Optimal $\mu $ -Estimation-Based Regenerative Braking Strategy for an AWD HEV

Abstract

Braking force distribution (BFD) for electrified vehicles with the aim of maximizing energy regeneration has been a challenging research topic, due to the complex operating conditions and tradeoff among different vehicle performance measures. It is known that the level of tire-road friction has a significant impact on the braking force boundaries that define the locking conditions of front and rear wheels, and therefore, on the allowable set of BFD’s where the vehicle’s stability and controllability are maintained. However, previously developed BFD strategies for regenerative braking have not considered the changing boundaries of braking limits due to varying tire-road friction levels and introduce conservative BFD constraints to ensure stability and controllability. This paper proposes a BFD strategy for an all-wheel-drive electrified vehicle with a single electric motor, based on the estimation of the tire-road friction coefficient ( $\mu $ ) using a fuzzy logic estimation approach. The proposed strategy takes into consideration the motor efficiency and available speed reduction ratios in order to find the optimal BFD, which maximizes the regenerative power during braking, for a given vehicle speed and deceleration demand. Simulation analyses demonstrate the effectiveness of the proposed tire-road friction estimation-based BFD optimization strategy that significantly improves braking energy recovery. Preliminary test results with a prototype vehicle provide additional validation of the benefits of the proposed method.