Intelligent Battery Recycling System

1. Used Lithium-ion batteries could cause serious environmental issues. Currently, there are no efficient systems to separate, characterize, and develop processes for recycling used lithium-ion batteries. 

2. We are proposing a closed loop of Lithium-ion battery using a highly intelligent system for battery separating, recycling process optimization and materials characterization, (and optimizing the process of recovering key materials from used lithium-ion battery cells.)

3. a) If our solution was scaled, the impact on the environment of used Lithium-ion batteries will be largely alleviated. b) The reliance on mining of key minerals such as Cobalt (conflict minerals), Nickel, and lithium will be significantly reduced. This also reduces the need for mining raw materials which involves energy-intensive processes.

Major economies like US, EU, China are investing heavily in transforming automotive industry to electrifications. The adoption of electric vehicles has created a huge demand for lithium-ion battery cells. The forecasted production of lithium-ion battery in 2030 is 1293 GWh which translates to over 2.6*109 tonnes of battery cells (source Bloomberg New Energy Finance). Lithium-ion batteries contain high-value and energy-intensive cathode materials including nickel and cobalt, which makes them economically valuable for recycling. Besides, Lithium-ion batteries contain organic electrolyte, which would contaminate the environment if not properly dealt. From both resource, energy and environment aspects, it is necessary to create a system to accelerate the development of recycling process for used Lithium-ion battery.

Essentially we are trying to create a system to accelerate the development of the recycling process of lithium-ion battery cells. 

• Consumers: creating a closed-loop recycling system can improve the salvage value of lithium-ion battery packs while reducing the cost of owning an electric vehicle. 
• Manufacturers: reducing the overall cost of Lithium-ion battery and alleviate the reliance on supply-chain of raw materials.
• Society: alleviating the impact of used batteries on the environment. 

We want to develop an intelligent platform for sorting and guiding the process development of lithium-ion recycling. We understand batteries from different OEMs have different chemistry and formula. We want to leverage data mining algorithms with empirical models from materials science to effectively shorten the process development cycle while conventional R&D of battery materials rely on the trial and error method. We plan to combine automation and high throughput method, including sorting, synthesis, characterization, and testing, to speed up the recycling process development. The system will leverage advanced machine learning algorithms to optimize the formulation development of direct recycling process for key cathode materials. Our database and models can also provide valuable insights for OEMs to better design batteries for recycling.

1. We use the combination of automation and high throughput method to replace the normal the trial and error method in the battery recycling development process. The development process is shortened compared with the conventional development cycle with less human labors involved.  

2. We will build up a database collecting data of aged battery, recycling product materials and testing results, which could be further used for creating the machine learning algorithms to self-improve the recycling process without further experiment.

1. Development of high throughput method for characterizing, processing and testing recycling materials;

2. Database of materials characterization data and ML models of optimizing the recycling formula and processing parameters;

It is unavoidable that used lithium-ion battery have to be dealt with in the future. It would be a matter of time before large scale of recycling of used lithium-ion battery happens. By speeding up the development process, we can make it come early.

Within the next year, the goal is to develop a high-throughput method, including sorting, powder processing, characterization, and testing of used batteries and materials. 

Within the next 5 year, the goal is to build up database and develop machine learning algorithm for self-improvement.

For next year: 

Technical challenges: 

1. Conventional materials processing equipment are batch-based with long processing time and limited processing capacities. The typical cycle time of processing battery active materials are over 15 hours. We need to work with equipment manufacturers to customize equipment suited for our high-throughput materials processing and automatic parameter setting based on the central computer model we’ve developed based on previous test data.

2. Finding key partners to get access to a large number of aged batteries for training our model and optimizing the process. 

3. Design and validate testing/characterizing methods for used batteries and recycled materials to suit high throughput process;

1. Find equipment manufacturers to jointly develop the equipment that fit our process;

2. Find strategic partners in the Electric Vehicle Market to jointly develop the recycle process and gather data for our system;

3. Leverage design of experiment, six sigma methodologies to develop and validate our testing/characterization process for the system.

Carnegie Mellon University

3

One of the partner is currently a Ph.D candidate Carnegie Mellon University and his Ph.D research focus is on (1) battery aging mechanism from electrode materials level to cell level; (2) recycling of aged cathode materials. His has deep knowledge in the field of both materials science and electrochemistry with 5 publications. He also have good understanding of the current battery recycling industry and technologies and insight to the future of the battery recycling field. 

Another partner obtained a his Ph.D. degree in Materials Science and Engineering from CMU and worked in the cleantech field for the past 9 years. He has strong technical background: author/co-authored over 20 peer-reviewed journals, been invited to review numerous manuscript for top ranking journals in the battery field with one patent pending and two provisional patents filed. He has strong expertise in lithium-ion battery materials supply chain and has worked with major cathode/anode materials suppliers since 2014. He had participated in several joint development projects for active materials development.

Customer Segments: 1) EV OEMs and owners who need to recycle their batteries; 2) Batteries OEMs who need active materials supply for battery manufacturing; 3) Battery recyclers who need to improve their recycling efficiencies.

Value Proposition: 1) Effective test/diagnostic methods to classify used batteries to determine reuse, repurpose, recycle battery packs; 2) Active materials (cathodes and metals) from recycling process to be used in new lithium-ion packs. 3) Information system and consulting services for recycling companies.

Funding: 1) seed funding from angel investors, incubators, VC fundings; 2) Federal SBIR and STTR; 3) Revenue from recycling services, consulting services, and selling materials.

The access to raising funds for development;

The access to industrial partners for development ;

The recycling of battery is complex process, which currently involves labor-intensive process and complex chemical process. By creating the database containing from information of battery cycle life and optimized recycling process, we can then apply Al and ML into the system and create a self-improved system by constantly optimization. The usage of Al and ML could speed up the development process of battery recycling.