Model based integrated control strategy for effective brake energy recovery to extend battery longevity in electric two wheelers

Abstract

This study aims to achieve optimal regenerative braking performance in the form of a reduced decline in battery SOC for a BLDC electric machine with peak torque of 10 Nm for use in electric two-wheelers. This is conducted via a comparison of control algorithms based on Direct Look-up table, Fuzzy Logic and their combination with PID control. The whole vehicle model and the energy recovery control strategies are designed using MATLAB Simulink by benchmarking the design with the parameters of the Ola Electric S1. A physical motor-dynamometer test bench is utilised to obtain a complete motor operating range to derive a realistic efficiency map that is used in the model. WLTP Class 2 and NYCC standard drive cycles are implemented for vehicle simulation. Two live-recorded driving patterns are also used to validate the model to analyse the adaptability of the control strategies. After obtaining the required motor speed, torque values and the range by matching the theoretical drive cycle profile, the control strategy is further optimised using the PID auto-tuning toolbox in Simulink. Using physical testbench data, the effect of various regenerative braking control strategies on overall vehicle performance is more accurately realised. The Fuzzy PID control strategy exhibits the most optimal gains in terms of energy recovery for electric two-wheelers, allowing for the highest battery SOC levels of 41.88% and average motor regenerative torque of 7.25 Nm for the standard drive cycles. An analogous trend is observed for the on-road driving pattern as Fuzzy PID provides highest battery SOC and average motor regenerative torque of 48.34% and 7.5 Nm respectively. For the driving scenarios aforementioned, this provides a 17% and 44% increase in SOC respectively when compared to a non-regenerative braking-based system.

Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy

“…Heydari et al[102], and Gang et al[105] was less than 15%. The energy re efficiency of Wu et al[92], Ashok et al[95], Liu et al[96], Sandrini et al[97], Chu[98], Ji et al[103], and He et al[104] exceeded 20%, with Liu et al[96] having an recovery efficiency of over 45%.…”

“…It can be seen that the energy recovery efficiency of He et al [10], Yang et al [11], Jiang et al [21], Geng et al [26], Ning et al [27], Zhao et al [28], Li et al [70], Yin et al [93], Chen et al [94], Shang et al [98], Chang et al [99], Chun et al [100], Liu et al [101], Heydari et al[102], and Gang et al[105] was less than 15%. The energy recovery efficiency of Wu et al[92], Ashok et al[95], Liu et al[96], Sandrini et al[97], Chun et al[98], Ji et al[103], and He et al[104] exceeded 20%, with Liu et al[96] having an energy recovery efficiency of over 45%.…”

A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles

“…The energy recovery of regenerative braking can be improved by up to 9.62%. Ashok et al [32] showed that the fuzzy PID control strategy performs well in the energy recovery of electric two-wheeled vehicles, with an energy recovery rate of 44%. Liu et al [33] adopted an adaptive braking force distribution control strategy.…”