An Integrated Control Framework for Torque Vectoring and Active Suspension System

Four-wheel independently driven electric vehicles(FWID-EV)endow a flexible and scalable control framework to improve vehicle performance.This paper integrates the torque vectoring and active suspension system(ASS)to enhance the vehicle’s longitudinal and vertical motion control performance.While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden.To facilitate the controller design and ease the load,a half-vehicle dynamics system is built and simplified to the linear-time-varying(LTV)model.Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety,energy-saving and comfort requirements.The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption.Finally,the effectiveness of the proposed controller is verified through the rapid-control-prototype(RCP)test.The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.

Energy-Optimal Braking Control Using a Double-Layer Scheme for Trajectory Planning and Tracking of Connected Electric Vehicles

Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution,few combine the connected vehicle technologies into braking velocity planning.If the braking intention is accessed by the vehicle-to-everything communication,the electric vehicles(EVs)could plan the braking velocity for recovering more vehicle kinetic energy.Therefore,this paper presents an energy-optimal braking strategy(EOBS)to improve the energy efficiency of EVs with the consideration of shared braking intention.First,a double-layer control scheme is formulated.In the upper-layer,an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm,which could derive the energy-optimal braking trajectory.In the lower-layer,the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system,then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety.Several simulations are conducted by jointing MATLAB and CarSim,the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy.Finally,the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration,battery charging power,and motor efficiency,which could be a guide to real-time control.

Adaptive Multi-modal Fusion Instance Segmentation for CAEVs in Complex Conditions:Dataset,Framework and Verifications

Current works of environmental perception for connected autonomous electrified vehicles(CAEVs)mainly focus on the object detection task in good weather and illumination conditions,they often perform poorly in adverse scenarios and have a vague scene parsing ability.This paper aims to develop an end-to-end sharpening mixture of experts(SMoE)fusion framework to improve the robustness and accuracy of the perception systems for CAEVs in complex illumination and weather conditions.Three original contributions make our work distinctive from the existing relevant literature.The Complex KITTI dataset is introduced which consists of 7481 pairs of modified KITTI RGB images and the generated LiDAR dense depth maps,and this dataset is fine annotated in instance-level with the proposed semi-automatic annotation method.The SMoE fusion approach is devised to adaptively learn the robust kernels from complementary modalities.Comprehensive comparative experiments are implemented,and the results show that the proposed SMoE framework yield significant improvements over the other fusion techniques in adverse environmental conditions.This research proposes a SMoE fusion framework to improve the scene parsing ability of the perception systems for CAEVs in adverse conditions.

Engineering a fumaric acid-responsive two-component biosensor for dynamic range improvement in Escherichia coli

Due to the selective permeability of the cytomembrane,high-yield fumaric acid strains form a steep difference between intra-and extracellular concentrations.Intracellular biosensors cannot detect the real concentration change of extracellular fumaric acid.To overcome this limitation,a two-component biosensor(TCB)that could respond to extracellular fumaric acid was designed based on the DcuS-DcuR two-component system.The two-component system consists of a histidine kinase(SK)and response regulator.SK is a transmembrane histidine kinase sensor that can detect concentration changes in extracellular compounds.To improve the dynamic range of the constructed fumaric acid TCB,we optimized the expression ratio and expression intensity of dcuS and dcuR.We found that the optimum expression ratio of dcuS:dcuR was 46:54.Under this ratio,the higher was the expression level,the greater the dynamic range.In addition,we modified the ATP-binding site on the DcuS,and the final dynamic range of the TCB reached 6.6-fold.Overall,the obtained fumaric acid-responsive TCB with a high dynamic range is reported for the first time,providing a synthetic biology tool for high-throughput screening and dynamic metabolic regulation of fumaric acid cell factories.

Predictive lane-changing control for platoon based on cloud control system in highway scenarios

This research proposes a predictive lane-changing control system for platoon based on cloud control system(CPPLC),Which is designed to improve the safety,economy,and driving efficiency of a platoon.The system constructs a vehicle-cloud hierarchical control architecture,with the cloud as the decision-making layer,which collaboratively optimizes the longitudinal acceleration and lateral lane-changing decisions of the platoon based on a model predictive control framework to improve the comprehensive performance of platoon driving.The vehicle is the execution layer,which cooperates with the decision-making in the cloud to generate the platoon driving trajectory and carry out tracking control to ensure the safety of platoon driving.The proposed system is evaluated based on a joint simulation platform consisting of Sumo,Matlab/Simulink,and Trucksim,and the results show that the system can realize the improvement of the economy and driving efficiency while ensuring the safety compared with the conventional microscopic driving model.