Automated lane change assist combined with adaptive cruise control has the potential to reduce energy consumption and improve safety. This paper models adaptive cruise control combined with automated lane change assist to investigate the energy consumption improvements that such a system may provide compared to conventional adaptive cruise control. Automatically executing a lane change may improve efficiency, for example, when following a vehicle that is slowing to make a turn. Changing lanes while maintaining speed should be more efficient than staying in the same lane as the turning vehicle and reducing speed. The differences in such scenarios are simulated in a virtual environment using a cuboid model with idealized sensors. The ego-vehicle will detect scenarios, evaluate if a lane change is feasible, and possibly perform a lane change to reduce or eliminate required speed changes. The results of the simulations compare the energy content of the resulting drive cycle as an idealized method to measure energy consumption for each cruise control strategy. The simulations consider traffic laws, such as turn signal requirements that may dictate…

Onboard sensing and external connectivity using Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) technologies will allow a vehicle to "know" its future operating conditions with some degree of certainty, greatly narrowing prior information gaps. The increased development of such Connected and Automated Vehicle (CAV) systems, currently used mostly for safety and driver convenience, presents new opportunities to improve the energy efficiency of individual vehicles. The NEXTCAR program is one such initiative by the Advanced Research Projects Agency – Energy (ARPA-E) to developed advanced vehicle dynamics and powertrain control technologies that leverage such connected information streams. Southwest Research Institute (SwRI) in collaboration with Toyota and University of Michigan is currently working on improving energy consumption of a Toyota Prius Prime 2017 by 20%. This paper provides an overview of the various algorithms that have been developed to achieve the energy consumption target. A breakdown of how individual algorithms contribute to the overall target is presented. The team built a specialized test-bed called CAV dynamometer that integrates a traffic simulator and a hub dynamometer for testing the…

Range-extended hybrids are an attractive option for medium- and heavy-duty (M/HD) commercial vehicle fleets because they offer the efficiency of an electrified powertrain and accessories with the range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy award for M/HD Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric (PHEV) class 6 truck with a range-extending engine designed for pickup and delivery application. The National Renewable Energy Laboratory (NREL) assisted by developing a representative work day drive cycle for class 6 operation and adapting it to enable track testing. A novel, automated driving system was developed and utilized by Southwest Research Institute (SwRI) to improve the repeatability of vehicle track testing used to quantify vehicle energy consumption. Cummins…

Connected and automated driving technology is known to improve real-world vehicle efficiency by considering information about the vehicle’s environment such as traffic conditions, traffic lights or road grade. This study shows how the powertrain of a hybrid-electric vehicle realizes those efficiency benefits by developing methods to directly measure transient real-time efficiency and power losses of the vehicle’s powertrain components through chassis-dynamometer testing. This study is a follow-on to SAE Technical Paper 2019-01-0116, Test Methodology to Quantify and Analyze Energy Consumption of Connected and Automated Vehicles, to understand the sources of efficiency gains resulting from connected and automated vehicle driving. A 2017 Toyota Prius Prime was instrumented to collect power measurements throughout its powertrain and driven over a specific driving schedule on a chassis dynamometer. The same driving schedule was then modified to simulate a connected and automated vehicle driving profile, and the sources of vehicle efficiency improvements are analyzed. While conventional powertrain components typically only have two sources and sinks of power, e.g. an input and output shaft, the components of modern hybrid-electric vehicles are…

In the development of an Adaptive Cruise Control (ACC) system, a model-based design process uses a simulation environment with models for sensor data, sensor fusion, ACC, and vehicle dynamics. Previous work has sought to control the dynamics between two vehicles both in simulation and in empirical testing environments. This paper outlines a new modular simulation framework for full model-based design integration, to iteratively design ACC systems. The simulation framework uses physics-based vehicle models to test ACC systems in three ways. The first two are Model-in-the-Loop (MIL) testing, using scripted scenarios or Driver-in-the-Loop (DIL) control of a target vehicle. The third testing method uses collected test data replayed as inputs to the simulation to additionally test sensor fusion algorithms. The simulation framework uses 3D visualization of the vehicles and implements mathematical driver comfortability models to better understand the perspectives of the driver or passenger. The addition of a high-fidelity vehicle plant model provides energy consumption and emissions predictions for autonomous, conventional vehicles or hybrid electric vehicles (HEV) in realistic driving scenarios. Finally, the simulations are run…

