The new global decarbonization and energy transition guidelines have led the industrial sector to undergo a metamorphosis towards more sustainable alternatives. To this end, phenomena such as digital transformation and the implementation of new solutions at the forefront of technological advances are helping to accelerate these changes.


Due to the expected demand in the battery sector in the coming years, the industry´s efforts are focused on the technological evolution of the different stages of the value chain, such as, for example, the recycling of these devices at the end of their useful life.

This phase is a critical activity, since its deployment will determine whether the industry is truly "circular" and meets the expectations of the sector in terms of competitiveness, guarantee of supply and sustainability.

For all these reasons, it is necessary that the technological recycling routes that are developed are profitable, industrializable and sustainable; achieving, quickly and efficiently, the necessary economies of scale to ensure the viability of the activity.

Hence the importance of taking advantage of the new approaches that digitalization and the solutions that compose it (such as Artificial Intelligence, also known as AI) can offer to battery recycling plants, with the corresponding advantages that in terms of time, cost and efficiency present this type of tools.

This document makes a high-level identification of this set of opportunities presented by AI for the two main activities that make up the recycling of this type of devices: on the one hand, the management of waste once the useful life of the battery is over and, on the other hand, the recovery of materials from these wastes through the different existing technological routes.


Two main stages in the battery recycling process

Phase 1

Waste sorting

Phase 2

Recovery of materials


Initial stage aimed at classifying existing waste in order to identify potentially recoverable waste and materials.

Treatment stage of previously sorted waste, in order to recover the available materials through the different existing technological routes.

Summary of opportunities presented by AI

  • Increased efficiency in the grouping process.
  • Process automation capability.
  • Reduction of associated costs.
  • Risk reduction due to the possible toxicity of the waste.
  • Increased efficiency in the batch treatment process.
  • Cost reduction.
  • Reduction of the environmental impact of the process.
  • Process automation capacity.
  • Exploitation of synergies between the different alternatives / technological routes existing in the industry.

Table 1: Summary of phases of the battery recycling process and potential opportunities for AI in these phases.


As indicated above, the battery recycling activity starts with a first phase associated with the classification of waste in order to identify which waste and materials are potentially recoverable.

With this objective, this first initial stage can count on a great ally in AI, offering, through its different algorithms, very useful pattern recognition solutions in this phase of categorization and grouping of the different wastes. All this, with the added benefit of enabling a higher degree of safety in waste treatment, as we will see below.

In relation to the classification capacity, there are studies [1] that have shown how, through image recognition techniques, the AI is able to determine the location and type of waste to be treated. This makes it possible to determine which method or route may be the best for "managing" that waste according to its nature (taking into account, for example, aspects such as its toxicity or other key aspects associated with safety in its handling). All this not only results in greater efficiency in waste classification, but also reduces the risk associated with the process by selecting the most optimal one.

This first categorization is complemented by the possibility of automating the process of grouping waste into "batches", performing the corresponding groupings according to their characteristics in order to unify their subsequent treatment and recovery. In this case, there are approaches [2] that have demonstrated the use of convolutional neural networks for this purpose, which allow, from a sufficient set of labeled data, to perform this grouping. It is also expected that this categorization capacity will be increased and polished as the number of data and references available to the AI for this purpose increases, through approaches such as those proposed by A. Abucide-Armas et al [3] and Azurmendi et al [4].

Beyond these advantages, another key element of the use of AI in this phase of the process is the ability to capture data to predict the potential amount of waste to be treated (for which it is key that they work in real time [5] [6]). This involves planning the activity in advance (determining and optimizing possible recycling routes), and quantifying how much material can be recovered for subsequent reuse in new batteries.


Once the waste has been sorted and grouped, as mentioned at the beginning, it is ready to be treated for subsequent recovery, thus initiating the second phase of the recycling activity.
Currently, there are three main technological routes identified as viable within the battery recycling industry:



Advantages of each route

Cross-cutting opportunities offered by AI


Use of high temperatures (>1,500º) to melt and burn carbon-based compounds.

  • High metal recovery rate
  • Low cost
  • Reduction of hazardous waste
  • Increased efficiency
  • Cost reduction
  • Reduced environmental impact
  • Increased safety
  • Exploitation of synergies between routes


Based on the acid solubility of the elements present in the active materials to carry out their recovery.

