Evaluation of 2004 Toyota Prius Hybrid Electric Drive System

Staunton, R.H.; Ayers, C.W.; Marlino, Laura D.; Chiasson, John; Burress, B. A.

doi:10.2172/890029

Cited by 66 publications

(70 citation statements)

References 4 publications

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“…Coordination of PEVs in smart grid requires accurate modeling of its battery profile and charging characteristics. Many modern battery chargers are capable of achieving high efficiency values; however, their charging efficiencies indicate significant dependency on the charging rate due to the internal battery resistance [19][20][21]. This is particularly important in calculating the actual stored energies and SOCs at different times during the charging period.…”

Section: Modeling Of Battery and Charger For Pevsmentioning

confidence: 99%

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Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance

Hajforoosh

Masoum

Islam

2016

Electric Power Systems Research

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Participation of plug-in electric vehicles (PEVs) is expected to grow in emerging smart grids. A strategy to overcome potential grid overloading caused by large penetrations of PEVs is to optimize their battery charge-rates to fully explore grid capacity and maximize the customer satisfaction for all PEV owners. This paper proposes an online dynamically optimized algorithm for optimal variable charge-rate scheduling of PEVs based on coordinated aggregated particle swarm optimization (CAPSO). The online algorithm is updated at regular intervals of Δt=5min to maximize the customers' satisfactions for all PEV owners based on their requested plug-out times, requested battery state of charges (SOC Req ) and willingness to pay the higher charging energy prices. The algorithm also ensures that the distribution transformer is not overloaded while grid losses and node voltage deviations are minimized. Simulation results for uncoordinated PEV charging as well as CAPSO with fixed charge-rate coordination (FCC) and variable charge-rate coordination (VCC) strategies are compared for a 449-node network with different levels of PEV penetrations. The key contributions are optimal VCC of PEVs considering battery modeling, chargers' efficiencies and customer satisfaction based on requested plug-out times, driving pattern, desired final SOCs and their interest to pay for energy at a higher rate.

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Section: Modeling Of Battery and Charger For Pevsmentioning

confidence: 99%

“…The inverse algebraic products (Eq.18) of the proposed penalty functions for voltage (Eqs. [19][20] and demand (Eq.21) are used as the fitness function to combine the PEV coordination objective function (Eq.1) and constraints (Eqs.11-15):…”

Section: Capso Fitness Functionmentioning

confidence: 99%

Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance

Hajforoosh

Masoum

Islam

2016

Electric Power Systems Research

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“…Inspecting the figure, one can observe that the middle region of the pack has the highest temperature; however, the maximum temperature difference recorded is about Figure 20. Three-dimensional FE model for the radiator for the Prius III showing the two portions for low-temperature and hightemperature coolants' circuits [18]. Power-split hybrid configuration thermal model A. Mayyas et al…”

Section: Resultsmentioning

confidence: 99%

“…Prius high-temperature and low-temperature coolant loops architecture[18].Power-split hybrid configuration thermal modelA. Mayyas et al…”

mentioning

confidence: 99%

Thermal modeling of an on-board nickel-metal hydride pack in a power-split hybrid configuration using a cell-based resistance-capacitance, electro-thermal model

Omar

Pisu

Mayyas

et al. 2011

Int. J. Energy Res.

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SUMMARY Presented study discusses the development of a finite differencing (FD) thermal model for a power‐split hybrid configuration employing a nickel‐metal hydride battery pack. A resistance–capacitance electro‐thermal model is used to couple the experimental boundary conditions (current, voltage, state of charge, and temperature) with the modeled battery resistance to capture its electro‐chemical behavior and the cell exothermic reactions. Battery current, voltage, and temperature (discrete and full field) for a vehicle with a power‐split hybrid configuration were collected under different standard (Federal Highway Driving Schedule and Federal Urban Dynamometer Driving Schedule (FUDS)) and artificially generated driving cycles. This manuscript analyzes the battery current and voltage in relation to vehicle speed and shows how the proposed FD model predicts the spatial and temporal temperature profiles of the power train in good agreement with the vehicle data as reported by the on‐board diagnostics module. Copyright © 2011 John Wiley & Sons, Ltd.

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“…where ( ), detailed below in (2)-(4), is the power required to follow the driving cycle, ( ) is the power output of the motor, ( ), illustrated in Fig.2 [34], is the efficiency of the combination of motor and inverter, which is a function of motor speed and torque, ( ) is the output power of the generator, and ( ) is the battery power.…”

Section: Modeling Of Series Hybrid Electric Vehiclementioning

confidence: 99%

Simultaneous Identification and Control Using Active Signal Injection for Series Hybrid Electric Vehicles Based on Dynamic Programming

Zhu

Song

Hou

et al. 2020

IEEE Trans. Transp. Electrific.

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Hybrid electric vehicles (HEVs) have an over-actuated system by including two power sources, a battery pack and an internal combustion engine. This feature of HEV is exploited in this paper to simultaneously achieve accurate identification of battery parameters/states. By actively injecting current signals, state of charge, state of health, and other battery parameters can be estimated in a specific sequence to improve the identification performance when compared to the case where all parameters and states are estimated concurrently using the baseline current signals. A dynamic programming strategy is developed to provide the benchmark results about how to balance the conflicting objectives corresponding to identification and system efficiency. The tradeoff between different objectives is presented to optimize the current profile so that the richness of signal can be ensured and the fuel economy can be optimized. In addition, simulation results show that the Root-Mean-Square error of the estimation can be decreased by up to 100% at a cost of less than 2% increase in fuel consumption.With the proposed simultaneous identification and control algorithm, the parameters/states of the battery can be monitored to ensure safe and efficient application of the battery for HEVs. NomenclatureLinearized OCV-SOC slope (V) Frontal area of the HEV (m 2 ) Constants of linearized OCV-SOC curve (V)

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Evaluation of 2004 Toyota Prius Hybrid Electric Drive System

Cited by 66 publications

References 4 publications

Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance

Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance

Thermal modeling of an on-board nickel-metal hydride pack in a power-split hybrid configuration using a cell-based resistance-capacitance, electro-thermal model

Simultaneous Identification and Control Using Active Signal Injection for Series Hybrid Electric Vehicles Based on Dynamic Programming

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