Electrochemical Energy Conversion & Storage Systems for Automotive Applications (ME7383)
This graduate course targets graduate engineering students or professionals who would like to receive a comprehensive and general exposure to the field of electrochemical energy conversion and storage systems. Particular relevance is given to energy storage systems for electrified vehicles based upon Lithium ion technology, covering cell materials and fundamental properties, testing procedures for performance characterization, modeling and simulation, pack design, system integration, control, diagnostics and safety.
Entry-level graduate background in thermodynamics, heat/mass transfer and system dynamics is necessary for this course, however no prior knowledge of electrochemistry for batteries and fuel cells is expected. All assignments will make use of Matlab/ Simulink, hence proficiency with this software is a must.
Upon completion of this course, participants will possess practical knowledge of:
- The operating principles and characteristics of Lithium-ion batteries, including the effects of electrode/electrolyte materials on performance and durability;
- The experimental methods for characterizing performance and life of Li-ion cells, in support of modeling, design and prototype verification;
- Modeling and simulation tools to solve system-level design and optimization problems for battery packs for EVs and HEVs;
- State of the art in battery pack design for automotive applications and methods for system integration and control (Battery Management Systems, charging and balancing strategies, SOC/SOH estimation).
- Basic principles of PEM fuel cell systems, and their applications in the automotive industry.
Canova’s work in energy optimization of advanced powertrains has led to significant fuel economy benefits and has been implemented in production programs by major OEMs. In addition, he has published over 110 articles in refereed journals and conference proceedings and received, among others, the SAE Vincent Bendix Automotive Electronics Engineering Award (2009), the SAE Ralph E. Teetor Educational Award (2016), the NSF CAREER Award (2016), the Lumley Research Award (2016) and the Michael J. Moran Award for Excellence in Teaching (2017).