Parallel Hybrid Electric Vehicle Powertrain Research Articles

An active multiobjective real-time vibration control algorithm for parallel hybrid electric vehicle

To improve the contradiction between vehicle dynamic performance and ride comfort by using the motor active output torque compensation method to reduce the torsional vibration of the hybrid electric vehicle (HEV) powertrain, an active multiobjective real-time vibration control (AMRVC) algorithm based on model prediction control (MPC) is proposed in this paper, and the current mainstream parallel HEV is taken as the research object to verify the proposed algorithm. Firstly, the torsional vibration model of the parallel HEV powertrain is established, simplified, and verified according to the experimental data, which provides the basis for the multimode adaptability and the application of the controller. Then, based on the state space equation of the torsional vibration system, the MPC controller considering the balance between ride comfort and power performance is designed, which can meet the multiobjective and real-time control effect of the hybrid power system while realizing the active vibration control. Finally, the effectiveness and real-time performance of the AMRVC algorithm are verified by multimode simulation analysis and hardware-in-the-loop (HIL) experiment. The simulation results show that the AMRVC algorithm based on MPC has good real-time application conditions, which can well coordinate the multiobjective requirements of vehicle ride comfort and dynamic performance, and remarkably improve the torsional vibration response of the powertrain by more than 55% in all HEV typical operating modes.

Multi-objective optimization of a dual mass flywheel with centrifugal pendulum vibration absorbers in a single-shaft parallel hybrid electric vehicle powertrain for torsional vibration reduction

This research deals with an optimization of a dual mass flywheel (DMF) with centrifugal pendulum vibration absorbers (CPVAs) used in a single-shaft parallel hybrid electric vehicle (HEV) powertrain for the reduction of the torsional vibration at the rotor of the electric motor and the total moment of inertia of the DMF and CPVAs. Unlike the previous studies, this research considers the overall behavior of an entire powertrain with an in-line four-cylinder engine and a five-speed automatic transmission for the optimization of the DMF with CPVAs. For this purpose, the multibody dynamics model of the HEV powertrain is constructed and used to analyze the transient response of the system for a given gas pressure map and acceleration condition of the vehicle. Then, a multi-objective genetic algorithm is applied to determine the Pareto optimal front that yields the design parameter values of the DMF and CPVAs effective in the torsional vibration reduction and inertia minimization. The result of the optimization is compared with the HEV powertrain system with a flywheel only, and shows that the vibrations transmitted to the motor are significantly reduced in all the solutions of the Pareto front. By using the method proposed in this research, various parameter combinations of the DMF and CPVAs can be considered in the early design process.

Power Demand Forecasting Method and Its Application in Parallel Hybrid Electric Vehicle Powertrain Control

For hybrid electric vehicles (HEVs) , vehicle velocity and road gradient are essential. They can decide the driving power demand and have a great impact on its powertrain energy management performance. This paper presents a power demand forecasting method, aiming to predict the short-term future velocity and road gradient in real-time for the predictive energy management of HEVs. The artificial neural networks (ANNs) and the autoregressive integrated moving average(ARIMA) learning model are established with the data from expereiments to predict the vehicle velocity and the road gradient during each control horizon. Simulation results indicate the predictors can reflect the varying tendency of the velocity and the road gradient perfectly, validating the good performance of the method. Then we employ Model Predictive Control (MPC) to build the predictive energy management strategy, and solve the optimal powertrain control problem during each control horizon with Dynamic Programming (DP), getting the HEV fuel economy improved by about 5%.

Estimation of Synchromesh Frictional Torque and Output Torque in a Clutchless Automated Manual Transmission of a Parallel Hybrid Electric Vehicle

This paper studies the estimation of the synchromesh frictional torque and the output torque of the integrated system of an electric motor and a clutchless automated manual transmission (AMT) as part of a two-shaft parallel hybrid electric vehicle powertrain. The case study powertrain is discussed, and the dynamical model of the powertrain from the electric motor to the AMT output shaft, where it is linked to the torque coupler, is developed constituting the basis for the observer design. Given the fact that the aforementioned torques are unknown inputs to the system, they are modeled as state variables of a fictitious unforced linear time-invariant system in order to be aggregated into the systems dynamics. For the augmented system of the actual and fictitious states, a deterministic Luenberger observer and a stochastic Kalman–Bucy filter (KBF) are designed to estimate the synchromesh frictional torque and the output torque of the AMT. The estimation is based on measuring angular velocities of the electric motor and the AMT output shaft, together with the imparted electromagnetic torque of the traction motor on the system. A set of experiments with distinct scenarios is performed to compare the performance of the designed observers and to quantify by how much the KBF can improve the root-mean-square error (RMSE) of the estimation by mitigating the effect of the process and measurement noises. Ultimately, after validation of the designed observers, the estimated value of the synchromesh frictional torque is exploited in a closed-loop feedback configuration in order to track desired trajectories such as step, ramp, and sinusoidal torque commands.

Control of Engine-Starts for Optimal Drivability of Parallel Hybrid Electric Vehicles

The pretransmission parallel hybrid electric vehicle (HEV) with a single electric motor requires relatively little changes from existing powertrain configurations. This configuration, however, has a challenging drivability issue during engine-starts because the electric motor must simultaneously provide the demanded propulsion torque and start the engine. Depending on the propulsion power level, such engine-start process may require a trade-off between drivability and quick start. The goal of this study is to find theoretical performance limits and corresponding optimal control strategies that achieve the balance between these two conflicting goals. We first develop a simplified parallel HEV powertrain model to predict the engine, clutch, and vehicle dynamics. Assuming that the clutch torque can be accurately estimated and perfectly cancelled, an optimal engine-start control problem is formulated to minimize engine-start time while supplying the driver demanded torque. This nonlinear constrained optimal control problem is solved both analytically and numerically. For some special cases, the optimization problem can be solved analytically to obtain a closed-form solution. For the numerical method, dynamic programming (DP) is used, and both analytical and numerical solutions show that selecting a proper level of constant clutch pressure is the key to achieve near-optimal drivability performance. Furthermore, the DP control policy is found to be time-invariant, and thus can be implemented in the form of a full state feedback controller.

Dynamic Model of a Pre-Transmission Parallel Hybrid Electric Vehicle

This paper describes mathematical modeling, analysis, and simulation in an iterative process including development of Parallel Hybrid Electric Vehicle (PHEV) hardware, system performance measures, control software, and complete PHEV powertrain system synthesis. A PHEV is synthesized using a conventional spark ignited (SI) internal combustion engine (ICE), a dry clutch between the ICE and an alternating current (AC) induction traction motor, a converterless automatic. transmission, and a differential and halfshafts that drive front wheels. An operating strategy is presented with simulations that demonstrate performance.