Search Results

The transition towards electrification in commercial vehicles has received more attention in recent years. This paper details the conversion of a production Medium-Duty class-5 commercial truck, originally equipped with a gasoline engine and 10-speed automatic transmission, into a battery electric vehicle (BEV). The conversion process involved the removal of the internal combustion engine, transmission, and differential unit, followed by the integration of an ePropulsion system, including a newly developed dual-motor beam axle that propels the rear wheels. Other systems added include an 800V/99 kWh battery pack, advanced silicon carbide (SiC) inverters, an upgraded thermal management system, and a DC fast charging system. A key part of the work was the development of the propulsion system controls, which prioritized drivability, NVH suppression, and energy optimization.

A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.

The assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.

Global regulatory targets and customer demand are driving the automotive industry to improve vehicle fuel efficiency. Methods for achieving increased efficiency include improvements in the internal combustion engine and an accelerating shift toward electrification. A key enabler to maximizing the benefit from these new powertrain technologies is proper systems integration work - including developing optimized controls for the propulsion system as a whole. The next step in the evolution of improving the propulsion management system is to make use of available information not typically associated with the powertrain. Advanced driver assistance systems, vehicle connectivity systems and cloud applications can provide information to the propulsion management system that allows a shift from instantaneous optimization of fuel consumption, to optimization over a route. In the current paper, we present initial work from a project being done as part of the DOE ARPA-E NEXTCAR program.

Low Pressure Cooled Exhaust Gas Recirculation (LP EGR) is an attractive technology to reduce fuel consumption for a spark-ignition (SI) engine, particularly at medium-to-high load conditions, due to its knock suppression and combustion cooling effects. However, the long LP EGR transport path presents a significant challenge to the transient control of LP EGR for the engine management system. With a turbocharged engine, this is especially challenging due to the much longer intake induction system path compared with a naturally aspirated engine. Characterizing and modeling the EGR, intake air mixing and transport delay behavior is important for proper control. The model of the intake air path includes the compressor, intercooler and intake plenum. It is important to estimate and track the final EGR concentration at the intake plenum location, as it plays a key role in combustion control. This paper describes the development of a real-time, implementable model for LP EGR estimation.

A key quantity for use in engine control is the exhaust manifold pressure. For production applications it is an important component in the calculation of the engine volumetric efficiency, as well as EGR flow and residual fraction. For cost reasons, however, it is preferable to not have to measure the exhaust manifold pressure for production applications. For that reason, it is advantageous to develop a model for estimating the exhaust manifold pressure in production application software that is small, accurate, and simple to calibrate. In this paper, a mean-value model for calculating the exhaust manifold pressure is derived from the compressible flow equation, treating the exhaust system as a fixed-geometry restriction between the exhaust manifold and the outlet of the tailpipe. Validation data from production applications is presented.

A dynamic EGR State Estimator (ESE) intended for production engine management systems (EMS) implementation is presented. It better describes the development of external exhaust gas recirculation (EGR) concentration at the engine intake ports during EGR transients than traditional models. The dynamics of EGR concentration time and spatial development in the intake manifold are described as a perfect mixing model in the intake manifold plenum volume and non-mixing plug flow in the intake manifold runners. The time scale of EGR transients precludes the use of traditional EGR measurement techniques for model verification. Instead a wide range air fuel (WRAF) sensor is used. Results are shown for a large variation in operating conditions and compared to the performance of a traditional model.

A Barometric Pressure Estimator (BPE) algorithm was implemented in a production speed-density Engine Management System (EMS). The BPE is a model-based, easily calibrated algorithm for estimating barometric pressure using a standard set of production sensors, thereby avoiding the need for a barometric pressure sensor. An accurate barometric pressure value is necessary for a variety of engine control functions. By starting with the physics describing the flow through the induction system, an algorithm was developed which is simple to understand and implement. When used in conjunction with the Pneumatic and Thermal State Estimator (PSE and TSE) algorithms [2], the BPE requires only a single additional calibration table, generated with an automated processing routine, directly from measured engine data collected at an arbitrary elevation, in-vehicle or on a dynamometer. The algorithm has been implemented on several different engines.