove unforeseen bottlenecks in the disassembly process.

Environment configuration and use case application for battery pack demanufacturing

Using a simulation environment, known for its high-fidelity graphical capabilities, researchers at UiA were able to create a controlled virtual space ideal for visualising complex robotic processes and interactions related to demanufacturing electric vehicle (EV) batteries. The robotic cell design is decomposed across all the subtasks/segments of the disassembly process, with specific consideration to safety aspects and optimised efficiency and accessibility of robotic manipulators.

In order to study in depth and to demonstrate the efficacy of a proposed fully automated demanufacturing line, researchers at UiA meticulously recreated a virtual environment where they simulated the disassembly of a an EV battery pack. This simulation encompasses the entire process from automated discharging to the disassembly of packs into modules, subsequent characterisation, sorting, and finally, the disassembly of modules into individual cells. All elements of the simulation are animated using the simulation platform and a robotic operating system code, providing a holistic view of the potential automation within the demanufacturing process.

For this particular use case, researchers at UiA have calculated the time individually for each disassembly operation, reaching roughly between 12 and 14 minutes for the entire process.

The findings of this research that replicated the complete demanufacturing of EV LiB pack in a virtual, yet realistic industrial setting, illustrate the leverage of automated processes over conventional approaches conventionally relying on manual techniques. The simulation provides estimates for operation time for a given disassembly procedure (disassembly sequence and disassembly process). Upcoming steps will involve AI to generate and optimise the procedures. Additionally, the simulation can identify solutions to minimise human exposure to potential hazards associated with battery disassembly processes. Future in depth and multidisciplinary research is required to optimise the disassembly sequences and process in the simulated environment by training reinforcement learning agents and including a collision avoidance system, to name a few.

Ultimately, the aim of this research is to anticipate the increasing number of EV batteries that will be decommissioned soon, and to ensure a proper management of waste, while recovering all the resources available in clean mobility technologies.

Discover UiA’s previous activities

© Photo: Adobe

Three dimensional (3D) Scanning of Battery Packs

Following the manual dismantling of various battery packs during the first six months of project, researchers at University of Agder (UiA) have developed a semi-automated process to address the diverse nature of battery packs. Their advanced robotic system can estimate the size of individual components of a battery pack. Afterwards, using different angles, it identifies optimal locations to capture precisely 3D images of these components, thus ensuring no detail is missed through this comprehensive scanning.

Using different perspectives, this thorough scanning process is further translated into a list of point clouds. Applying sophisticated algorithms,  these point clouds are later combined and merged into a solid mesh component. This process is repeated individually for each new component which needs to be scanned.

Scanned components of Battery pack

Beyond geometric characteristics

While geometric characteristics are important, they provide even greater value when combined with other physical attributes and interconnection data of the components. The researchers have successfully documented these details, resulting in a rich digital repository of the battery pack. With the establishment of this detailed digital repository, the focus is now shifting towards its applications, where the primary goal is to automate the disassembly sequences.

Simultaneously, the team is also focusing on the automatic characterisation of battery packs and modules. Significant efforts are channeled towards creating a robust digital simulator, which will serve as a platform for training and rigorous testing of the disassembly planner – currently under development.

Innovation in disassembly tools

At the same time, the research team involved in work package 3 have been working on improving the disassembly operations tools. The results reported positive feedback, with several tools already successfully  tested in lab environment. This is a significant step towards the fully automated disassembly process.

A noteworthy development is the automation of the non-destructive disconnection of cables. This procedure is essential as it stands as the second most frequent operation in battery pack disassembly, just after the unscrewing operation.

You can read more about previous activities developed in work package 3 in the article ‘Manual dismantling of a battery pack‘.

During the first six months, University of Adger [UiA] received three battery packs (out of the five planned) and manually disassembled them, opening for further analysis. In the future, this activity will feed a digital repository as promised in the first delivery of Work package 3. 

For each battery pack, the analysis includes: 

• a precedence graph informing how components are connected, which, in the upcoming steps, will help determine the best order to dismantle these components automatically.   

• an Excel table listing the characteristics of each type of component other than geometrical characteristics: number of items, mass, material, or other specific features.  
• 3D scanning in the form of point clouds (pcls) to provide information on the geometry and texture of the components constituting the different battery packs. After testing several hardware and algorithms, two of them have been selected.  

In parallel, several of the main important tools have already been identified based on the manual disassembly of these three battery packs, and a tool changer is under development. End effectors (tools) will be able to be changed quickly, including their connection to their power source (electric and/or pneumatic) and their signals. 

In addition, the disconnection of power and signal cables using non-destructive methods – operation identified as critical, has been investigated and currently, a concept is prototyped and evaluated. The main challenge is to design a tool that “fits them all”. Additional activities carried out within WP3 have investigated different sorting (characterisation) methods, based on temperature, mass loss, and other flaws, such as deformations, leakage, trace of heat damages.  

Safety has also been an important part of the work completed within WP3 during the first six months. A complete monitoring system and a set of safety measures to be followed during the scheduled demanufacturing (discharge, sorting and disassembly) activities have been established.  

During February, when the researchers started examining the available methods for automatic task planning using search algorithms and/or reinforcement learning, the robotic system adaptability was discussed. In anticipation of the implementation and testing phases of these adaptive robotic methods, thorough battery knowledge stored within the digital repository must first be developed.