Implementation of Adaptive Equivalent Consumption Minimization Strategy

Electrification of vehicles is an important step towards making mobility more sustainable and carbon-free. Hybrid electric vehicles use an electric machine with an on-board energy storage system, in some form to provide additional torque and reduce the power requirement from the internal combustion engine. It is important to control and optimize this power source split between the engine and electric machine to make the best use of the system. This paper showcases an implementation of the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with minimization in real-time in the dSPACE MicroAutobox II as the Hybrid Supervisory Controller (HSC). While the concept of A-ECMS has been well established for many years, there are no published papers that present results obtained in a production vehicle suitably modified from conventional to hybrid electric propulsion including real world testing as well as testing on regulatory cycles. This paper details all the supportive algorithms that made this possible and the specific components to achieve the A-ECMS implementation and outlines design and performance details.

A-ECMS was tested for 1400+ miles of vehicle testing with 130,000+ miles on virtual environments (Model-in-Loop and Hardware-in-Loop) to determine the best calibration parameters for the battery-fuel equivalency factor and the penalties to achieve the best fuel economy. This strategy was implemented on a 2019 Chevrolet Blazer modified into a hybrid by The Ohio State University's EcoCAR team. Several comparisons are presented between the vehicle with A-ECMS and a stock vehicle. The project vehicle showed 6% fuel improvement in city cycles over the stock vehicle and 17% over the standardized FTP Drive Cycle on a two-axle dynamometer.