Combined Estimation of Vehicle Dynamic State and Inertial Parameter for Electric Vehicles Based on Dual Central Difference Kalman Filter Method

Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size,and inertial parameter has seldom been tackled and systematically estimated.This paper presents a dual central difference Kalman filter(DCDKF)where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters,such as vehicle sideslip angle,vehicle mass,vehicle yaw moment of inertia,the distance from the front axle to centre of gravity.The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs.The four-wheel nonlinear vehicle dynamics estimation model considering payload variations,Pacejka tire model,wheel and motor dynamics model is developed,the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory.To address system nonlinearities in vehicle dynamics estimation,the DCDKF and dual extended Kalman filter(DEKF)are also investigated and compared.Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-CarsimR.The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions.This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.

Logic-Gated Plasmonic Nanodevices Based on DNA-Templated Assembly

Life has evolved numerous elegant molecular machines that recognize biological signals and affect mechanical changes precisely to achieve specific and versatile biofunctions.Inspired by nature,synthetic molecular machines could be designed rationally to realize nanomechanical operations and autonomous computing.We constructed logic-gated plasmonic nanodevices through coassembly of two gold nanorods(AuNRs)and computing elements on a tweezer-shaped DNA origami template.After recognition of various molecular inputs,such as DNA strands,glutathione,or adenosine,the geometry and plasmonic circular dichroism(CD)signals of the AuNR–origami nanodevices produced corresponding changes.Then we designed and characterized a set of modular Boolean logic-gated nanodevices(YES,NOT,AND,OR)and proceeded to construct a complicated 3-input circuit capable of performing Boolean OR-NOT-AND operations.Our plasmonic logic devices transduced external inputs into conformational changes and near-infrared(NIR)chiral outputs.This DNA-based self-assembly strategy holds great potential for applications in programmable optical modulators,molecular information processing,and bioanalysis.