Prius Plug-in Research Articles

Co-Optimization of On-Ramp Merging and Plug-In Hybrid Electric Vehicle Power Split Using Deep Reinforcement Learning

Current research on Deep ReinforcementLearning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics. This work considers automated on-ramp merging for a power-split Plug-In Hybrid Electric Vehicle (PHEV), the 2015 Toyota Prius Plug-In, using DRL. The on-ramp merging control and the PHEV energy management are co-optimized such that the DRL policy directly outputs the power split between the engine and the electric motor. The testing results show that DRL can be successfully used for co-optimization, leading to collision-free on-ramp merging. When compared with sequential approaches wherein the upper-level on-ramp merging control and the lower-level PHEV energy management are performed independently and in sequence, we found that co-optimization results in economic but jerky on-ramp merging while sequential approaches may result in collisions due to neglecting powertrain power limit constraints in designing the upper-level on-ramp merging controller.

Emission Implications of Plug-in Hybrid Electric Vehicles Through an Empirical Exploration of Engine Starts

This paper aims to characterize the engine start activity profiles and emission potential of various plug-in hybrid electric vehicle (PHEV) models by examining the characteristics associated with engine starts, identifying the travel conditions that trigger engine starts, and determining the frequency of different types of engine starts. The study analyzed on-road vehicle data from six PHEV models: Toyota Prius Plug-in, Ford C-Max Energi, Ford C-Max Fusion, Toyota Prius Prime, Chrysler Pacifica, and Chevrolet Volt. An analysis on travel conditions before engine starts revealed that low state-of-charge is the dominant engine start trigger for PHEVs with high all-electric range whereas high vehicle power requirement is the most critical trigger for PHEVs with low all-electric range. For PHEVs with mid-range capabilities, several vehicle specifications, ranging from peak electric motor power to curb weight, could be engine start determinants. A strong inverse correlation exists between battery capacity and the annual frequency of engine starts but this relationship does not hold for cold and high-power cold starts. Both the low and the high battery capacity PHEVs logged fewer cold starts than the mid-sized battery vehicles, indicating that there could be a fundamental tradeoff between engine start emissions and fuel displacement for PHEVs to a certain degree. Despite this tradeoff, all PHEV models in the study logged fewer cold starts than comparable conventional internal combustion engine vehicles, performing the same trips. Ultimately, long-range PHEVs with high battery capacity are found to be ideal for both curbing start emissions and reducing fuel use.

Variability in Measured Real-World Operational Energy Use and Emission Rates of a Plug-In Hybrid Electric Vehicle

Compared to comparably sized conventional light duty gasoline vehicles (CLDGVs), plug-in hybrid electric vehicles (PHEVs) may offer benefits of improved energy economy, reduced emissions, and the flexibility to use electricity as an energy source. PHEVs operate in either charge depleting (CD) or charge sustaining (CS) mode; the engine has the ability to turn on and off; and the engine can have multiple cold starts. A method is demonstrated for quantifying the real-world activity, energy use, and emissions of PHEVs, taking into account these operational characteristics and differences in electricity generation resource mix. A 2013 Toyota Prius plug-in was measured using a portable emission measurement system. Vehicle specific power (VSP) based modal average energy use and emission rates are inferred to assess trends in energy use and emissions with respect to engine load and for comparisons of engine on versus engine off, and cold start versus hot stabilized running. The results show that, compared to CLDGVs, the PHEV operating in CD mode has improved energy efficiency and lower CO2, CO, HC, NOx, and PM2.5 emission rates for a wide range of power generation fuel mixes. However, PHEV energy use and emission rates are highly variable, with periods of relatively high on-road emission rates related to cold starts.

