Aim and goal of the study AI in the battery industry:
An increasing number of publications discusses the use of artificial intelligence (AI) and one of its technologies machine learning (ML) as the next disruptive tool for companies. While these, often until now hypothetical, use cases are very interesting, it is of importance to understand what works, what not and where and when an investment by companies in the technology pays off.
But - the use cases of AI/ML in the battery industry value chain are very interesting and worth investigating.
Overview
Outline in a PDF
Authors:
A. Eggerz and the team
Publication language:English
Publication date
Q1/2020
This statement is based on two constraints the energy storage industry is faced by:
First there are the challenges for battery development and production that are numerous. There is for example the questions around (new) materials, the battery chemistry, cell design as well as production at competitive cost, battery management systems, safety and charging cycles influenced by ageing behavior of the cell chemistry.
Then there is the innovation cycle known from hard-science driven industries. The problem is that progress in hard science is mostly slow and marked by a lot of failures. In addition, traditional research methods have been limited to measuring data in test environments while the elaboration and assessment of real data is limited by the capacity to collect and availability of highly qualified and
trained scientists and development engineers. Also, the pool of available talent is small and follows the educational cycle which is long.
While these are factors are influencing the overall industry, the competitive situation for European based participant of the battery industry is especially challenging due to the dominance of Asian
players. Thus, there is a key interest in advanced, new technologies leveling the competitive field helping to win competition.
To identify why this could be AI/ML and to what extend is the key objective of this study which starts with the research hypothesis that this in fact is possible.
First there are the challenges for battery development and production that are numerous. There is for example the questions around (new) materials, the battery chemistry, cell design as well as production at competitive cost, battery management systems, safety and charging cycles influenced by ageing behavior of the cell chemistry.
Then there is the innovation cycle known from hard-science driven industries. The problem is that progress in hard science is mostly slow and marked by a lot of failures. In addition, traditional research methods have been limited to measuring data in test environments while the elaboration and assessment of real data is limited by the capacity to collect and availability of highly qualified and
trained scientists and development engineers. Also, the pool of available talent is small and follows the educational cycle which is long.
While these are factors are influencing the overall industry, the competitive situation for European based participant of the battery industry is especially challenging due to the dominance of Asian
players. Thus, there is a key interest in advanced, new technologies leveling the competitive field helping to win competition.
To identify why this could be AI/ML and to what extend is the key objective of this study which starts with the research hypothesis that this in fact is possible.
The study about the economic impact of AI/ML in the battery industry will contain the following information and data points:
1) Overview of the cost/economic impact on each step of the battery value chain
2) Overview of projects in the field
3) Overview of patents
4) Overview on solution providers
We update the content index in July 2019 and will also publish the research agenda.
2) Overview of projects in the field
3) Overview of patents
4) Overview on solution providers
We update the content index in July 2019 and will also publish the research agenda.