As battery recycling scales, digitalization is becoming essential for improving safety, efficiency, and process reliability. In recent years, we have experienced rapid changes in the market, including shifts in battery demand and supply chains, technological advancements with the scaling and implementation of AI, and global government commitments to investment. Let me recap and provide updates about the new best practices for battery recycling, as well as forecast what I see on the horizon.
I’ve covered the complete supply chain, including lithium production from geothermal brines, battery materials production and unit operations of slurry mixing and reactions, solid-liquid separation and process dryers, electrolytes and their effectiveness, innovations in battery types, chemistry, and designs, and lithium-ion battery recycling.
This now brings us to the operational and technical challenges these process silos present when designing a battery recycling facility. Digitalization can help operators overcome some of these barriers. Each silo falls into three main categories: Collection, transport and storage; diagnosis, discharge and disassembly; and material recovery.
Battery Collection, Transport, and Storage
The supply chain begins with the feed material and collection, transport, and storage. Different battery types present various challenges to ensure maximum recovery. Cloud-based advanced modeling and simulations can help achieve batch-to-batch integrity.
For the battery materials to be produced and reused, the required quality is as strict as pharmaceutical because any feed material variability can impact how a cell works in an EV. Modeling and simulations are used to optimize the collection by types, such that the feed material to each process is reproducible and the quality of the output can be controlled.
All the necessary data and documentation need to be recorded, including integration of monitoring sensors for critical parameters of temperature, pressure, and off-gases in real-time. This addresses the reproducibility of the process to ensure that each process step meets the quality targets.
Finally, strategically placed cameras can detect early signs of fire hazards, gas leaks, or battery structural damage. These cameras are based on charge-coupled device technology, which uses specialized sensors to capture light and convert it into high-fidelity digital images.
The cameras can look at the actual battery and compare it to the original battery documentation to ensure the structural quality. We now have the feed material stored in a safe location, and the next group of steps is diagnosis, discharge, and disassembly.
Diagnosis, Discharge, Disassembly
To begin, you must examine the battery to determine if it’s suitable for second use or recycling. The diagnostic process involves assessing key parameters, such as remaining capacity, internal resistance, voltage stability, and physical integrity. The process also involves diagnosing the physical structural state as well as advanced cell testing. You can perform this analysis using vision cameras and machine learning to determine the extent of any battery damage. Prior to recycling, the battery needs to be safely discharged using brine soaking or electrical discharge back to the grid or energy storage systems. Electrical discharge approaches are more energy efficient and environmentally sound.
Machine-learning (ML) algorithms and process simulation can significantly reduce the time and complexity of the selected process and can predict the optimal discharge profile, including the temperature rise to prevent thermal runaway and possible battery rebound charge or over-discharge danger. Machine learning can also develop standardized discharge protocols based on the battery’s age, chemistry, usage history, residual charge, and conditions. A discharge curve is reproducible, similar to the process drying curve, such that from the same initial parameters, the process will be the same. The recycler can input the battery parameters, and machine learning will produce the discharge curve to ensure the correct cycle time.
Currently, manual operations for disassembly involve over 50 individual steps depending upon the type of battery and how the battery cell, module, or pack is designed. This disassembly continues to be a barrier to scalability. Robots can perform the initial work to increase efficiencies. For the more detailed disassembly, battery processors can incorporate vision systems, sensors, and AI. These technologies provide real-time insights for identifying variations or damages in the batteries, as well as analyzing the information for adjusting the disassembly process.
Digital Twins for Material Recovery and Commissioning
The following pretreatment and hydrometallurgical steps can benefit from the use of digital twins for process control and de risking the scale-up. All these steps have interdependent physical and chemical parameters that influence each other, such as equipment designs, pH, temperature, reagent flow rates, and metal concentrations. The traditional design of experiments approach to process optimization requires a great deal of time and effort, while digital twins enable rapid hypothesis interactional testing. Digital twins also incorporate computational fluid dynamics (CFD) models for physical behavior analysis as well as machine learning. The combination of digital twins and CFD models with system integration testing provides valuable improvements in safety and reliability.
Lastly, once the plant is mechanically complete, you can use digital twins to conduct virtual commissioning to minimize delays before the actual operations begin. As the demand and pressures for efficiency increase on battery recycling, it’s essential that recycling plants are designed like chemical plants. Chemical engineers and recyclers must create something that is safe, efficient, scalable, and sustainable to meet the demand of industry across the globe. The chemical engineering techniques used for specialty chemicals, pharmaceuticals, and similar plants for process design, optimization, safety, and reliability are transferable to the recycling industry.
For companies developing, investing in, or partnering across the battery recycling value chain, Barry brings the real-world process expertise needed to separate promising concepts from scalable operations. Avicenne Energy clients can leverage his insight to pressure-test facility designs, identify operational risks, and build recycling strategies grounded in practical chemical plant execution.
Diagnosis, Discharge, Disassembly
Barry Perlmutter has over 40 years of technical engineering and business marketing experience in SLS, including filtration & separation, centrifugation, and process drying, as well as for lithium production, battery materials manufacturing, and battery recycling. As our Chemical and Mechanical Process Engineering Expert, Barry has supported clients across the North American battery value chain and is available to support your organization for Q&As, on a project basis, or through ongoing support. Click below to schedule a quick discovery call to see how Barry, or our other SMEs, can support your team.
