Robust synergetic control of electric vehicle equipped with an improved load torque observer

Abstract

For environmental reasons, a great effort was focused on the improvement of the steady-state performance and dynamic performance of electric vehicles (EV). The EV is one of the cleanest and most ecological transport solutions. Accurate knowledge of EV load torque signals is important information for controlling and improving EV active safety. The estimation of load torque in EV drive contributes to cost reduction and solves many of the implementation problems encountered: lack of space, and severe environment. On this basis, in this research, our contribution contains two parts. The first part is the conception of a new scheme of control based on synergetic control (SC) for the speed of an EV propelled by a Six-Phase Permanent Magnet Synchronous Motor (PMSMs) is developed. This control technique offers a fast response, decreases the size of modeled system, and asymptotic stability of the closed-loop system in a vast domain. The second part deals with the synthesis of the sliding mode observer (SMO) to estimate load torque, compensate for strong disturbances and achieve high servo precisions. Moreover, reducing the chattering is inherent in the conventional SMO, we have proposed to replace the switching observer term Ksign with a smooth function and super twisting algorithm. The obtained simulation results proved the high performance assured by the synergetic control such as robustness and dynamic performance, and good estimation accuracy with free chattering even in strong noises.

Machine Learning-Based Regression Models for State of Charge Estimation in Hybrid Electric Vehicles: A Review

“…(4) Dynamic Operating Conditions: HEVs frequently operate in diverse and dynamic conditions, including regenerative braking, fast acceleration, and variations in load. Traditional methods may not accurately capture these dynamic changes in SOC, potentially leading to inaccurate estimations [19].…”