Smart Mobility Boosting Using High-Fidelity Magnetorheological Fluid Modeling for Adaptive Damper Control Development

With the rise of high-performance electric vehicles (EVs) which require advanced suspension systems capable of delivering precision, high force output, and rapid adaptability, magnetorheological (MR) dampers are critical for achieving rapid-response suspension control. However, their temperature sensitivity limits reliability under extreme conditions. This protocol presents a systematic approach to address this challenge. A high-performance MR fluid is synthesized using carbonyl iron particles dispersed in a thermally stable carrier fluid with anti-wear agents and antioxidants. We propose an Exponential Linear Mixed Analysis (ELMA) model and its parameter identification method, which can be considered as a superior alternative to the bi-plastic Bingham model. The ELMA framework is extended to MR dampers, with temperature compensation algorithms improving current tracking accuracy by 3.98% and force tracking accuracy by 7.75% (peak: 19.92%). Joint CarSim/Simulink simulations demonstrate that temperature-compensated Sky-hook and Mixed SH-ADD algorithms reduce vertical acceleration variance by 11.97% and peak pitch rate by 41.78% on Class D roads. This protocol bridges MR fluid physics to adaptive damper control, offering a replicable workflow for enhancing EV suspension systems in extreme thermal environments.