AI-Driven Battery Technology Market to Reach US$2.7 Billion by 2037

 

 

The AI-driven battery technologies market is set to grow to US$2.7 billion by 2037, representing a CAGR of 25% from 2026. This large growth rate is primarily due to increasing deployment of edge-based diagnostics in premium electric cars, consumer electronics, and commercial vehicles, although there will also be significant growth in cloud-based diagnostics, cell testing, and second-life assessment. AI is well-positioned to disrupt processes throughout the battery supply chain, expediting cell development, increasing yield in gigafactories, extending battery lifetime and providing lower costs and testing times for second-life battery repurposing. For more details, see IDTechEx's report, "AI-Driven Battery Technology 2027-2037: Technologies, Innovation, Applications and Opportunities".

 

 

 

Incumbent Li-ion battery chemistries are close to reaching their performance ceiling, with theoretical improvements to energy density, charging rate, and safety primarily being accessible via new cathode and anode materials. Testing new battery designs using standard cell cycling methods requires a large amount of time and energy, requiring complete cycling of cells from production to end-of-life. This significantly slows down the development process, and limits the number of companies able to financially support development. Inefficiencies during the battery lifecycle include drops in manufacturing quality leading to lower yield, poor battery management system (BMS) analytics leading to shorter lifetimes than could otherwise be achieved, and higher costs for second-life repurposing, due to reliance on length cell cycling methods for battery grading. AI offers a range of use cases that are well positioned to improve these inefficiencies and help reduce development timelines, while also potentially providing access to a range of new battery materials and cell designs, as is explored in "AI-Driven Battery Technology 2027-2037: Technologies, Innovation, Applications and Opportunities".

 

 

 

 

 

The term 'AI' has seen significant hype in recent years, with most usage focusing on generative AI, specifically large language models (LLMs); however, AI can refer to a wide range of algorithms and technologies, with the broad definition of enabling smarter machines/software. Specific approaches include supervised learning methods such as artificial neural networks (ANN) and decision tree (DT) methods, as well as unsupervised methods such as principal component analysis. There is a range of technological approaches under the broad umbrella of AI, with IDTechEx's report highlighting their potential in the battery industry, as well as the differentiation approaches taken by different software players.

 

IDTechEx identifies a range of application areas for AI throughout the battery lifecycle. These can be broken down into five categories/stages: materials screening/discovery, cell testing/simulation, cell manufacturing/assembly, in-life diagnostics/analytics, and second-life assessment. Materials screening/discovery refers to the application of AI for selection of materials or formulations for battery components. Cell testing/simulation refers to AI for simulating cells, usually to expedite cell testing and design processes. Cell manufacturing/assembly refers to AI for development of smart manufacturing, including application of image analysis software to catch defects and manufacturing line limitations. In-life diagnostics/analytics is the most established AI-driven battery technology market, referring to AI used via cloud or within the battery management system (BMS) to analyze BMS data and provide diagnostics, extending battery lifetime and preventing hazards. Second-life assessment AI is software applied to expedite battery grading after initial end-of-life, to determine suitability for second-life deployment.

 

 

IDTechEx anticipates significant growth for all of these application areas over the next decade, although some technologies are more commercially mature than others. Materials screening is one of the least commercially mature technologies within the battery industry, although the theoretical value proposition of materials informatics for development of new battery chemistries and cell designs is significant. This is explored in significant detail throughout IDTechEx's report, "AI-Driven Battery Technology 2027-2037: Technologies, Innovation, Applications and Opportunities".

 

 

 

 

There are several significant challenges that have led to slower uptake of AI-driven battery technologies than might otherwise have been realised. One of the largest of these is industry inertia, with many AI-driven battery technologies requiring significant initial investment to deploy (e.g. high levels of data quality and quantity for material screening and smart manufacturing), while also offering less directly tangible value propositions than e.g. direct improvements to battery electrochemical parameters. It has proven to be a challenge for players in the space to prove their value proposition in an easily digestible way, as well as differentiate themselves from companies offering similar products/services.

 

 

In the new market report, "AI-Driven Battery Technology 2027-2037: Technologies, Innovation, Applications and Opportunities", IDTechEx provides analysis of AI-driven battery technology markets, including:

 

Market forecasts and analysis:

 

Application market trends:

 

Player analysis:

 

 

For more information on this report, including downloadable sample pages, please visit www.IDTechEx.com/AIBattTech or for the full portfolio of related research available from IDTechEx, see www.IDTechEx.com.