Fuel Minimization of a Hybrid Electric Racing Car by Quasi-Pontryagin's Minimum Principle

This paper improves the fuel efficiency of a student-made parallel hybrid electric racing car whose internal combustion engine (ICE) either operates with peak efficiency or is turned off. The control to the ICE thus becomes a binary problem. Owing to the very limited computation resource onboard, the energy management strategy (EMS) for this car must have small time and space complexities. A computationally efficient controller that combines the advantages of dynamic programming (DP) and Pontryagin's minimum principle (PMP) is developed to run on a low-cost microprocessor. DP is employed offline to calculate the optimal speed trajectory, which is used as the reference for the online PMP to determine the real-time ICE on/off status and the electric motor (EM) torques. The normal PMP derives the optimal costate trajectory through solving partial differential equations. The proposed quasi-PMP (Q-PMP) method finds the costate from the value function obtained by DP. The fuel efficiency and computational complexity of the proposed controller are compared against several state of the art methods through both model-in-the-loop (MIL) and processor-in-the-loop (PIL) simulations. The new method reaches similar fuel efficiency as the explicit DP, but requires less than 1% onboard flash memory. The performance of the Q-PMP controller is compared between binary-controlled and continuously controlled ICEs. It achieves roughly 12% higher fuel efficiency for the binary ICE with only approximately 1/3 CPU utilization.

Automated summary

This paper introduces a computationally efficient controller for a student-made parallel hybrid electric racing car, achieving similar fuel efficiency as the explicit dynamic programming method while utilizing less than 1% onboard flash memory. The proposed controller combines dynamic programming and Pontryagin's minimum principle, running on a low-cost microprocessor with limited computational resources to optimize the energy management strategy.