Author(s)

Hao Zhu ¹, Xin Gu ^2,3, Su Zhou ²

Affiliation(s)

¹ Shanghai Motor Vehicle Inspection Certification & Tech Innovation Center Co., Ltd, Shanghai, 201805, China
² Tongji University, Shanghai, 201804, China
³ Shanghai New Power Automotive Technology Co., Ltd, Shanghai, 200438, China

Corresponding Author

Su Zhou

ABSTRACT

Electrification of heavy-duty commercial vehicles represents a pivotal approach to addressing energy consumption and emissions. This study focuses on 49-ton heavy-duty commercial vehicles, employing electric drive axles to replace traditional engine propulsion systems. Research into the power performance and energy consumption of this system is conducted on this basis. As a fundamental method for analyzing these performance aspects, the establishment of models and performance simulations are indispensable steps in the development of electric systems. Effective modeling and simulation techniques can expedite the automotive development process and minimize costs. In this paper, a comprehensive plant model for E-Axle Electric Vehicle Systems is established, integrating various dynamics models. Utilizing the parameters of a 49-ton heavy-duty tractor, the economically optimal configuration for the E-axle is determined. Simulations based on the CHTC-TT cycle provide dynamic and economic indicators, facilitating comprehensive vehicle performance evaluations. Ultimately, the simulation results demonstrate that the E-axle system can meet the power performance requirements of the CHTC-TT cycle, achieving a maximum vehicle speed of 88 km/h over a 23 km journey while consuming 59 kWh of power. These findings indicate that the proposed configuration and matched parameters satisfy vehicle performance specifications, providing theoretical support for the electrification of heavy-duty commercial vehicles.

KEYWORDS

Electric Vehicle; Electric Drive Axles; Analysis of Simulation; CHTC-TT

CITE THIS PAPER

Hao Zhu, Xin Gu, Su Zhou, Modeling and Simulation of E-Axles Electric Vehicle Based on Simulink. Advances in Computer, Signals and Systems (2025) Vol. 9: 137-149. DOI: http://dx.doi.org/10.23977/acss.2025.090119.

REFERENCES

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