Powertrain Torque Control Based on Torque Observer

To reduce shock during transmission gear shift, a transmission torque feedback closed loop control system is proposed based on the powertrain system model and a torque observer. The ignition time of engine was delayed to reduce transmission output shaft torque during gear shift. In contrast to traditional control method, the closed loop control system based on torque observer can obviously reduce the transmission output shaft torque during gear shift. It can be concluded that by way of torque feedback closed loop control, transmission shift shock can be reduced.

THROTTLE CONTROL STRATEGIES IN THE PROCESS OF INTEGRATED POWERTRAIN CONTROL

Combining with the development of automated manual transmission （AMT）, the various throttle control demands are analyzed under different working conditions of AMT such as tracking acceleration pedal, start, shift and so on. Based on simulation, the responding throttle control strategies are proposed, and a simple but effective throttle control method is presented. The testing results have proved that the strategies are effective for improving the pedal tracking precision and the qualities of start and shift.

Two-stage on-board optimization of merging velocity planning with energy management for HEVs

This paper proposes a two-stage hierarchy control system with model predictive control(MPC)for connected parallel HEVs with available traffic information.In the first stage,a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging.After formulating the merging problem into a nonlinear optimization problem,a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road,respectively.The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage.In the second stage,an energy management strategy(EMS)for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency.The formulated nonlinear optimization problem is solved by sequential quadratic programming(SQP)method under the same receding horizon.The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule.

Distributed optimal energy consumption control of HEVs under MFG-based speed consensus

This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus.Large scale vehicles in a traffic flow is targeted instead of individual vehicles,and it is assumed that the propulsion power of vehicles is hybrid electric powertrain.The control scheme is designed in the following two stages.In the first stage,in order to achieve speed consensus,the acceleration control law is designed by applying the MFG(mean-field game)theory.In the second stage,optimal powertrain control for minimizing energy consumption is obtained through coordinate the engine and the motor under the acceleration constraint.The simulation is conducted to demonstrate the effectiveness of the proposed control strategy.

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything(V2X),including geographic information,vehicle-to-infrastructure(V2I)information and vehicle-to-vehicle(V2V)information.The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator,where the running route is generated based on real-world data with slope and intersection position.The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time.To show the HEV powertrain characteristics,a case study is given with the speed planning and energy management strategy.

On-board torque management approach to the E-COSM benchmark problem with a prediction-based engine assignment

This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles(HEVs)on a road with a slope.We assume that HEVs are in a connected environment with real-time vehicle-to-everything information,including geographic information,vehicle-to-infrastructure information and vehicle-to-vehicle information.The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time.The proposed strategy includes multiple rules and model predictive control(MPC).The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode.To improve fuel economy,the optimal energy management strategy is primarily considered,and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints,a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon.Therefore,this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed.To validate the proposed optimization strategy,a powertrain control simulation platform in a traffic-in-the-loop environment is constructed,and case study results performed on the constructed platform are reported and discussed.