Aaron Bollinger
Bio
Aaron Bollinger is the Assistant Vice President of Automotive and Diversified Engineering at East Penn Manufacturing. Aaron started his career in the Automotive Battery Test Laboratory. He then held positions in Applications Engineering, serving automotive aftermarket companies, and Product Design Engineering, developing powersports and auxiliary products. He recently held management positions in Automotive Process Engineering before his current role. Aaron has a bachelor’s degree in Physics from Kutztown University, and a master’s degree in Systems Engineering from Penn State, as well as certificates from Rutgers and Lehigh University.
The application of LV (low voltage) auxiliary batteries in vehicles with increasing degrees of powertrain electrification up to full battery electric vehicles will require robust battery systems to provide energy for safety critical functions. To meet this developing application, improvements must be made in product consistency and reliability. Opportunities for improvement can be found in both product and process design. When improving the product design, it is important to understand the requirements of the application and have methodologies to evaluate the fitness for use. Part of this presentation will discuss the industry activities underway to characterize the application and East Penn’s experience in supplying into the auxiliary application. The design can be improved through structured design methodologies like Design for Manufacturability and Prototyping. These methodologies and their applicability to the lead battery space will be reviewed. The manufacturing process can be evaluated through methodologies like FMEA and Failure Tree Analysis. These techniques help identify key product components and characteristics, as well as identify the way in which processes or components can fail. The benefit of these methodologies will be discussed, and real-world lead battery examples will be presented. The changes from the xEV Auxiliary application will push the existing manufacturing processes to perform more precisely and repeatably. To meet these challenges, the equipment and process need to be critically reviewed. Modern technology will play a role in improving the ability to monitor and control the process, such as advanced data science techniques. Defect detection can be improved by utilizing machine learning algorithms and advanced vision systems. Meeting these challenges is crucial for success in the xEV Auxiliary Battery application. These topics and the impact on the lead battery industry will be discussed.
