Abstract
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.
Highlights
H YBRID electric vehicles (HEVs) have shown compelling potentials on energy conservation and emission reduction as the electric motor (EM) on the powertrain allows the internal combustion engine (ICE) to operate with high efficiency
Aiming at a parallel hybrid electric racing car with a BCICE, this paper proposes a Q-Pontryagin’s minimum principle (PMP) control method to improve its fuel efficiency on a prescribed racing track
dynamic programming (DP) provides an optimal HEV speed trajectory as the reference so that the original optimal control problem (OCP) is simplified to a great extent; on the another hand, a 3D lookup table of value function from the DP solutions is used to estimate the optimal costate for the Q-PMP controller which is responsible for determining the real-time ICE on/off status and the supplementary torque by the EMs
Summary
H YBRID electric vehicles (HEVs) have shown compelling potentials on energy conservation and emission reduction as the electric motor (EM) on the powertrain allows the internal combustion engine (ICE) to operate with high efficiency. Rule-based strategies are preferred mainly because of their low cost on development, rapid execution for online control, and wide applicability to the fast-changing driving conditions [12]-[14] These controllers have shown their advantages on ICE efficiency improvement and robust operation on power mode switching but suffer the limitations of not realizing the maximal fuel efficiency or violating some system constraints. Global optimization methods, such as dynamic programming (DP) [15]-[17], Pontryagin’s minimum principle (PMP) [18]-[20], simulated annealing [21], [22], particle swarm optimization (PSO) [23], [24], can generate a non-causal global optimal solution based on a priori information of the driving cycle.
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