The AAC2016 Benchmark - Look-Ahead Control of Heavy Duty Trucks on Open Roads

A combustion engine-driven vehicle can be made more fuel efficient over some drive cycles by, for example, introducing electric machines and solutions for electrical energy storage within the vehicle’s driveline architecture. The possible benefits of different hybridization concepts depend on the architecture, i.e., the type of energy storage, and the placement and sizing of the different driveline components. This paper examines a diesel electric plug-in hybrid truck, where the powertrain includes a diesel engine supported with two electric motors, one supporting the crank shaft and one the turbocharger. Numerical optimal control was used to find energy-optimal control strategies during two different accelerations; the trade-off between using electrical energy and diesel fuel was evaluated using a simulation platform. Fixed-gear acceleration was performed to evaluate the contribution from the two electric motors in co-operation, and individual operation. A second acceleration test case from 8 to 80 km/h was performed to evaluate the resulting optimal control behavior when taking gear changes into account. A cost factor was used to relate the cost of diesel fuel to electrical energy. The selection of the cost factor relates to the allowed usage of electrical energy: a high cost factor results in a high amplification from electrical energy input to total system energy savings, whereas a low cost factor results in an increased usage of electrical energy for propulsion. The difference between fixed-gear and full acceleration is mainly the utilization of the electric crank shaft motor. For the mid-range of the cost factors examined, the crank shaft electric motor is used at the end of the fixed-gear acceleration, but the control sequence is not repeated for each gear during the full acceleration. The electric motor supporting the turbocharger is used for higher cost factors than the crank shaft motor, and the amplification from electrical energy input to total energy savings is also the highest.

show abstract

“…The chassis model, gearbox and final drive ratios are from [24]. The driveline model is considered to be stiff.…”

Section: Chassismentioning

confidence: 99%

Electrification of a Heavy-Duty CI Truck—Comparison of Electric Turbocharger and Crank Shaft Motor

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to Hellström et al (2009), in economy mode, fuel consumption is decreased even more, but the travel time is increased [100]. Fuel consumption is obviously road---dependent; for example, Eriksson et al (2016) is formulating a benchmark problem for fuel---efficiency determination in the long---haul truck sector taking into account this factor [66]. On undulating roads, where a vehicle will often accelerate or decelerate by its own weight, it is harder to drive in a fuel---efficient manner.…”

Section: Driver Support Using Automationmentioning

confidence: 99%

Design for reduced resource consumption during the use phase of products

et al. 2017

View full text Add to dashboard Cite

Much work on sustainable design has focused on product manufacture/assembly and end of life. Gains in products' technical efficiency address the use phase, but how these products are used clearly affects resource consumption. There are two main approaches to design interventions to reduce resource consumption during product life. Firstly, interventions aim to change user behavior, through information and feedback, as well as physical product affordances abstracted from lead users to guide or steer users toward the desired behavior. Secondly, automatic adjustment of product systems performance levels based on personal user profiles and anticipated usage is implemented using artificial intelligence techniques.

show abstract

“…The reference driving cycles are the Heavy-Duty Södertälje-Norköping Drive Cycle (HDSNDC), the Heavy-Duty Urban Dynamometer Driving Schedule (HDUDDS), and Heavy Heavy-Duty Diesel Truck (HHDDT) cycle. In particular, the HDSNDC is a virtual driving profile derived from the benchmark problem in IFAC ACC Conference 2016 [15], as illustrated in Figure 3.…”

Section: Driving Cyclesmentioning

confidence: 99%

“…Two types of parallel hybrid electric vehicles are tested in this work: (1) a light-duty parallel hybrid electric vehicle, whose electric motor is engaged in the second gear of its gearbox; (2) a virtual heavy-duty truck from [15] that is hybridized with real powertrain components. The main characteristics of the light-and heavy-duty parallel hybrid electric powertrains are summarized in Table 2.…”

Section: Simulation Setupmentioning

confidence: 99%

GRAB-ECO for Minimal Fuel Consumption Estimation of Parallel Hybrid Electric Vehicles

Zhao

Sciarretta

Eriksson

2017

Oil & Gas Science and Technology - Rev. IFP Energies nouvel

Self Cite

View full text Add to dashboard Cite

-As a promising solution to the reduction of fuel consumption and CO 2 emissions in road transport sector, hybrid electric powertrains are confronted with complex control techniques for the evaluation of the minimal fuel consumption, particularly the excessively long computation time of the design-parameter optimization in the powertrain's early design stage. In this work, a novel and simple GRaphical-Analysis-Based method of fuel Energy Consumption Optimization (GRAB-ECO) is developed to estimate the minimal fuel consumption for parallel hybrid electric powertrains in light-and heavy-duty application. Based on the power ratio between powertrain's power demand and the most efficient engine power, GRAB-ECO maximizes the average operating efficiency of the internal combustion engine by shifting operating points to the most efficient conditions, or by eliminating the engine operation from poorly efficient operating points to pure electric vehicle operation. A turning point is found to meet the requirement of the final state of energy of the battery, which is charge-sustaining mode in this study. The GRAB-ECO was tested with both light-and heavy-duty parallel hybrid electric vehicles, and validated in terms of the minimal fuel consumption and the computation time. Results show that GRAB-ECO accurately approximates the minimal fuel consumption with less than 6% of errors for both light-and heavy-duty parallel hybrid electric powertrains. Meanwhile, GRAB-ECO reduces computation time by orders of magnitude compared with PMP-based (Pontryagin's Minimum Principle) approaches.

show abstract

The AAC2016 Benchmark - Look-Ahead Control of Heavy Duty Trucks on Open Roads

Cited by 16 publications

References 2 publications

Electrification of a Heavy-Duty CI Truck—Comparison of Electric Turbocharger and Crank Shaft Motor

Electrification of a Heavy-Duty CI Truck—Comparison of Electric Turbocharger and Crank Shaft Motor

Design for reduced resource consumption during the use phase of products

GRAB-ECO for Minimal Fuel Consumption Estimation of Parallel Hybrid Electric Vehicles

Contact Info

Product

Resources

About