This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND) and in-situ vehicle parameter characterization. Tools at the powertrain level include PHEV mode blending, predictive drive-unit state control, and non-linear model predictive control powertrain torque split management. These tools were developed with the capability of being implemented in a real-time vehicle control system. As a result, many of the developed technologies have been demonstrated in real-time…

The research on coasting resistance is vital to electric vehicles, since the smaller the coasting resistance, the longer the coast-down distance. Vehicle resistance consists of rolling resistance, vehicle inner resistance and the aerodynamic drag. The vehicle inner resistance is mainly caused by driveline’s friction loss and oil splash loss. The rolling resistance is decided by tire resistance coefficient, which is influenced by tires and road conditions. And the aerodynamic drag is affected by vehicle’s shape and air. In this paper, four factors that are tire pressure, road surface condition, air circulating mode, and atmosphere temperature are examined. Experimental tests have been conducted on three different vehicles: one subcompact sedan, one compact sedan and one compact SUV. The outcome shows that, when the tire pressure is 20% less, the average coasting resistance is increased by 1% to 3% depending on vehicle types, which indicates an increase in energy consumption by 0.9% to 2.4%. On wet road surface, the average coasting resistance is increased by 10% - 20%, which could decrease the NEDC range by 6% to…

One of the main parasitic loads on the engine in a vehicle is the air-condition. In this paper, we propose an open-source Matlab/Simscape model for the air-condition system of a vehicle. The model leverages recent improvements in the two-phase modeling in Simscape. It is developed by utilizing the basic HVAC components of compressor, condenser, evaporator and expansion valve that are provided in the library of Simscape. The dynamics are simulated by incorporating climate and cabinet conditions in the vehicle. The model is tuned based on experimental data collected on a 2014 Chevy Cruz. The model is then used to develop a model predictive control (MPC) to minimize energy consumption while maintaining a good temperature reference tracking in the cabinet. The developed MPC is deployed on a microcontroller integrated in the Chevy Cruz. Experimental results for the controller are provided in addition to the site to download the model from. The tradeoff on performance aspects between traditional PID Controller and linear Model Predictive Control (MPC) for the vehicle is also described in the paper. Both the…

Driving cycles are typically defined as time profiles of vehicle velocity, and as such they reflect basic driving characteristics. They have a wide application from the perspective of both conventional and electric road vehicles, ranging from prediction of fuel/energy consumption (e.g. for certification purposes), estimation of greenhouse gas and pollutant emissions to selection of optimal vehicle powertrain configuration and design of its control strategy. In the case of electric vehicles, the driving cycles are also applied to determine effective vehicle range, battery life period, and charging management strategy. Nowadays, in most applications artificial certification driving cycles are used. As they do not represent realistic driving conditions, their application results in generally unreliable estimates and analyses. Therefore, recent research efforts have been directed towards development of statistically representative synthetic driving cycles derived from recorded GPS driving data. The state-of-the-art synthesis approach is based on Markov chains, typically including vehicle velocity and acceleration as Markov chain states. However, apart from the vehicle velocity and acceleration, a road slope and vehicle mass are also shown to significantly impact…

The technology for self-driving cars and other highly-automated applications are becoming more and more advanced. At the same time, Electrical/Electronic (E/E) architectures are becoming more complex. Classical decentralized E/E architectures based on a large number of Electronic Control Units (ECU) represent an obstacle for the realization of new applications due to the computational power, energy consumption, weight, and the size of embedded components constraints in the automotive industry. Therefore the adoption of new embedded centralized E/E architectures represents a new opportunity to tackle these challenges. However, they also raise concerns and questions about their safety, hence, an appropriate evaluation must be performed to guarantee that safety requirements resulting from an Automotive Safety Integrity Level (ASIL) according to the standard ISO 26262 are met. In this paper, an evaluation of a dynamically reconfigurable system implemented on a centralized architecture is presented. The parameters evaluated are centered in reliability, probability of failure and possible trade-offs through the implementation of redundancy into reprogrammable devices and its performance parameters. The method used is divided into three stages. The first…