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Researchers at the Faculty of Engineering and Science within the University of Agder [UiA] have developed a digital simulator along with an algorithm that automatically generates the Disassembly Process Plan [DPP] for batteries. The algorithm is built to select autonomously the most suitable machine to execute each disassembly operation, along with the corresponding toolkit. Beyond automating the DPP, this technological breakthrough promises to reduce the total disassembly process duration.

With the electrification of mobility, the upcoming wave of e-waste will be hard to deal with. In general terms, electrical waste is shredded in bulk before sorting and reprocessing. But lithium-ion batteries [LiBs], the types used in EVs, are inflammable and request careful handling. Moreover, shredding lots of different types of e-waste simultaneously inevitably results in contamination. Separating components before shredding would yield greater levels of purity, even allowing various components, such as cathodes, to be reused in their entirety. Dismantling batteries is a dangerous operation due to the risk of fire or explosion. Nonetheless, this process typically involves manual labour to remove the casing and separating the internal components – electrodes, electrolyte, cabling and separators.

Within the RHINOCEROS project, the UiA is responsible for developing an automated system for characterising battery state, discharging via the grid and dismantling for reuse or recycling. Their ultimate goal is to reduce the operational duration and to improve resource utilisation.  An important phase of their work lies in the development of a Disassembly Process Plan – shortly DPP, which features a disassembly sequence plan [DSP] and an algorithm that can establish autonomously the most suitable equipment for each disassembly operation. To avoid several rounds of unsuccessful trials, researchers have firstly created a digital simulator where they already tested the algorithm generating the DPP.

Trained to use time and resources efficiently

The use of a simulation environment offers a safe and cost-effective way for researchers to test and refine the algorithm in a controlled context. Beyond safety and resource optimisation, simulators facilitate also scalability and reproducibility. The algorithm is trained to select the most suitable toolkit and calculate optimal tool change sequences, which reduces the overall disassembly process duration. Moreover, it also features integrated data that allow it to verify beforehand compatibilities between requested tools and machine capabilities. By determining optimal disassembly sequences and tool allocation, the DPP reduces operational costs through more efficient equipment usage.

Closer look at the technical specifications of the DPP developed in RHINOCEROS

From a technical point of view, the digital simulator, built on a foundation of Python and NumPy, tracks job progress, machine availability, tool states and temporal dependencies. Researchers applied various Reinforcement Learning (RL) algorithms, including Proximal Policy Optimisation (PPO), Policy Gradient (PG), Advantage Actor-Critic (A2C) and Asynchronous Advantage Actor-Critic (A3C), to solve the Job Shop Scheduling Problem (JSSP). The PPO algorithm, in particular, has demonstrated superior performance compared to other algorithms and state-of-the-art (SoA) solutions. The simulator is using datasets similar to the battery disassembling problem, mimicking real use case scenarios.

In a recent development, researchers have introduced an innovative approach called QTM (Q-learning with Tsetlin Machines) to improve scheduling algorithms. This new method combines the pattern recognition abilities of Tsetlin Machines (TM) with the decision-making strengths of Q-learning. The QTM approach uses a sophisticated reward system to balance the completion of tasks, minimize the time taken, and optimise the overall schedule. The TM component excels at identifying patterns in scheduling scenarios by analysing key features of jobs and operations. Meanwhile, the Q-learning framework provides a foundation for learning through a process of trial and error, gradually improving decisions over time.

The DPP developed in the RHINOCEROS project features PPO, QTM and classic deep Q-learning.

Impact on the real-world industrial applications

The DPP brings significant implications for real-world applications, particularly in industries that prioritise efficiency and sustainability. In EV battery recycling, it optimises the disassembly sequence, enhancing efficiency. It is also applicable to electronics WEEE recycling, where complex assemblies require precise disassembly. The DPP can integrate into large industrial scales, enabling the reuse of valuable components and supporting circular business models.

Automation integrators and robotic system developers benefit from the DPP’s computational framework, which models tool-changing operations and associated costs, optimising robotic movements and tool selection. Academic institutions can use the DPP to study broader questions in automated planning and robotic intelligence, providing a well-defined problem structure for testing new algorithms and heuristics.

For industrial research labs, the DPP offers a framework to develop specialised applications across different product categories, evaluate alternative product designs for end-of-life processing efficiency and simulate different disassembly strategies before physical implementation, reducing development costs and accelerating innovation cycles in recycling technologies.

Future developments for the Disassembly Process Plan

Plans for the DPP foresee its integration into a comprehensive digital twin of the manufacturing environment. Before its demonstration in a real-world setup, this digital twin operating with configurable tool capabilities and interdependent job sequences, will have to pass thorough testing in a simulated environment.

© Visual: University of Agder [UiA]

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.