Thermal Model Developments for Electrified Vehicles

Abstract

Argonne National Laboratory has analyzed the control behavior of advanced vehicles, such as hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs), to develop simulation models and to reproduce the performance of vehicles with simulation techniques. Since many of the novel and advanced studies about transportation technologies done at Argonne use these simulation techniques, they must be well-validated to conduct and support these studies. To improve its research ability, Argonne built a new testing facility that can test vehicles under different thermal conditions (e.g., -7°C or 35°C), and it has analyzed the controls and performance of several advanced vehicles under these conditions.Further, Argonne has used the analyzed results to develop thermal component models that reproduce the thermal behavior of the vehicles. A main reason to develop thermal models is that the thermal conditions have such a significantly large impact on vehicle performance, especially with regard to advanced vehicles like HEVs or PHEVs. For instance, engine and battery efficiencies must decrease at low temperatures since the battery might not be able to provide enough power if it is very cold. Moreover, the climate control system still has a great demand for additional energy under very cold weather conditions even if the engine is not operating at all. The test data obtained from Argonne's Advanced Powertrain Research Facility (APRF) are analyzed in order to understand the thermal impacts on controls and performance, and the thermal models are developed based on the analyzed results and validated with the test data. In comparative studies, the simulation models have been found to reproduce fuel consumption that is very close to the fuel consumption obtained from the tests.

A Novel Architecture of Multimode Hybrid Powertrains for Fuel Efficiency and Sizing Optimization

“…𝑡 𝑒𝑛𝑑 𝑡 0 (10) where 𝐽 is the objective function, and 𝐿 is the instantaneous cost function. The instantaneous cost function includes the instantaneous fuel consumption rate and penalty function which is given by, 𝐿 = 𝑚̇𝑓 𝑢𝑒𝑙 + 𝑓 𝑝 (11) where 𝑚̇𝑓 𝑢𝑒𝑙 is the instantaneous fuel consumption rate and 𝑓 𝑝 is the instantaneous penalty function.…”

“…There have been various research topics related to hybrid vehicle systems. The research topics of the recent studies include diverse research areas such as driving and regenerative control strategy of HEVs [1]- [3], optimal design of component sizing and electrical energy storage [4], [5], driving energy management strategy for HEV [6], a design methodology for compound-split hybrid electric vehicles with the compound lever diagram [7], comparative analysis of classical energy management optimization with reinforcement learning [8], a parametric model for power split hybrid transmissions [9], and thermal management and analysis of electrified drive systems and hydraulic hybrids [10]- [13]. For the research on architectures of hybrid systems, there have existed a number of studies for past decades since the 1970s and diverse recent studies like a multimode power split hybrid transmission [14], hybrid powertrain architectures for a fourwheel drive [15], series-parallel and power split architecture based dedicated hybrid transmissions (DHTs) [16], powertrain configuration of single motor hybrid systems [17], and novel powertrain architectures with Ravigneaux and planetary gear trains [18].…”

Control Map Generation Strategy for Hybrid Electric Vehicles Based on Machine Learning With Energy Optimization

“…For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ been researched in various areas. These include hybrid system architecture developments aimed at improving fuel efficiency and performance for a single motor system [1], systems based on power split architectures [2]- [4], a system based on series-parallel architectures [5], and four-wheel drive architectures [6]; the design of components for hybrid vehicles [7], [8]; thermal management of components and hybrid systems with energy analysis [9]- [12]; optimization methods for system performance predictions [13]- [15]; and control strategies for driving energy management [16]- [19].…”