  • High purity
  • Range of applications
  • Recovery rate

Direct recycling

Restoration of the initial properties of the devices through techniques that avoid chemical decomposition of the active battery material.

  • Cost competitive
  • Environmental impact
  • Conserve

Table 2: Main technological routes within battery recycling [7].

The great potential of AI within this phase of the recycling process is associated with the automation of the process itself (regardless of the route), which would mean a reduction of its complexity, cost and current environmental impact.

In this sense, AI allows automatic identification of the type of existing waste (again through images, as mentioned in the previous section). It thus determines their level of degradation and evaluates the type of action to be taken. This allows to increase the efficiency of the activity, improving the decision-making process according to the level and quality of the waste. It also reduces the degree of economic waste and minimizes the environmental impact that the process can generate unnecessarily.

In the same way, and as in the grouping phase, automation through AI provides a boost in terms of safety, by allowing automatic treatment of waste, minimizing, as far as possible, its contact with people and, therefore, potential risks.

Finally, another great opportunity presented by AI is the ability to facilitate the joint application of several of these routes, thus taking advantage of their synergies. Thus, depending on the type of waste and its treatment needs (input from the first phase of recycling), the AI is able to determine in which case and which waste it is more convenient to use one or another alternative, thus taking advantage of the strengths and advantages of each of these technological routes.


In an industry such as the battery industry that seeks to be as efficient as possible while at the same time sustainable, it seems clear, as described throughout the text, the fit of AI as an ally to achieve these objectives, thanks to its potential in key activities such as recycling.

Thus, on the one hand, AI solutions offer the ability to monitor and automate processes such as waste sorting and grouping or the recycling activity itself, which is an advantage in terms of efficiency, cost and safety.

On the other hand, it must be taken into account that these solutions are based on data capture and processing, which is a high value-added tool to better understand the challenges and areas for improvement in recycling processes, contributing to their continuous improvement, optimization and technological maturity.

Hence the opportunity that digitization, through this type of solutions, represents for strategic industries of the future such as batteries, as it is a catalyst for a better understanding of them, thus boosting their technological and industrial development.



[1] AZIS, F.; AROF, H.; MOKHTAR, N.; et al. "Rotation invariant bin detection and solid waste level classification". Measurement. April 2015, vol.65, p.19-28. DOI: 10.1016/j.measurement.2014.12.027.

[2] SAKR, G.E; MOKBEL, M.; DARWICH, A.; KHNEISSER, M.N.; HAD, A. "Comparing deep learning and support vector machines for autonomous waste sorting”. 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), Beirut, Lebanon. 2016, p.207-212. DOI: 10.1109/IMCET.2016.7777453.

[3] ABUCIDE-ARMAS, A.; PORTAL-PORRAS, K.; FERNÁNDEZ-GÁMIZ, U; et al. "A data augmentation-based technique for deep learning applied to CFD simulations". Mathematics. August 2021, vol. 9, no 16, p.1843. DOI: 10.3390/math9161843.

[4] AZURMENDI, I.; ZULUETA, E.; LÓPEZ-GUEDE, J.M.; et al. "Cooktop Sensing Based on a YOLO Object Detection Algorithm". Sensors, March 2023, vol. 23, no 5, p. 2780. DOI: 10.3390/s23052780.

[5] YANG, H.; NI, J.; GAO, J.; et al. "A novel method for peanut variety identification and classification by Improved VGG16". Scientific Reports. May 2021, vol. 11, no 1, p.1-17. DOI: 10.1038/s41598-021-95240-y

[6] GÜNEY, E.; BAYILMIS, C.; CAKAN, B. "An implementation of real-time traffic signs and road objects detection based on mobile GPU platforms". IEEE Acces, August 2022, vol. 10, p. 86191-86203. DOI: 10.1109/ACCESS.2022.3198954

[7] Nestor Antuñano. Analysis of the major recycling processes in the battery industry [en línea]. June 2021. Available in the website:

Iñigo Careaga, Strategy Manager, CIC energiGUNE

Andrea Casas, sustainability specialist

In collaboration with:

Universidad del Pais Vasco EHU/UPV

Cookies on this website are used to personalize content and advertisements, provide social media features, and analyze traffic. You can get more information and configure your preferences HERE