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

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.

Introduction

The study focuses on improving the efficiency, safety, and sustainability of autonomous and connected electric vehicles through advanced regenerative braking, vehicle communication, perception systems, and intelligent energy management. Self-driving and connected vehicle technologies utilize vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, advanced sensors, and artificial intelligence to enhance traffic flow, reduce accidents, and improve energy efficiency. However, challenges such as reliability, safety concerns, high development costs, and incomplete regulatory frameworks continue to limit widespread adoption.

A significant research area is regenerative braking, which recovers kinetic energy during deceleration and stores it in the vehicle battery. Conventional systems often depend on driver behavior and cannot anticipate traffic conditions, reducing energy recovery efficiency. Recent studies have addressed these limitations using V2I communication, machine learning, optimization algorithms, and predictive control techniques to improve braking performance, energy recovery, and passenger comfort. Sensor fusion technologies combining cameras, radar, and other sensing devices have also been developed to improve object detection and autonomous navigation while reducing dependence on expensive sensors such as LiDAR.

The literature review highlights advancements in autonomous driving, cooperative eco-driving, intelligent energy management, regenerative braking optimization, and perception systems. Researchers have demonstrated that AI-based decision-making, adaptive cruise control, and connected vehicle technologies can significantly reduce energy consumption, emissions, and traffic congestion while enhancing safety and ride comfort. Studies also show that machine learning techniques can effectively model regenerative braking behavior across different electric vehicle platforms.

The identified problem is that existing reinforcement learning-based regenerative braking systems are primarily designed for electric vehicles and may not be directly applicable to hybrid electric vehicles. Additionally, object detection systems still face challenges related to accuracy, computational efficiency, and reliable decision-making in real-world driving environments.

To address these issues, the proposed methodology develops an intelligent Regenerative Braking System (RBS) for autonomous vehicles. The system predicts braking requirements, optimizes braking force distribution between electric and mechanical brakes, maximizes energy recovery, and maintains smooth and comfortable vehicle deceleration. The model uses vehicle speed and deceleration as inputs and monitors battery parameters such as voltage, current, and State of Charge (SOC).

Simulation results show that a vehicle traveling at 20 m/s with a regenerative braking torque of 100 Nm can recover approximately 37–40 kJ of energy during a 10-second braking period, achieving around 4 kW of recovered electrical power. To estimate battery SOC accurately, a Mamdani Fuzzy Logic Controller (FLC) is developed using speed and deceleration inputs. The fuzzy logic system provides smooth and reliable SOC estimation, achieving approximately 50% SOC with about 100 kJ of energy recovery and 37–38 kJ of stored battery energy. Overall, the proposed regenerative braking and fuzzy logic-based control strategy improves energy efficiency, battery management, driving comfort, and sustainability in autonomous electric vehicle applications.

Conclusion

In this paper, using a fuzzy logic controller to maximize regenerative braking and battery power management in autonomous cars. 1) The findings show that under various driving circumstances, the suggested control method successfully recovers braking energy and preserves an enhanced battery state of charge. The system accomplishes an energy recovery of around 100 kJ at a vehicle speed of 15 km/h, resulting in a battery state of charge of roughly 50% with approximately 37–38 kJ of energy effectively stored in the battery. The controller minimizes needless energy losses and guarantees smooth energy flow by cleverly controlling speed and deceleration inputs. 2) These advancements support dependable and stable battery management in autonomous vehicle systems and contribute to increased energy efficiency. Additionally, the close agreement between the fuzzy logic controller outputs and the MATLAB/Simulink results validates the accuracy and robustness of the suggested method, confirming its suitability for real-world vehicle energy management applications.

References

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Copyright

Copyright © 2026 P. Gowsalya, J. Shanmugasudharam . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.