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This paper includes analysis results for the control strategy of the Peugeot 3008 Hybrid4, a diesel-electric hybrid vehicle, under different thermal conditions. The analysis was based on testing results obtained under the different thermal conditions in the Advanced Powertrain Research Facility (APRF) at Argonne National Laboratory (ANL). The objectives were to determine the principal concepts of the control strategy for the vehicle at a supervisory level, and to understand the overall system behavior based on the concepts. Control principles for complex systems are generally designed to maximize the performance, and it is a serious challenge to determine these principles without detailed information about the systems. By analyzing the test results obtained in various driving conditions with the Peugeot 3008 Hybrid4, we tried to figure out the supervisory control strategy.

This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.

In order to determine the factors that affect fuel economy quantitatively, the power flows through the major powertrain components were measured during operation over transient cycles. The fuel consumption rate and torque and speed of the engine output and axle shafts were measured to assess the power flows in a vehicle with a CVT. The measured power flows were converted to energy loss for each component to get the efficiency. Tests were done at Phase 1 and Phase 3 of the FTP and for two different CVT shift modes. The measured energy distributions were compared with those from the ADVISOR simulation and to results from the PNGV study. For both the Hot 505 and the Cold 505, and for both shift modes, the major powertrain loss occurs in the engine, including or excluding standby losses. However, the efficiency of the drivetrain/transmission is important because it influences the efficiency of the engine.

As emerging automated vehicle technology is making advances in safety and reliability, engineers are also exploring improvements in energy efficiency with this new paradigm. Powertrain efficiency receives due attention, but also impactful is finding ways to reduce driving losses in coordinated-driving scenarios. Efforts focused on simulation to quantify road load improvements require a sufficient amount of background validation work to support them. This study uses a practical approach to directly quantify road load changes by testing the coordinated driving of two vehicles on a test track at various speeds (64, 88, 113 km/h) and vehicle time gaps (0.3 to 1.3 s). Axle torque sensors were used to directly measure the load required to maintain steady-state speeds while following a lead vehicle at various gap distances.

This paper presents a comprehensive analysis of emerging powertrain technologies for a wide spectrum of vehicles, ranging from light-duty passenger vehicles to medium and heavy-duty trucks. The study focuses on the anticipated evolution of these technologies over the coming decades, assessing their potential benefits and impact on sustainability. The analysis encompasses simulations across a wide range of vehicle classes, including compact, midsize, small SUVs, midsize SUVs, and pickups, as well as various truck types, such as class 4 step vans, class 6 box trucks, and class 8 regional and long-haul trucks. It evaluates key performance metrics, including fuel consumption, estimated purchase price, and total cost of ownership, for these vehicles equipped with advanced powertrain technologies such as mild hybrid, full hybrid, plug-in hybrid, battery electric, and fuel cell powertrains.

The U.S. Environmental Protection Agency instrumented and tested a line-haul Class 8 tractor trailer on a 4-wheel-drive heavy-duty chassis dynamometer. A vehicle model was then developed in the Powertrain Systems Analysis Toolkit (PSAT), Argonne National Laboratory's vehicle simulation tool, using the truck technical specifications and the recorded data, which included the Portable Emissions Measurement System (PEMS) and Controller Area Network (CAN) signals. In this paper, we describe the test scenarios and the analysis performed on the data. We then present the vehicle model and assumptions. Finally, we compare the test and simulation data, including fuel consumption and component signals, as well as the main challenges specific to heavy-duty vehicle testing and simulation.