As the battery market continues to expand rapidly, so does the importance of optimizing long-term performance. For OEMs, battery pack manufacturers, electric fleet managers, and Electric Vehicle (EV) stakeholders, maximizing battery life depends on harnessing data effectively. By employing machine learning and data analytics methods, battery data can accurately assess, predict, and enhance battery life. Predictive intelligence and data analytics will be crucial in achieving high battery efficiency and operational reliability, especially with the increasing impact of artificial intelligence in battery technology. The Battery Intelligence for Automotive Applications conference will gather industry and academic leaders to explore how organizations can leverage battery intelligence to significantly and continually improve battery life.
Coverage will include, but is not limited to:
- Industry and Academic Perspectives
- Intelligent Chemistry and Materials
- Intelligent Manufacturing
- Data Strategy, Security, and Traceability
- Machine Learning for Batteries
- Diagnostic, Predictive, and Prescriptive Analytics
The deadline for priority consideration is May 24, 2024.
All proposals are subject to review by session chairpersons and/or the Scientific Advisory Committee to ensure the overall quality of the conference program. Additionally, as per Cambridge EnerTech’s policy, a select number of vendors and consultants who provide products and services will be offered opportunities for podium presentation slots based on a variety of Corporate Sponsorships.
Opportunities for Participation: