# A Review of Coordinated Control Technology for Chassis of Distributed Drive Electric Vehicles
## 5 Discussion  
The core of research on coordinated chassis control for DDEVs is to maximize the potential of the independent drive/braking of each wheel to enhance the overall vehicle performance, and the key to achieving this goal lies in effective multi-subsystem coordination. In the choice of control architecture, hierarchical architecture, due to its comprehensive advantages in task decoupling, balancing global and local control, and system flexibility, has become an industry consensus and the mainstream direction for future development. On this basis, research on coordinated control strategies has gradually deepened, evolving from two-dimensional coupling to three-dimensional integration. At the level of two-dimensional coordinated control, significant progress has been made. Researchers have successfully enhanced handling stability and path tracking accuracy, balanced ride comfort, and optimized energy efficiency for specific objectives. These advancements were achieved through two primary means. The first was the precise coordination of key subsystems, such as Active Steering, Direct Yaw moment Control, Active/Semi-Active Suspension, and differential drive/braking. The second involved leveraging advanced algorithms like Model Predictive Control (MPC), Sliding Mode Control (SMC), Fuzzy Logic Control (FLC), and Multi-Agent Systems (MASs). However, this progress has also revealed ongoing challenges, particularly in the areas of accurate modeling, online parameter identification, and multi-objective conflict resolution. Furthermore, to achieve a more comprehensive optimization of overall vehicle dynamic performance, research has advanced towards integrated three-dimensional (lateral-longitudinal-vertical) coordinated control. This advanced control is typically implemented through two main approaches: methods based on weight allocation (including mode switching) and those based on global optimization. Both approaches conduct a higher-level regulation and performance optimization of the vehicle's multi-dimensional dynamics. This evolution signifies a clear trend in chassis control towards higher levels of integration and intelligence.  
Looking to the future, coordinated chassis control technology for DDEVs will evolve towards deeper levels of intelligence and refinement. Its core lies in first breaking through the bottlenecks in the high-precision modeling and real-time perception of complex vehicle dynamic behavior, especially online tire-road interactions and the precise transmission of vertical loads, to provide a solid physical basis and accurate state inputs for subsequent advanced control strategies. On this foundation, future research will primarily focus on overcoming the limitations of existing control theories, particularly in computational efficiency and nonlinear processing capabilities. A promising path forward is the deep integration of artificial intelligence algorithms, such as reinforcement learning and neural networks. By doing this, coordinated control systems can be endowed with capabilities for autonomous learning, online optimization, and personalized adaptation. This will ultimately drive a paradigm shift from traditional model-based control to an intelligent control framework that combines both data-driven and model-based approaches. Ultimately, these technological advancements will deeply serve high-level autonomous driving. Through standardized integration architectures and comprehensive testing and validation systems, an efficient linkage between chassis coordinated control and autonomous driving decision-making and planning layers will be promoted, achieving an optimization of vehicle performance and driving intelligence in all scenarios.  
This work has been supported by the National Natural Science Foundation of China (52225212, 52272418, U22A20100) and the National Key Research and Development Program of China (2022 YFB2503302).  
Not applicable.  
We would like to extend our gratitude to all the researchers and professionals.  
The authors declare no conflicts of interest.