Intelligent Regenerative Braking Control With Novel Friction Coefficient Estimation Strategy for Improving the Performance Characteristics of Hybrid Electric Vehicle

Abstract

Regenerative braking is of paramount importance both in terms of energy recovery and from the perspective of hybrid electric vehicles (HEVs). It allows HEVs to recover energy during braking which in turn enhances the range, efficiency and sustainability by reducing electricity demand and environmental impacts. Upon considering the demand for high-performance regenerative braking in HEVs, the development and optimization of advanced control strategies become mandatory. The study presents a model-based regenerative braking system that integrates tire-road coefficients with an intelligent control algorithm to enhance the efficiency and sustainability of HEVs. The literature review provides insights into existing hybrid power systems, tire-road friction estimation, braking force distribution, and regenerative braking optimization strategies. Different control strategies including lookup table, fuzzy logic control (FLC) and adaptive neuro-fuzzy inference system (ANFIS) are analyzed for their impact on the efficiency of regenerative braking systems under various driving conditions and their results have been presented. The study reveals the supremacy of the ANFIS control algorithm across multiple driving cycles, demonstrating its potential to enhance fuel efficiency and state of charge (SOC). The study involves several input and output parameters of a vehicle to arrive at the desired outcome, and this also showcases ANFIS’s capabilities in providing a better and smoother driving behaviour for the driver, indicating its adaptability and proficiency in energy recovery. FLC strategy excelled in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> - estimation with lower estimation errors of about 0.15%, 0.16% and 0.13% across various driving cycles when compared to the look-up table approach. ANFIS exhibited fuel economy, improvements of about 0.282%, 0.437%, and 0.345% over the look-up table approach in the same cycles. In terms of SOC optimization, ANFIS achieved the highest final SOC values, surpassing the look-up table strategy by 2.082%, 6.799%, and 0.702% in the same cycles.