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System Level Vehicle Model Development of Light Heavy Duty Battery Electric Vehicle in GT-Suite
Technical Paper
2019-01-2369
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Model based development is an approach pursued to obtain an advanced insight into powertrain & vehicle development. This approach enables the ability to trace errors early on, thereby reducing the dependence on field testing and saving cost & time involved in the development process. The purpose of this study is to develop a system level battery electric vehicle model using GT-Suite as modeling platform, for Light Heavy Duty applications. The outcome of this study is electric range estimation, performance analysis, component sizing and optimization for inverter-motor and battery components. The model has been developed using a map-based approach. Hence, the simulation time is faster than real-time, requires less input data and can be used for preliminary range & performance estimation. The model fidelity has been validated against performance, acceleration, deceleration and coast-down data obtained from field testing. Apart from field data, standard EPA drive cycles and customer usage based drive cycles have been used as target to the model. Energy efficiency (Wh/mile) and MPGe calculations have been performed as part of model validation. The applications of this study are to observe and study the effect of parameters such as motor power, air conditioning, battery capacity etc. on range and performance.
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Authors
- Santhosh Pasupathi - Isuzu Technical Center of America Inc. (Plymouth, Michigan,
- Aishwarya Shetty - Isuzu Technical Center of America Inc. (Plymouth, Michigan,
- Smruti Rathod - Isuzu Technical Center of America Inc. (Plymouth, Michigan,
- Gerald Bergsieker - Isuzu Technical Center of America Inc. (Plymouth, Michigan,
Citation
Pasupathi, S., Shetty, A., Rathod, S., and Bergsieker, G., "System Level Vehicle Model Development of Light Heavy Duty Battery Electric Vehicle in GT-Suite," SAE Technical Paper 2019-01-2369, 2019, https://doi.org/10.4271/2019-01-2369.Data Sets - Support Documents
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References
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