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Intelligent Power Systems

Annual Plan

Integrated Transient Control and Thermal Management of Autonomous Off‐Road Vehicle Propulsion Systems

Project Summary

Principal Investigators

  • Robert Prucka (PI), Clemson University
  • Chris Edrington, Qilun Zhu, Yasha Parvani, Gokhan Ozkan (Co‐ PIs) Clemson University


  • Hamidreza Mirzaei, Payam Badr, Atharva Ghate, Clemson University


  • Denise Rizzo, Vamshi Korivi, U.S. Army GVSC


  • TBD

Project begins June 2020, estimated duration 2 years.

Powertrains for autonomous off‐road vehicles need to produce extremely high‐power outputs for short periods to meet propulsion and on‐board electrical system demands. High instantaneous power requirements stem from pulsed electrical loads, short‐term tractive force requirements to clear obstacles, high steering loads for tracked off‐road vehicles, and the high acceleration rates possible without the constraints of human driver/passenger fatigue. High instantaneous power requirements create significant challenges for coordination of energy resources, especially in a cooling‐constrained environment.

Current electro‐mechanical powertrain control and calibration techniques are highly empirical, often ad hoc in nature, and do not account for drive‐cycle look‐ahead information. These methods become very restrictive as the powertrain and electrical power system complexity increases, leading to long development times and a low probability that the system is working optimally at any given time. State‐of‐the‐art for automotive powertrain control is to utilize optimal control algorithms for individual sub‐ systems (e.g. the engine air path) within the powertrain. Using real‐time optimization to improve energy efficiency and thermal management has also been discussed extensively in literature, but with limited experimental support and production implementation due to the difficulty of finding global optimal solutions with limited computational time. This research will address these issues by leveraging the recent development in artificial intelligence to improve the computational efficiency of the optimization‐based control strategies. Our previous research demonstrated computation time reduction by one order of magnitude.

This research focuses on developing real‐time optimization strategies that account for individual component and system response and ensure fast and efficient torque delivery and high‐quality electrical power within thermal constraints of the powertrain. This research will bring together all systems of a hybrid vehicle, including energy storage, batteries, fuel cells, driveline, and internal combustion engines. Of particular focus is the management of components to lower thermal footprint while meeting powertrain and electrical system objectives, thereby minimizing needed package space and/or cooling requirements. The control methodologies developed will take advantage of forward‐looking information, when available from autonomous sensing systems, to better optimize powertrain efficiency, cooling and electrical energy delivery.

The fundamental questions to be addressed by this research are:

  • How can optimal control algorithms be designed that can incorporate control of mechanical and electrical systems with widely varying response times?
  • What is the proper way to design an optimization cost function such that simultaneous control of energy, electrical power and heat rejection can be achieved?
  • Is it possible to develop complete powertrain algorithms that are effective, but have low computational load?
  • What is the preferred methodology to transfer electrical power from vehicle to vehicle to minimize transfer losses?