## 1 Introduction
[15] introduced a DT of a smart city paradigm that improves insight into the technical interactions of urban human-machine infrastructures. However, most of these mentioned simulation approaches suffer from poor flexibility and complex configurations. In fact, the DT often contains multiple subsystems, and it is difficult to build accurate mechanistic models for complex physical systems. In contrast, a data-driven approach uses historical and real-time operational data of the system to update, revise, connect, and supplement the numerical model [16], fusing system mechanics and operational data to better evaluate the system dynamically in real time [17; 18; 19]. In the field of automotive manufacturing, research targeting DT is also evolving [20; 21]. [PERSON] et al. [22; 23] proposed a health metric estimation method based on the DT for condition monitoring of power electronic converters, demonstrating the application of Buck DC-DC converters. A digital copy of the experimental prototype was built, which included the power stage, sampling circuit and closed-loop controller. [PERSON] et al. [24] proposed a cloud BMS to build a DT of the battery system that could ensure continuous and accurate monitoring of the battery state, but it did not give a specific DT model. [PERSON] et al. [25], for a similar purpose, used equivalent battery circuit modeling and real-time RLS updates to construct a DT that provides accurate insights into battery charge estimation and conditions for the BMS. [PERSON] et al. [26] developed health monitoring and prognostics for PMSM by creating an intelligent digital twin (i-DT) in MATLAB / Simulink. Next, a remote health monitoring and diagnostic center (RHMPC) was developed to remotely monitor motor performance through cloud communication from EV service providers. [PERSON] et al. [27] presented how DT can be integrated with integrated vehicle health management (IVHM) for condition-based maintenance (CBM) of vehicles to provide targeted components and systems of vehicles with customized maintenance plans. [PERSON] et al. [28] created a simulation-based DT model combined with an integrated model of the braking system to support thermal monitoring and predictive maintenance of the vehicle braking system. [PERSON] et al. [29] performed health inspection of vehicle tire subsystems based on DT. [PERSON] et al. [30] created a DT of a complete photovoltaic energy conversion unit (PVECU),including PV energy, source-level power converters, and controllers, which is capable of effectively troubleshooting complex systems without the need for physical analysis. To a large extent, these studies have contributed to the penetration of DT in the field of smart vehicles, but they have mostly focused on the component level, illustrating the characteristics of a particular subsystem of the vehicle, and research on DT for the whole vehicle is very limited. However, the study of the whole vehicle is much more meaningful because it fully considers the interactions between the subsystems, which is more comprehensive and realistic. Therefore, the knowledge gap of such research exists in the following aspects: (1) simulation modeling methods are mostly inflexible and error-prone with complex configurations; (2) DT model lacks data-driven real-time updates and parallel operation with entities; (3) there is a lack of research on the characteristics of the whole vehicle. In summary, DT breaks through the limitations of previous simulation techniques in the design phase and local applications [31] to achieve multi-physical field coupling and data linkage between virtual and physical entities to improve simulation accuracy. In the face of complex systems and multivariable environments, DT can reflect the entity system information more comprehensively, accurately and in real time [32; 33]. The purpose and novelties of this paper are as follows:  
1.