Impact of Low and High Congestion Traffic Patterns on a Mild-HEV Performance

Abstract

Driven by stricter mandatory regulations on fuel economy improvement and emissions reduction, market penetration of electrified vehicles will increase in the next ten years. Within this growth, mild hybrid vehicles will become a leading sector. The high cost of hybrid electric vehicles (HEV) has somewhat limited their widespread adoption, especially in developing countries. Conversely, it is these countries that would benefit most from the environmental benefits of HEV technology. Compared to a full hybrid, plug-in hybrid, or electric vehicle, a mild hybrid system stands out due to its maximum benefit/cost ratio. As part of our ongoing project to develop a mild hybrid system for developing markets, we have previously investigated improvements in drive performance and efficiency using optimal gearshift strategies, as well as the incorporation of high power density supercapacitors. In this paper, the fuel and emissions of a baseline conventional vehicle and mild hybrid electric vehicle (MHEV) are compared. The objective of this analysis is to compare the fuel economy and Greenhouse Gas (GHG) emissions of the baseline and MHEV models, using low and highdensity traffic patterns chosen for their similarity to traffic density profiles of our target markets. Results demonstrate the benefits of a lower ongoing cost for the HEV architecture. These advantages include torque-hole filling between gear changes, increased fuel efficiency and performance.

Comparative fuel economy, cost and emissions analysis of a novel mild hybrid and conventional vehicles

“…A highway cycle is not representative of real world driving in our target market and is not considered useful to this study. This is validated by prior literature, e.g., (46,47), which establishes that the percentage improvement in fuel economy by adopting mild hybrid configurations for highway cycles is significantly less than for city driving.…”

Energy Management Systems’ Modeling and Optimization in Hybrid Electric Vehicles

“…FTP75 cycle is a standardized test procedure in the US to evaluate the performance, fuel consumption, and emissions of light-duty vehicles in typical urban conditions such as stop-start traffic and low-speed driving [53,57]. HWFET driving cycle is a standardized test procedure used by the US Environmental Protection Agency (EPA) to evaluate the fuel consumption and emissions of vehicles by representing real-world highway driving conditions in a driving scenario which involves higher speeds and longer durations compared to the city driving cycles [6,53,58]. NEDC is a standardized test procedure developed by the EU in the 1970s to assess the efficiency, performance, fuel consumption, and emissions of vehicles in urban and non-urban driving conditions [53,59,60].…”

“…EMS aims to improve the drivability and overall user experience of HEVs, ensuring smooth power delivery and responsive performance [29,64,66,67,71]. It can adapt to different driving conditions and traffic patterns by optimizing the operation of the powertrain for maximum efficiency and performance [6,11,58]. It can incorporate real-time traffic information to optimize power distribution and improve energy efficiency according to driving conditions [52,72,73].…”

“…It considers various constraints such as power demand, SoC, and system limitations to ensure the safe and reliable operation of HEVs [6,53,64,68,74]. EMSs can optimize electric motor and battery utilization, allowing for longer electric driving range in HEVs [10,58,75,76]. EMSs maximize the capacity and extend the lifetime of the battery by enabling its efficient use in HEVs [12,29,67,74,[77][78][79].…”

Using a low-cost bluetooth torque sensor for vehicle jerk and transient torque measurement

“…The drive cycle testing methodology is well-described in literature. 2,[15][16][17] The overall structure of the powertrain model A high-level view of the simulation environment is shown in Figure 13. The simulation environment fully describes a conventional vehicle in its testing environment and implements a forward-looking modelling approach, wherein the driver model is used to provide throttle, braking, gear selection and clutch position input to the simulation, according to a pre-determined vehicle drive cycle (vehicle speed vs time).…”