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Legislative emissions requirements, customer expectation and environmental concerns are driving the introduction of Hybrid Electric Vehicle (HEV) technologies. In the European market, where diesel powertrain technology has high penetration, Micro Hybrid technology, featuring engine stop/start plus regenerative charging, is attractive due to system cost versus CO2 emission benefits. The availability of the engine stop/start feature in real world usage depends on the control logic taking account of, for example, safety, comfort or other factors. The research reported here involved developing tools to analyze the duration of automatic engine stop events in real world usage taking account of the situations where automatic engine stop would be unavailable. These tools help determine the durability requirements for key system components, in particular the battery, and estimation of the likely fuel savings as a function of the system calibration.

In recent years we have witnessed increased discrepancy between fuel economy numbers reported in accordance with EPA testing procedures and real world fuel economy reported by drivers. The debates range from needs for new testing procedures to the fact that driver complaints create one-sided distribution; drivers that get better fuel economy do not complain about the fuel economy, but only the ones whose fuel economy falls short of expectations. In this paper, we demonstrate fuel economy improvements that can be obtained if the driver is properly sophisticated in the skill of driving. Implementation of SmartGauge with EcoGuide into the Ford C-MAX Hybrid in 2013 helped drivers improve their fuel economy on hybrid vehicles. Further development of this idea led to the EcoCoach that would be implemented into all future Ford vehicles.

Regenerative braking in hybrid electric vehicles is an essential feature to achieve the maximum fuel economy benefit of hybridization. During vehicle braking, the regenerative braking recuperates its kinetic energy, otherwise dissipated into heat due to friction brake, into electrical energy to charge the battery. The recuperation is realized by the driven wheels propelling, through the drivetrain, the electric motor as a generator to provide braking while generating electricity. “Rigid” connection between the driven wheels and the motor is critical to regenerative braking; otherwise the motor could drive the input of the transmission to a halt or even rotating in reverse direction, resulting in no hydraulic pressure for transmission controls due to the loss of transmission mechanical oil pump flow.

In recent years the fuel efficiency of modern hybrid electric vehicle (HEV) powertrains has progressed to a point where low voltage auxiliary electrical system loads have a pronounced impact on fuel economy (FE). While improving the energy consumption of an individual component may result in minor improvements, the collective optimization of such loads across a complete vehicle system can result in meaningful FE gains. Traditional methods using chassis dynamometer testing alone to quantify the impact of a specific auxiliary load can lead to issues where signal state changes are too small for accurate detection. This presents difficulties in accurately predicting the influence of such loads on FE of next-generation electrified vehicles under development. This paper describes a newly developed method where dynamometer test results are combined with computer simulation analyses to create a practical technique for assessing the impact of small changes in auxiliary load energy consumption.

Ford Motor Company has investigated a series hybrid electric vehicle (SHEV) configuration to move further toward powertrain electrification. This paper first provides a brief overview of the Vehicle System Controls (VSC) architecture and its development process. The paper then presents the energy management strategies that select operating modes and desired powertrain operating points to improve fuel efficiency. The focus will be on the controls design and optimization in a Model-in-the-Loop environment and in the vehicle. Various methods to improve powertrain operation efficiency will also be presented, followed by simulation results and vehicle test data. Finally, opportunities for further improvements are summarized.

The automotive industry is rapidly expanding its Hybrid, Plug-in Hybrid and Battery Electric Vehicle product offerings in response to meet customer wants and regulatory requirements. One way for electrified vehicles to have an increasing impact on fleet-level CO2 emissions is for their sales volumes to go up. This means that electrified vehicles need to deliver a complete set of vehicle level attributes like performance, Fuel Economy and range that is attractive to a wide customer base at an affordable cost of ownership. As part of “democratizing” the Hybrid and plug-In Hybrid technology, automotive manufacturers aim to deliver these vehicle level attributes with a powertrain architecture at lowest cost and complexity, recognizing that customer wants may vary considerably between different classes of vehicles. For example, a medium duty truck application may have to support good trailer tow whereas a C-sized sedan customer may prefer superior city Fuel Economy.

Hybrid Electric Vehicles (HEV) offer improved fuel efficiency compared to their conventional counterparts at the expense of adding complexity and at times, reduced total power. As a result, HEV generally lack the dynamic performance that customers enjoy. To address this issue, the paper presents a HEV with eAWD capabilities via the use of a torque vectoring electric rear axle drive (TVeRAD) unit to power the rear axle. The addition of TVeRAD to a front wheel drive HEV improves the total power output. To further improve the handling characteristics of the vehicle, the TVeRAD unit allows for wheel torque vectoring at the rear axle. A bond graph model of the proposed drivetrain model is developed and used in co-simulation with CarSim. The paper proposes a control system which utilizes tire force optimization to allocate control to each tire. The optimization algorithm is used to obtain optimal tire force targets to at each tire such that the targets avoid tire saturation.