In the rapidly evolving landscape of modern vehicles, active systems emerged as a key driving force behind advancements in automotive technology. These sophisticated systems play a pivotal role in enhancing vehicle performance, safety, and overall driving experience. However, they increase substantially the complexity of the chassis, because of the multiple interactions (and in some cases actuation redundancy) among them and the mechanical hardware systems. In order to manage such complexity and keeping feasible development times, it is necessary to adopt innovative development methodologies, mostly based on simulation (off-line and on-line), especially in the early stages of the development, where key decisions need to be taken. The aim of this paper is to summarize the different Integrated Chassis Control (ICC) strategies and present a model approach based on Centralized architecture for a four-wheel-motor electric vehicle in in order provide a high level of stability, handling, and maneuverability. The system is based on a non-linear Model Predictive Control (NLMPC) controller that distributes requested driver torque into the 4 wheels. NLMPC model uses a four-wheel vehicle model with simplified Magic-Formula (MF) tire formulation for lateral dynamics. Longitudinal dynamics modelling is simplified inside the NLMPC formulation and considered with a longitudinal Slip Controller that modifies the NLMPC boundaries. NLMPC cost function focus on a yaw rate and sideslip reference signal and a stable region for the sideslip angle and rate to keep the vehicle in a safe and desired path. Unlike traditional architectures which consider independent controllers for each task in a peaceful co-existence, centralized architectures group different tasks into a single system. On one side, centralized architecture has less modular capacity, requires more implementation efforts and computational load. On the other side, it provides more adaptability to different scenarios, allows multi-objective coordination, and has a better global stabilityoriented design. Specifically, current ICC controller groups tasks done by the following traditional systems: TVS, RWS, ESC, ABS and TCS. After the MPC formulation, the overall vehicle + controller model performance has been firstly optimized with off-line simulations in several open loop scenarios typically used in vehicle dynamics evaluations. Finally, the controller parameters have been subjectively tuned by professional drivers on the IDIADA Driving Simulator DiM250, both for individual scenarios as well as for racetrack driving conditions. In summary, the main benefit the ICC provides comparing to a traditional non centralized implementation is a significantly more optimal usage of actuators (from a vehicle dynamics perspective only) which are dealing with conflicting cost function targets simultaneously.

Development of Integrated Chassis Control Systems in DiL environment

会议论文
发表日期：

2025年

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Jaume Cartró,
Javier Gutiérrez

摘要

In the rapidly evolving landscape of modern vehicles, active systems emerged as a key driving force behind advancements in automotive technology. These sophisticated systems play a pivotal role in enhancing vehicle performance, safety, and overall driving experience. However, they increase substantially the complexity of the chassis, because of the multiple interactions (and in some cases actuation redundancy) among them and the mechanical hardware systems. In order to manage such complexity and keeping feasible development times, it is necessary to adopt innovative development methodologies, mostly based on simulation (off-line and on-line), especially in the early stages of the development, where key decisions need to be taken. The aim of this paper is to summarize the different Integrated Chassis Control (ICC) strategies and present a model approach based on Centralized architecture for a four-wheel-motor electric vehicle in in order provide a high level of stability, handling, and maneuverability. The system is based on a non-linear Model Predictive Control (NLMPC) controller that distributes requested driver torque into the 4 wheels. NLMPC model uses a four-wheel vehicle model with simplified Magic-Formula (MF) tire formulation for lateral dynamics. Longitudinal dynamics modelling is simplified inside the NLMPC formulation and considered with a longitudinal Slip Controller that modifies the NLMPC boundaries. NLMPC cost function focus on a yaw rate and sideslip reference signal and a stable region for the sideslip angle and rate to keep the vehicle in a safe and desired path. Unlike traditional architectures which consider independent controllers for each task in a peaceful co-existence, centralized architectures group different tasks into a single system. On one side, centralized architecture has less modular capacity, requires more implementation efforts and computational load. On the other side, it provides more adaptability to different scenarios, allows multi-objective coordination, and has a better global stabilityoriented design. Specifically, current ICC controller groups tasks done by the following traditional systems: TVS, RWS, ESC, ABS and TCS. After the MPC formulation, the overall vehicle + controller model performance has been firstly optimized with off-line simulations in several open loop scenarios typically used in vehicle dynamics evaluations. Finally, the controller parameters have been subjectively tuned by professional drivers on the IDIADA Driving Simulator DiM250, both for individual scenarios as well as for racetrack driving conditions. In summary, the main benefit the ICC provides comparing to a traditional non centralized implementation is a significantly more optimal usage of actuators (from a vehicle dynamics perspective only) which are dealing with conflicting cost function targets simultaneously.