Robust Predictive Battery Thermal Management Strategy for Connected and Automated Hybrid Electric Vehicles Based on Thermoelectric Parameter Uncertainty

The connected and automated hybrid electric vehicles (CAHEVs) have the potential to improve safety and mitigate traffic congestions. A crucial problem of the CAHEVs is that the lithium-ion batteries are highly temperature sensitive, whose output power is severely limited at low temperatures. Moreover, the cells may be premature aging at high operating temperatures and even result in an explosion, seriously threatening the human safety. Consequently, a practical and energy-efficient battery thermal management (BTM) strategy is required with the minimum possible cooling/heating energy consumption. To achieve the multiple objectives, a finite-set based model predictive control (FSMPC) strategy is presented for the BTM in CAHEVs. Since the thermoelectric model of the BTM system is highly nonlinear and time variant, an extended state observer is implemented for accurate system state estimation and prediction so that the parameter uncertainties inherent in the system can be compensated. The hardware-in-the-loop validation of the proposed strategy is conducted on the urban dynamometer drive schedule-based on a Toyota Prius plug-in HEV model. The results demonstrate that the proposed BTM strategy can maintain the battery pack in the CAHEV operating at the optimum temperature and save 30% BTM energy compared to the conventional method even under 50% parameter uncertainty.

Comparative study on energy consumption for plug-in hybrid electric vehicles based on trip characteristics

In this paper, an evaluation method for energy consumption comparison of plug-in hybrid electric vehicle (PHEV) and the result of energy consumption comparison of two PHEVs are presented based on trip characteristics of passenger vehicles in Harbin, China. Trip characteristics including daily vehicle kilometre travelled distribution, daily vehicle frequency travelled distribution and travel time distribution have been performed for comparison analysis. For a common use of two PHEVs with similar configuration, the work mode has a great difference. As a consequence, daily vehicle kilometre travelled in Harbin is 28.44 km, daily vehicle frequency travelled is 2.69 times and daily vehicle time travelled is 101.96 min. Furthermore, users' travel requirements with different all electric range and the different operation cost between two typical PHEVs were obtained. It was found that the comprehensive fuel consumption of the two PHEVs is 3.69 and 2.73 L/100km, respectively. Compared with Prius plug-in, integrated fuel consumption of Volt is reduced by 26.02% under Harbin trip characteristics.

Modeling Charging Choices of Small-Battery Plug-In Hybrid Electric Vehicle Drivers by Using Instrumented Vehicle Data

Methods for modeling plug-in hybrid electric vehicle drivers’ choices of whether to charge at the end of a trip are compared. Instrumented vehicle data from 125 preproduction Toyota Prius plug-in hybrids were combined with electric vehicle supply equipment (i.e., charging station) location data from multiple sources, including PlugShare and the U.S. Department of Energy. The effects of factors including battery state of charge (SOC), dwell time, and location on the probability that a driver will charge during a stop were then modeled for locations where charging was possible. The amount of energy that can be transferred during a charging session was a better predictor of charging behavior than SOC and dwell time considered independently. Results were sensitive to data and methods used to identify charging locations, which indicated that caution is needed in this area. Finally, a latent class logit model was investigated and found to provide a better fit to the observed data than did the mixed logit model. The results of the latent class model indicated that all users were more likely to charge after 8:00 p.m. and at times when doing so provided a larger gain in SOC. For some classes of users, charging was more likely at home or after the last trip of the day.

Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control

Plug-in hybrid electric vehicles (PHEVs) are promising options for future transportation. Having two sources of energy enables them to offer better fuel economy and fewer emissions. Significant research has been done to take advantage of future route information to enhance vehicle performance. In this paper, an ecological adaptive cruise controller (Eco-ACC) is used to improve both fuel economy and safety of the Toyota Prius Plug-in Hybrid. Recently, an emerging trend in the research has been to improve the adaptive cruise controller. However, the majority of research to date has focused on driving safety, and only rare reports in the literature substantiate the applicability of such systems for PHEVs. Here, we demonstrate that using an Eco-ACC system can simultaneously improve total energy costs and vehicle safety. The developed controller is equipped with an onboard sensor that captures upcoming trip data to optimally adjust the speed of PHEVs. The nonlinear model predictive control technique (NMPC) is used to optimally control vehicle speed. To prepare the NMPC controller for real-time applications, a fast and efficient control-oriented model is developed. The authenticity of the model is validated using a high-fidelity Autonomie-based model. To evaluate the designed controller, the global optimum solution for cruise control problem is found using Pontryagin's minimum principle (PMP). To explore the efficacy of the controller, PID and linear MPC controllers are also applied to the same problem. Simulations are conducted for different driving scenarios such as driving over a hill and car following. These simulations demonstrate that NMPC improves the total energy cost up to 19%.

