Inverter Modeling and Regenerative Braking, Speed Optimization Using a Fuzzy Logic Controller for Autonomous Vehicles Battery Power Management

Engineering notes

Key topics: autonomous driving, autonomous vehicle, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

In order to improve battery power management and regenerative braking energy recovery in Connected Autonomous Vehicles (CAVs), this study proposes an intelligent control technique. During approach, cruising, and deceleration events, the suggested framework maximizes vehicle operating conditions. The system reduces needless braking, smoothness speed changes, and enhances energy flow into the regenerative braking system (RBS) by anticipating the driving situation.For co-simulation, a MATLAB/Simulink rule-based controller was created and connected with a vehicle simulation platform. Under ideal circumstances, the CAV decelerated from 15 m/s to 0 m/s after traveling around 100 meters in the evaluation scenario. The suggested control approach achieved a braking energy recovery of over 100 kJ by increasing effective RBS usage. Additionally, by lowering peak power demands, guaranteeing smoother torque distribution, and improving overall energy utilization, the technology enhanced battery power management.The controller outperformed the other solutions evaluated in terms of vehicle efficiency, battery energy optimization, regenerative braking efficacy, and safe operating behavior. The substantial potential of intelligent transportation technologies to improve the energy efficiency and operational sustainability of next-generation electric and driverless cars is highlighted by this work

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