Jeffrey Gonder

Energy Management Strategies for Plug-In Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) differ from hybrid vehicles (HEVs) with their ability to use off-board electricity generation to recharge their energy storage systems. In addition to possessing charge-sustaining HEV operation capability, PHEVs use the stored electrical energy during a charge-depleting operating period to displace a significant amount of petroleum consumption. The particular operating strategy employed during the charge-depleting mode will significantly influence the component attributes and the value of the PHEV technology. This paper summarizes three potential energy management strategies, and compares the implications of selecting one strategy over another in the context of the aggressiveness and distance of the duty cycle over which the vehicle will likely operate.

Using Global Positioning System Travel Data to Assess Real-World Energy Use of Plug-In Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) have received considerable recent attention for their potential to reduce petroleum consumption significantly and quickly in the transportation sector. Analysis to aid the design of such vehicles and predict their real-world performance and fuel displacement must consider the driving patterns the vehicles will typically encounter. This paper goes beyond consideration of standardized certification cycless by leveraging state-of-the-art travel survey techniques that use Global Positioning System (GPS) technology to obtain a large set of real-world drive cycles from the surveyed vehicle fleet. This study specifically extracts 24-h, second-by-second driving profiles from a set of 227 GPS-instrumented vehicles in the St. Louis, Missouri, metropolitan area. The performance of midsize conventional, hybrid electric, and PHEV models is then simulated over the 227 full-day driving profiles to assess fuel consumption and operating characteristics of these vehicle technologies over a set of real-world usage patterns. In comparison to standard cycles used for certification procedures, the travel survey duty cycles include significantly more aggressive acceleration and deceleration events across the velocity spectrum, which affect vehicle operation and efficiency. Even under these more aggressive operating conditions, PHEVs using a blended charge-depleting energy management strategy consume less than 50% of the petroleum used by similar conventional vehicles. Although true prediction of the widespread real-world use of these vehicles requires expansion of the vehicle sample size and a refined accounting for the possible interaction of several variables with the sampled driving profiles, this study demonstrates a cutting-edge use of available GPS travel survey data to analyze the (highly drive cycle–dependent) performance of advanced technology PHEVs. This demonstration highlights new opportunities for using innovative GPS travel survey techniques and sophisticated vehicle system simulation tools to guide vehicle design improvements and to maximize the benefits offered by energy efficiency technologies.

Analyzing Vehicle Fuel Saving Opportunities through Intelligent Driver Feedback

Driving style changes, e.g., improving driver efficiency and motivating driver behavior changes, could deliver significant petroleum savings. This project examines eliminating stop-and-go driving and unnecessary idling, and also adjusting acceleration rates and cruising speeds to ideal levels to quantify fuel savings. Such extreme adjustments can result in dramatic fuel savings of over 30%, but would in reality only be achievable through automated control of vehicles and traffic flow. In real-world driving, efficient driving behaviors could reduce fuel use by 20% on aggressively driven cycles and by 5-10% on more moderately driven trips. A literature survey was conducted of driver behavior influences, and pertinent factors from on-road experiments with different driving styles were observed. This effort highlighted important driver influences such as surrounding vehicle behavior, anxiety over trying to get somewhere quickly, and the power/torque available from the vehicle. Existing feedback approaches often deliver efficiency information and instruction. Three recommendations for maximizing fuel savings from potential drive cycle improvement are: (1) leveraging applications with enhanced incentives, (2) using an approach that is easy and widely deployable to motivate drivers, and (3) utilizing connected vehicle and automation technologies to achieve large and widespread efficiency improvements.

An Analysis of Possible Energy Impacts of Automated Vehicles

Automated vehicles (AVs) are increasingly recognized as having the potential to decrease carbon dioxide emissions and petroleum consumption through mechanisms such as improved efficiency, better routing, and lower traffic congestion, and by enabling advanced technologies. However, AVs also have the potential to increase fuel consumption through effects such as longer distances traveled, increased use of transportation by underserved groups, and increased travel speeds. Here we collect available estimates for many potential effects and use a modified Kaya Identity approach to estimate the overall range of possible effects. Depending on the specific effects that come to pass, widespread AV deployment can lead to dramatic fuel savings, but has the potential for unintended consequences.

