AI and machine learning methods can enhance new developments and diagnostic capabilities across a battery’s life, from material discovery and cell testing to second-life assessment.
IDTechEx’s report, “AI-Driven Battery Technology 2025-2035: Technology, Innovation and Opportunities,” explores predictions and innovation possibilities within the sector over the next decade. It states that employing AI within the battery sector could bring about faster cell development time, improved yield and quality control, and the future development of new cell chemistries and battery formulations.
AI and machine learning could be involved in every stage of a battery’s life. AI technology could assist in material discovery, high-throughput cell screening, and temperature and pressure simulation during the initial development stage. The manufacturing processes for AI applications that follow could then include defect identification, parameter optimisation through regression, and quality control.
During the battery’s active in-life stage, AI and machine learning can maintain it, prolong its life, and give it the best chance at reaching optimal performance. Charge-discharge protocol optimisation, hazard identification, state-of-health (Soh) calculation, charge calculations, and defect detection will all ensure safety is prioritised, such as when a battery is active within an electric passenger vehicle.
At the end of a battery’s life cycle, SoH calculations, defect identification, and second-life assessments are required. AI could provide the most accurate information to determine which applications it would suit in a second-life run.
The main issues surrounding rechargeable batteries include challenges with energy density, the safety of liquid electrolytes, and the extraction of critical materials. Liquid electrolytes tend to become dangerous under high temperatures and pressures, presenting an opportunity for AI and machine learning to perform in-life diagnostics and monitoring to help maintain a level of safety and assurance that, should any problems begin, they are detected early.
The limited supply of critical materials such as lithium, nickel, cobalt, and copper can pose difficulties for battery manufacturers, along with the high extraction costs and negative environmental impacts. These factors associated with sourcing highly sought-after materials incentivise battery repurposing and recycling, which can also be assisted through machine learning techniques.
Electric vehicles are predicted to be one of the largest markets for AI in batteries. AI’s potential to benefit and enhance the many stages of a battery’s life will, in turn, allow for better development, testing, and management of EVs.
New battery chemistries and structures will be necessary to improve energy density, charging, and safety, all of which EV manufacturers aim to prioritise. AI’s ability to explore a range of chemistries efficiently and effectively could mean improvements are seen much faster and with less need for trial and error.
The assurance that AI and machine learning’s intelligence can provide could also be unmatched when it comes to achieving strong diagnostics, as levels of safety can be achieved that weren’t previously possible. Battery management systems will also enable users to access data as it is being analysed and interpreted for greater involvement in what is happening with the vehicle.