Researching the potential for using decentralised, agent based, self-organised robots for testing and sorting second life batteries
Lead Research Organisation:
Loughborough University
Department Name: Wolfson Sch of Mech, Elec & Manufac Eng
Abstract
The aim of this project is to investigate the use of automated, battery dismantling systems with human collaboration to re-purpose, sort and grade batteries from electric vehicles for second life applications.
With the decision by the UK government to ban the sale of new internal combustion engine (ICE) vehicles by 2030 and hybrid vehicles by 2035, it is expected that there will be a substantial increase in the number of electric vehicles (EV) being produced by 2050. As of this, the number of batteries coming through will be large and with the current automation this presents challenges in terms of storing, dismantling and recycling.
To recycle a battery is time consuming and can be expensive and dangerous. As an alternative to manually dismantling the battery, it is possible to mitigate the risks of electrocution, chemical exposure and arc flash by automating the process. However, this is a major challenge because battery packs are not standard with different vehicle manufacturers opting for different physical configurations, cell types and cell chemistries.
Currently, there are no known commercial plants undertaking automatic battery dismantling for re-use applications. Following the ICE ban, the UK government has invested £246 million into the Faraday battery challenge to help new facilities scale up and advance the production, use and recycling of batteries. This research supports that work and looks at the possibility of using decentralised, agent-based, self-organised robots for testing and sorting second life batteries. This work would focus on increasing the utilisation of automated systems to allow for more flexible and flow-efficient workstations. Moreover, there is a need to explore using symbiotic relations between humans and machines, through combined reinforcement learning, to utilise a human's intelligence with a machine's capability for processing data quickly, accurately and reliably.
With the decision by the UK government to ban the sale of new internal combustion engine (ICE) vehicles by 2030 and hybrid vehicles by 2035, it is expected that there will be a substantial increase in the number of electric vehicles (EV) being produced by 2050. As of this, the number of batteries coming through will be large and with the current automation this presents challenges in terms of storing, dismantling and recycling.
To recycle a battery is time consuming and can be expensive and dangerous. As an alternative to manually dismantling the battery, it is possible to mitigate the risks of electrocution, chemical exposure and arc flash by automating the process. However, this is a major challenge because battery packs are not standard with different vehicle manufacturers opting for different physical configurations, cell types and cell chemistries.
Currently, there are no known commercial plants undertaking automatic battery dismantling for re-use applications. Following the ICE ban, the UK government has invested £246 million into the Faraday battery challenge to help new facilities scale up and advance the production, use and recycling of batteries. This research supports that work and looks at the possibility of using decentralised, agent-based, self-organised robots for testing and sorting second life batteries. This work would focus on increasing the utilisation of automated systems to allow for more flexible and flow-efficient workstations. Moreover, there is a need to explore using symbiotic relations between humans and machines, through combined reinforcement learning, to utilise a human's intelligence with a machine's capability for processing data quickly, accurately and reliably.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/R513088/1 | 01/10/2018 | 30/09/2023 | |||
| 2575395 | Studentship | EP/R513088/1 | 01/07/2021 | 31/12/2024 | Matthew Beatty |
| EP/T518098/1 | 01/10/2020 | 30/09/2025 | |||
| 2575395 | Studentship | EP/T518098/1 | 01/07/2021 | 31/12/2024 | Matthew Beatty |