A predictive control strategy based on A-ECMS to handle Zero-Emission Zones: Performance assessment and testing using an HiL equipped with vehicular connectivity. (15th June 2023)

• Record Type: Journal Article
• Authors: Brunelli, Lorenzo; Capancioni, Alessandro; Canè, Stella; Cecchini, Giammarco; Perazzo, Alessandro; Brusa, Alessandro; Cavina, Nicolò
• Is Part Of: Applied energy. Volume 340(2023)
• Publication Date: 2023-06-15
• Subjects: Zero-Emission Zones -- Hybrid electric vehicles -- Adaptive-ECMS -- Vehicular connectivity -- Hardware-in-the-Loop; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2023.121008
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Simulation environment with real vehicular connectivity and prototype control units. Speed Profile Prediction based on real-time traffic and navigation data. Energy Management Strategies to allow electric drive within an urban area. Reduction of CO2 production between 20% and 26% Abstract: Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. So, the risk of not having enough energy stored to carry out the driving mission and then paying a fee is substantial. This work presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. The tests have been conducted with different initial SoC values for each strategy, showing a maximum error in the SoC prediction of 2.4% and up to 26.1% of CO2 saving with the A-ECMS.