A Dynamic Rollover Prediction Index of Heavy-Duty Vehicles With a Real-Time Parameter Estimation Algorithm Using NLMS Method

Rollover accidents are more likely to occur with Heavy-Duty Vehicles (HDV) due to the high center of gravity and large size. A dynamic rollover index (RI) can describe the transient nature and responses of vehicle states. This study proposes an improved RI to detect real-time rollover events in tripped and untripped conditions. The RI combines the roll-plane dynamics of the sprung and unsprung mass and even fully considers vehicle suspension dynamics under uncertain external road inputs. Moreover, a low-cost unknown input observer (UIO) is designed to estimate vehicle roll angle under the uncertain disturbance of road bank. The updating improved RI is in terms of quasi-constant parameters, estimated states and external perturbation. A parameter estimation algorithm based on the Normalized Least Mean Square (NLMS) is developed to observe unmeasurable and time-varying parameters. Furthermore, by investigating the thresholds of roll angle and lateral acceleration, a dynamic predictive rollover threshold is determined with state constraints. The results demonstrate that the proposed parameters estimator is capable of estimating the c. g. height, roll moment of inertial, suspension roll stiffness, and suspension roll damping within acceptable errors in different driving scenarios. Moreover, the improved RI can strictly detect rollover in a complex driving scenario. Finally, the proposed dynamic RI threshold reduces invalid rollover warnings, predicates the rollover risk and guarantees sufficient response time for controller delay.