Eric Rask

Autonomie model validation with test data for 2010 Toyota Prius

The Prius — a power-split hybrid electric vehicle from Toyota — has become synonymous with the word “Hybrid.” As of October 2010, two million of these vehicles had been sold worldwide, including one million vehicles purchased in the United States. In 2004, the second generation of the vehicle, the Prius MY04, enhanced the performance of the components with advanced technologies, such as a new magnetic array in the rotors. However, the third generation of the vehicle, the Prius MY10, features a remarkable change of the configuration ― an additional reduction gear has been added between the motor and the output of the transmission [1]. In addition, a change in the energy management strategy has been found by analyzing the results of a number of tests performed at Argonne National Laboratory’s Advanced Powertrain Research Facility (ARRF). Whereas changes in the configuration, such as the reduction gear, are possibly noticeable, it is not easy to determine the effect of the energy management strategy because the supervisory control algorithm is, generally, not published. Further, it is almost impossible to analyze the algorithm without testing results obtained from a well-designed testing process. On the basis of extensive experience in designing the controllers of power-split hybrid electric vehicles in Autonomie, we could identify the supervisory control algorithm by analyzing the testing results obtained from the APRF. A vehicle model and a control model for the Prius MY10 have been developed to reproduce the real-world behaviors, and the simulation results are compared with the testing results. In the simulation, the developed vehicle model achieves fuel consumption that is close to the testing value, within 5%, and the operation of the engine model was similar to that of the real-world engine.

Vehicle-level control analysis of 2010 Toyota Prius based on test data

The Prius, a power-split hybrid electric vehicle developed by Toyota, has been the top-selling vehicle in the United States hybrid electric vehicle market for the last decade. The transmission system of the vehicle is a frequent theme of study for hybrid electric vehicles. However, the control concept of the vehicle is not well known, since analyzing control behaviors requires well-designed facilities to obtain testing results and well-defined processes to analyze the obtained results. Argonne National Laboratory has these resources and capabilities. In addition, Argonne has produced a reliable simulation tool, Autonomie, by which a vehicle model for the 2010 Prius is developed on the basis of the analyzed results, and it is validated with the results of testing. The developed model demonstrates that results of vehicle performance from simulation are close to those of from real-world tests—within 5%. The main focus of this study is to provide information about the supervisory control for the 2010 Prius, so that researchers can reproduce the real-world behavior of the vehicle through simulations. The analyzed control ideas based on the testing results will be very helpful in terms of understanding the control behavior of the vehicle, and the information resulting from this study is useful to develop the controller for the vehicle at a simulation level.

An investigation into the PNGV battery model with the addition of a dynamic temperature range

The Partnership for Next Generation-Vehicles Hybrid Pulse Power Characterization (HPPC) battery model was used to develop a base-line estimation. Within the HPPC is a method to establish an estimated open circuit voltage (VOC) from a linear calculation of internal resistances and currents. The flexibility of the parameterization was tested over a changing temperature range. Algorithms were developed that predict the VOC and state of charge (SOC) based on the batterys temperature, current draw, terminal voltage, and sampling time step; therefore, increasing the accuracy of the battery parameterization estimator (BPE). Three production vehicles with lithium based battery chemistries were used in the study. A least square method is used as in the PNGV battery model to determine initial parameters; an improvement was shown over this base-line by using a non-linear algorithm. The estimates from both algorithms were compared to the measured data as verification.

Fuel consumption effects of a diesel hybrid electric vehicle across a range of driving styles and ambient conditions

Reducing fuel consumption and thus greenhouse gas emissions is a major challenge for the automotive industry. To help achieve this goal, Diesel combustion engines as well as hybrid architectures are often used to further improve vehicle fuel efficiency. Combining these two technologies, the Peugeot 3008 Hybrid 4 was the first production vehicle utilizing a Diesel engine in a full hybrid powertrain with significant electric operating capability. This paper briefly introduces this vehicles hybrid topology and provides analysis regarding vehicle fuel consumption and operation over a large panel of drive cycles as well as hot (35°C) and cold (-7°C) ambient conditions.

