Energy-Optimized Steering and Braking Coordination in Software-Defined Electric Vehicles
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I1P137Keywords:
Software-Defined Vehicles, Electric Vehicles, Steer-By-Wire, Brake-By-Wire, Energy Optimization, Model Predictive Control, Vehicle Dynamics, Motion Coordination, Regenerative BrakingAbstract
This article is a proposal of an energy-efficient steering and braking coordination scheme of the next generation Software-Defined Electric Vehicles (SD-EVs). The contemporary EVs are being turned to centralized, software defined architectures that enable dynamism in controlling vehicle subsystems using software abstraction layers. Nonetheless, the rise in the complexity of computational and control would demand efficient approaches of energy reduction without compromising safety and comfort of the ride. This paper suggests an integrated control architecture that can bring steer-by-wire (SbW) and brake-by-wire (BbW) together in a software-defined control stack with the ability to implement energy-efficient strategies in real time. The abstract addresses the reasons why it is better to use SDV architectures, restrictions of conventional hardware-based control systems, and the benefits of centralized electronic control units (ECUs). The solutions suggested are based on the model predictive control (MPC) and optimal energy allocation principles, and theory of motion coordination principles. We present a dynamic car model in which steering dynamic and braking dynamic are included in a combined manner and thus, allow coordination of tire forces and yaw forces. Our architecture will also enhance energy efficiency by dynamically reallocating braking effort between regenerative braking and friction braking paths, as well as steering actuator torques to reduce power consumption. Expert verification in a high-fidelity vehicle simulator and Hardware-in-the-Loop (HIL) test bench are done to test the performance in the different driving conditions, including slalom maneuvers, emergency braking, and urban stop-go cycles. Findings show that there was great saving of energy as opposed to traditional independent SbW and BbW systems. The offered strategy also leads to the improvement of the dynamic situation by minimizing the unwanted yaw moments and optimizing the use of tires. Additional information dwells on safety assurance, the modularity of software-defined control stacks, cyber-physical convergence and fault-tolerant behavior. This study demonstrates the opportunities available in software-defined vehicle systems in facilitating comprehensive energy optimization policies that would otherwise be challenging to implement in the older systems.
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