Regenerative Braking Control Strategy Based on AI Algorithm to Improve Driving Comfort of Autonomous Vehicles

Abstract

Recent studies on autonomous vehicles focus on improving driving efficiency and ignore driving comfort. Because acceleration and jerk affect driving comfort, we propose a comfort regenerative braking system (CRBS) that uses artificial neural networks as a vehicle-control strategy for braking conditions. An autonomous vehicle driving comfort is mainly determined by the control algorithm of the vehicle. If the passenger’s comfort is initially predicted based on acceleration and deceleration limits, the control strategy algorithm can be adjusted, which would be helpful to improve ride comfort in autonomous vehicles. We implement numerical analysis of the control strategy, ensuring reduced jerk conditions. In addition, backward propagation was applied to estimate the braking force limits of the regenerative braking systems more accurately. The developed algorithm was verified through the Car Sim and MATLAB/Simulink simulations by comparing them with the conventional braking system. The proposed CRBS offers effective regenerative braking within limits and ensures increased driving comfort to passengers.

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

“…M. H. Hwang et al [21] introduced a Comfort Regenerative Braking System (CRBS) utilizing neural networks to enhance driving comfort in autonomous vehicles. By predicting acceleration and deceleration limits based on passenger comfort criteria, the CRBS adjusts vehicle control strategies, reducing discomfort during braking.…”

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

“…The multi‐layer feedforward artificial neural network is a supervised learning technique that requires some training data. ANN is also used in [93] as a vehicle‐control strategy for braking conditions.…”

Efficiency Analysis of Hybrid Extreme Regenerative with Supercapacitor Battery and Harvesting Vibration Absorber System for Electric Vehicles Driven by Permanent Magnet Synchronous Motor 30 kW

“…Consequently, regenerative braking proves to be a highly effective technology for enhancing the overall efficiency of electric vehicles. Numerous efforts to meet the control performance requirements for regenerative braking have been documented in the literature, such as rule-based strategies [13], PID control strategies [11][12][13][14], and ANN approaches [15]. In situations where regenerative braking is active, the DC-link voltage experiences a rise, prompting the RBS program, with the assistance of the ANN controller, to redirect the energy from braking towards storage in the SC.…”

“…In situations where regenerative braking is active, the DC-link voltage experiences a rise, prompting the RBS program, with the assistance of the ANN controller, to redirect the energy from braking towards storage in the SC. The SC has SCB-HESP-powered scenario density and high battery energy density, but there are still limitations in using high-power electronic interfaces as a bidirectional dc/dc chopper is required to connect the battery to the SC, which leads to higher costs [9][10][11][12][13][14] and high-power electronic systems. The interface causes energy dissipation, and regenerative braking performance decreases later.…”