Impact of Thermal Architecture on Electric Vehicle Energy Consumption/Range: A Study with Full Vehicle Simulation GAC Motor Co, Limited

Format:

Book

Conference/Event

Author/Creator:

Yang, Bozhi, author.

Contributor:

Li, Gang

Li, Xiaohui

Wang, Meng

Wei, Dan

Yao, Meng

Conference Name:

SAE WCX Digital Summit (2021-04-13 : Live Online, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

Electric vehicles suffer from dramatic energy loss in cold and hot climate, resulting in range reduction up to 50% at -20 °C ambient and 30% at 40 °C ambient. Energy consumption by thermal management systems is responsible for most of the range loss. To study the impact of thermal architectural choices on energy consumption at vehicle level, full vehicle level simulations were carried out with an in-house simulation platform, which was built as a system-engineering tool to study the interaction among hierarchies of vehicle systems, subsystems and components. The top-level simulation system consists of a driver model, an environment model, a control system model and a vehicle plant model. The plant model consists of models for a complete thermal management system (e.g., coolant, refrigerant, cabin HVAC and underhood air systems), an integrated drive unit (e.g., motor, inverter and gearbox), a battery pack, chassis, aerodynamics, et cetera Two thermal architectures were studied: (1) resistive heating is adopted for both battery and cabin heating and (2) heat pump is used for cabin heating while resistive heating remained to heat up batteries. The cooling/heating performance and energy consumption of both thermal architectures were modeled from -20 °C to 40 °C ambient temperatures under NEDC and WLTP drive cycles. It shows EV with heat pump configuration has ~20-40% higher energy efficiency than the cabin heating using pure electric resistive heating at cold weather conditions. Simulation results agree with available test data, with error <10-15% for most cases. It shows the vehicle modeling platform is effective to predict the weather-dependent energy efficiency and range of electric vehicles

Notes:

Vendor supplied data

Publisher Number:

2021-01-0207

Access Restriction:

Restricted for use by site license