Optimizing Energy Consumption in Electric Vehicles
Learn how to maximize the performance of traction drives and other high‑power loads in electric vehicles
In part one of this two-part, on-demand webinar series, we explored how EVSE (Electric Vehicle Supply Equipment), the On-Board Charger (OBC), and the Battery Management Unit (BMU) play key roles in combatting EV range anxiety. These subsystems convert and store energy. In this session, we focus on how to optimize the consumption of this stored energy through architectural approaches, the application of specific control algorithms, and semiconductor technology choices.
Since battery electric vehicles lack an internal combustion engine, not only are they propelled down the road differently but they also lack the availability to drive hydraulics from the crankshaft. This means that several vehicle systems must be implemented in a different way as well. The biggest consumers of energy in an EV include the traction drive and other high power, high duty-cycle loads like the compressor, braking, and steering, to name a few. In this session, our focus will be on motor and load control with an emphasis on power electronics, diagnostics, and the control algorithms that help bolster efficiency and combat range anxiety.
You will learn:
- The key performance indicators for traction drives and other heavy loads
- Motor control strategies to optimize efficiency
- Power switch technologies for motor control and their associated trade-offs
|John Johnson's career spans over 25 years in the semiconductor industry with roles in engineering, marketing, and management. At STMicroelectronics, he manages the Automotive Systems Marketing group for the Americas region. Prior to this, he was an engineer/manager developing communication system test equipment, primarily targeting mobile infrastructure and video distribution. John has a BSEE from Purdue University and is the author/co-author of several articles and papers addressing subject matter including vehicle electrification and video analytics, and signal chain design and processing.