Recent hybrid electric vehicle trends and technologies

In collaboration with the Department of Energys Advanced Vehicle Testing Activity, Argonne National Laboratory performs dynamometer testing and evaluation for a wide array of advanced vehicles. Using data obtained from this benchmarking, this paper discusses recently observed trends in hybrid electric vehicle technologies, discussing both overall vehicle trends as well as select component trends. This work discusses both “full” hybrids with a significant amount of electric (EV) or engine-off operation and more “mild” hybrids with a lesser amount of electric operating capability. This paper seeks to summarize some of the high-level operation trends that have been appearing in recent hybrid electric vehicles.

PHEV Energy Management Strategies at cold temperatures with Battery temperature rise and Engine efficiency Improvement considerations

Limited battery power and poor engine efficiency at cold temperature results in low plug in hybrid vehicle (PHEV) fuel economy and high emissions. Quick rise of battery temperature is not only important to mitigate lithium plating and thus preserve battery life, but also to increase the battery power limits so as to fully achieve fuel economy savings expected from a PHEV. Likewise, it is also important to raise the engine temperature so as to improve engine efficiency (therefore vehicle fuel economy) and to reduce emissions. One method of increasing the temperature of either component is to maximize their usage at cold temperatures thus increasing cumulative heat generating losses. Since both components supply energy to meet road load demand, maximizing the usage of one component would necessarily mean low usage and slow temperature rise of the other component. Thus, a natural trade-off exists between battery and engine warm-up. This paper compares energy management strategies for a power-split PHEV for their ability to warm –up the battery and the engine, and ultimately the resulting fuel economy. The engine model predicts engine fuel rate as a function of engine utilization history and starting temperature, apart from speed and torque. The battery temperature rise model is a function of battery utilization. Engine and battery utilization is varied by changing the control parameter - wheel power demand at which the engine turns ON. The paper analyses the sensitivity of fuel and electrical energy consumption to engine and battery temperature rise, for different driving distances and driver aggressivenes

Parameter Estimation for a Lithium-Ion Battery From Chassis Dynamometer Tests

Simulation techniques are extensively used in vehicle performance evaluations, particularly for advanced vehicles such as hybrid electric vehicles (HEVs), plug-in HEVs, and battery electric vehicles. It is necessary that the parameters used in simulation models are estimated properly so that the simulations can produce results that are close to real-world behaviors. This paper suggests methodologies for estimating parameters for a lithium-ion (Li-ion) battery model by analyzing test data obtained from chassis dynamometer tests. A representative model based on a first-order equivalent circuit is used for the battery, and four main parameters of the model-source voltage, internal resistance, polarization resistance, and polarization capacitance-are obtained by characterizing the test results. The model is validated with the test data by applying the estimated parameters in the model, and the validation results show that the battery output voltage is calculated by the simulation model very well; 70% of simulations produce the output voltage within 1% of the root-mean-square error, as compared with the test data. Although the methodology cannot replace a conventional process that estimates the battery parameters from dedicated tests, the approach would be very feasible and save the effort and time needed to develop simulation models for Li-ion batteries.

PHEV engine operational considerations for criteria emissions control and low fuel consumption

As one part of a large research program in plug-in hybrid-electric vehicles (PHEVs), Argonne National Laboratory has tested many conventional, hybrid-electric and plug-in hybrid-electric vehicles. Data were examined from several low-volume aftermarket PHEVs. Found in the study were trade-offs between minimizing emissions and reducing fuel consumption (by using more electrical propulsion). Key to successful emissions control is how the engine is operated during initial start, warm-up, and restart. Temperature management for engine restart is also important. Some data from charge-sustaining operation are contrasted with the charge-depleting results. The challenges in describing blended-PHEV fuel consumption are explored. This paper will examine the interesting challenges unique to blended-type PHEVs, illustrated with test data from several test vehicles.