Route-Based Control of Hybrid Electric Vehicles

Today’s hybrid electric vehicle (HEV) controls do not necessarily provide maximum fuel savings over all drive cycles. An approach that employs route-based control could improve HEV efficiency at potentially minimal additional cost. This paper evaluates a range of routebased control approaches and identifies look-ahead strategies (using input from “on-the-fly” route predictions) as an area meriting further analysis. A novel implementation approach is developed and discussed, and a comparison with simulation results using an optimized general control setting indicates that fuel savings of approximately 2% to 4% can be obtained with route-based control. Given the increasing prevalence of GPS systems in vehicles, this advance has the potential to provide considerable aggregate fuel savings if applied across the entire national fleet. For instance, a 3% across-the-board reduction in HEV fuel use would save nearly 6.5 million gallons of fuel annually in the United States. These estimated savings will increase further as HEVs achieve greater market penetration.

FASTSim: A Model to Estimate Vehicle Efficiency, Cost and Performance

The Future Automotive Systems Technology Simulator (FASTSim) is a high-level advanced vehicle powertrain systems analysis tool supported by the U.S. Department of Energy’s Vehicle Technologies Office. FASTSim provides a quick and simple approach to compare powertrains and estimate the impact of technology improvements on light- and heavy-duty vehicle efficiency, performance, cost, and battery batches of real-world drive cycles. FASTSim’s calculation framework and balance among detail, accuracy, and speed enable it to simulate thousands of driven miles in minutes. The key components and vehicle outputs have been validated by comparing the model outputs to test data for many different vehicles to provide confidence in the results. A graphical user interface makes FASTSim easy and efficient to use. FASTSim is freely available for download from the National Renewable Energy Laboratory’s website (see www.nrel.gov/fastsim).

Simulated fuel economy and performance of advanced hybrid electric and plug-in hybrid electric vehicles using in-use travel profiles

As vehicle powertrain efficiency increases through electrification, consumer travel and driving behavior have significantly more influence on the potential fuel consumption of these vehicles. Therefore, it is critical to have a good understanding of in-use or “real world” driving behavior if accurate fuel consumption estimates of electric drive vehicles are to be achieved. Regional travel surveys using Global Positioning System (GPS) equipment have been found to provide an excellent source of in-use driving profiles. In this study, a variety of vehicle powertrain options were developed and their performance was simulated over GPS-derived driving profiles for 783 vehicles operating in Texas. The results include statistical comparisons of the driving profiles versus national data sets, driving performance characteristics compared with standard drive cycles, and expected petroleum displacement benefits from the electrified vehicles given various vehicle charging scenarios.

Drive Cycle Analysis, Measurement of Emissions and Fuel Consumption of a PHEV School Bus

The National Renewable Energy Laboratory (NREL) collected and analyzed real-world school bus drive cycle data and selected similar standard drive cycles for testing on a chassis dynamometer. NREL tested a first-generation plug-in hybrid electric vehicle (PHEV) school bus equipped with a 6.4L engine and an Enova PHEV drive system comprising a 25-kW/80 kW (continuous/peak) motor and a 370-volt lithium ion battery pack. A Bluebird 7.2L conventional school bus was also tested. Both vehicles were tested over three different drive cycles to capture a range of driving activity. PHEV fuel savings in charge-depleting (CD) mode ranged from slightly more than 30% to a little over 50%. However, the larger fuel savings lasted over a shorter driving distance, as the fully charged PHEV school bus would initially operate in CD mode for some distance, then in a transitional mode, and finally in a charge-sustaining (CS) mode for continued driving. The test results indicate that a PHEV school bus can achieve significant fuel savings during CD operation relative to a conventional bus. In CS mode, the tested bus showed small fuel savings and somewhat higher nitrogen oxide (NOx) emissions than the baseline comparison bus.

Contribution of Road Grade to the Energy Use of Modern Automobiles Across Large Datasets of Real-World Drive Cycles

Understanding the real-world power demand of modern automobiles is of critical importance to engineers using modeling and simulation to inform the intelligent design of increasingly efficient powertrains. Increased use of global positioning system (GPS) devices has made large scale data collection of vehicle speed (and associated power demand) a reality. While the availability of real-world GPS data has improved the industrys understanding of in-use vehicle power demand, relatively little attention has been paid to the incremental power requirements imposed by road grade. This analysis quantifies the incremental efficiency impacts of real-world road grade by appending high fidelity elevation profiles to GPS speed traces and performing a large simulation study. Employing a large real-world dataset from the National Renewable Energy Laboratorys Transportation Secure Data Center, vehicle powertrain simulations are performed with and without road grade under five vehicle models. Aggregate results of this study suggest that road grade could be responsible for 1% to 3% of fuel use in light-duty automobiles.

Data-Driven Reinforcement Learning–Based Real-Time Energy Management System for Plug-In Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off between real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. A case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.