Energy and environmental characterization of operational modes of plug-in vehicles

This paper presents an energy and environmental characterization of the two most relevant Plug-in Hybrid Electric Vehicles available in the market (Opel Ampera and Toyota Prius Plug-in), in order to provide an estimate of fuel and electricity consumption, tailpipe emissions and charge depleting mode mileage for any drive cycle studied, based on vehicle specific power methodology. These vehicles were monitored under real-world operation with a portable laboratory that collects data from vehicle sensors (via on-board diagnosis port), exhaust gas composition and GPS in a second-by-second basis. An indirect method to measure battery energy fluxes and consequently estimating electric range was developed, providing maximum errors for the Charge Depleting driving range of 4.2% for the Toyota Prius Plug-in and -0.2% for the Opel, when comparing with measured data. Regarding fuel consumption, the maximum error verified was of -4.1%. Using two driving profiles measured in Portugal and the USA, the performance of the two vehicles under charge depleting (CD) and charge sustaining (CS) conditions was compared. Major findings indicate that Opel Ampera is more efficient in CD mode, while the Prius Plug-In is more efficient under CS conditions, but highly dependent on driving behavior when in CD mode.

Effectiveness of Plug-in Hybrid Electric Vehicle Validated by Analysis of Real World Driving Data

In recent years, the usage of various alternative energies is considered from a viewpoint of CO2 emission reduction. Toyota Motor Corporation considers that Plug-in Hybrid Electric Vehicles (PHEVs) is the best practical solution, and has launched Prius Plug-in Hybrid in January 2012. Prior to this, in December 2009, Toyota sold 650 PHEVs through lease programs for validation testing in Europe, the U.S. and Japan. As a result, not only the fuel reduction effect in case of real market usage was confirmed, but also the relationship between frequency of charging and fuel reduction effect. This paper discusses the analysis of the validation test results.

A comparative study on the power characteristics and control strategies for plug-in hybrid electric vehicles

A comparative study was performed on two types of plug-in hybrid electric vehicles (PHEVs): the GM Volt and the Toyota Prius Plug-in Hybrid. First, the powertrain models of the two vehicles were derived. Based on the dynamic models, a detailed component control algorithm was developed for each PHEV. Specifically, a control algorithm was proposed for motor generator 1 (MG1) and MG2 to achieve optimal engine operation. Additionally, an energy management strategy for selecting the operation mode was developed from the viewpoint of fuel economy, battery state of charge and vehicle velocity. Using the dynamic model of the control algorithm for each PHEV, simulations were performed, and the simulation results were verified by comparing them with those obtained using the Powertrain System Analysis Toolkit simulator for the plug-in Prius. Based on the simulation results, a comparative study was performed, and it was found that the role and capacity of MG1 and MG2 and the mode selection algorithm must be determined depending on the configuration of the PHEV.

Toyota Prius Plug-in

Die Plug-in Variante des Toyota Prius, das "Plug-in Hybrid Electric Vehicle" (PEHV), basiert auf dem Prius mit Vollhybrid-Antrieb. Vor einem in 2012 geplanten Serienstart testet Toyota mit 600 Fahrzeugen weltweit, 200 in Europa und rund 20 davon in Deutschland, unter anderem das Nutzerverhalten von Fahrern des Prius Plug-in-Hybrid. Werkseitig werden bis zu 20 km "elektrische Reichweite" und im Bestfall 2,6 l/km Kraftstoffverbrauch angegeben. Vorgestellt werden zum einen die neuen Entwicklungen im elektrischen und thermischen Energiemanagement zur Erreichung dieser Werte, zum anderen wird auf die moeglichen Fahrmodi und den Einfluss des Fahrerverhaltens eingegangen.

Toyota Prius Plug-in

With 600 vehicles worldwide, 200 of which are in Europe and around 20 of these are in Germany, Toyota is testing the usage patterns of drivers and fleet operators of the Prius Plug-in Hybrid before its planned series launch in 2012. The Japanese company is claiming an “electric range” of up to 20 km and fuel consumption of 2.6l/100 km in best case. This maximum figure is due above all to new developments in electric and thermal energy management.