Towards real-time optimal energy management of HEV powertrains using stochastic dynamic programming

An energy management controller based on shortest path stochastic dynamic programming (SP-SDP) is implemented and tested in a prototype vehicle. The controller simultaneously optimizes fuel economy and powertrain activity, namely gear shifts and engine on–off events. Previous work reported on the controller's design and its extensive simulation-based evaluation. This paper focuses on implementation of the controller algorithm in hardware. Practical issues concerning real-time computability, driver perception, and command timing are highlighted and addressed. The SP-SDP controllers are shown to run in real-time, gracefully handle variations in engine start and gear-shift-completion times, and operate in a manner that is transparent to the driver. A hardware problem with the test vehicle restricted its maximum engine torque, which prevented a reliable fuel economy assessment of the SP-SDP controller. The data that were collected indicated that SP-SDP controllers could be straightforwardly designed to operate at different points of the fuel economy tradeoff curve and that their fuel economy may equal or exceed that of a baseline industrial controller designed for the vehicle.

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On Implementing Optimal Energy Management for EREV Using Distance Constrained Adaptive Real-Time Dynamic Programming

Electronics ◽

10.3390/electronics9020228 ◽

2020 ◽

Vol 9 (2) ◽

pp. 228 ◽

Cited By ~ 1

Author(s):

Aman Kalia ◽

Brian Fabien

Keyword(s):

Dynamic Programming ◽

Energy Consumption ◽

Real Time ◽

Electric Vehicle ◽

Energy Management ◽

Management Control ◽

Time Dynamic ◽

Optimal Energy ◽

Optimal Energy Management ◽

Driving Range

Extended range electric vehicles (EREVs) operate both as an electric vehicle (EV) and as a hybrid electric vehicle (HEV). As a hybrid, the on-board range extender (REx) system provides additional energy to increase the feasible driving range. In this paper, we evaluate an experimental research EREV based on the 2016 Chevrolet Camaro platform for optimal energy management control. We use model-in-loop and software-in-loop environments to validate the data-driven power loss model of the research vehicle. A discussion on the limitations of conventional energy management control algorithms is presented. We then propose our algorithm derived from adaptive real-time dynamic programming (ARTDP) with a distance constraint for energy consumption optimization. To achieve a near real-time functionality, the algorithm recomputes optimal parameters by monitoring the energy storage system’s (ESS) state of charge deviations from the previously computed optimal trajectory. The proposed algorithm is adaptable to variability resulting from driving behavior or system limitations while maintaining the target driving range. The net energy consumption evaluation shows a maximum improvement of 9.8% over the conventional charge depleting/charge sustaining (CD/CS) algorithm used in EREVs. Thus, our proposed algorithm shows adaptability and fault tolerance while being close to the global optimal solution.

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