Active Engine Vibration Control During Start/Stop in a Hybrid Electric Vehicle

2016 ◽  
Author(s):  
Jose Velazquez Alcantar ◽  
Rajit Johri ◽  
Ming Kuang
Keyword(s):  
Real Time ◽  
Hybrid Electric Vehicle ◽  
System Analysis ◽  
Production Control ◽  
Engine Model ◽  
Engine Vibration ◽  
Linear System Analysis ◽  
Power Split ◽  
Feedforward Controller ◽  
Fidelity Model

Disturbances during engine starts and stops can propagate to the drive wheels with minimal damping in the power split hybrid transaxle due to the system architecture. One such disturbance is the engine cranking torque from pumping & compression during the motoring phase of engine starts and stops before fueling. This paper proposes a control system to compensate for the engine disturbance and reduce the magnitude of the disturbance on the driveline. A high fidelity model of the power split transaxle is developed and validated with test data for NVH analysis. A simple model of a four cylinder engine is developed that can run in real-time and accurately estimate the cranking torque disturbance from the engine. It is show that the engine speed in the power split transaxle is controlled via a generator speed feedback loop. Performing a linear system analysis reveals that the addition of a feedforward term in the loop which is based on the engine cranking torque can reduce the driveline disturbance. It is shown through simulation that the proposed engine model with the feedforward controller can reduce the engine disturbance on the driveline. The engine model and feedforward controller are finally implemented on the production control module of a test vehicle and it is shown that the proposed control strategy can reduce the driveline disturbance by as much a 65% during engine starts. Moreover, it shown that the engine model can run in real-time and correlates well with in-cylinder pressure data measured from the engine.

Fast Online-Implementable Sub-Optimal Energy Management of a Power-Split Hybrid Electric Vehicle

2013 ◽  
Author(s):  
Jian Dong ◽  
Rui Cheng ◽  
Zuomin Dong ◽  
Curran Crawford
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Real Time ◽  
Electric Vehicle ◽  
Energy Management ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Powertrain ◽  
Power Split

The current focus of HEV controller design is on the development of real-time implementable energy management strategies that can approximate the global optimal solution closely. In this work, the Toyota Prius power-split hybrid powertrain is used as a case study for developing online energy management strategy for hybrid electric vehicle. The power-split hybrid powertrain combines the advantages of both the series and parallel hybrid powertrain and has been appealing to the auto-makers in the past years. The addition of two additional electric machines and a Planetary Gear Sets (PGS) allows more flexibility in terms of control at some cost of complexity. A forward-looking dynamic model of the power-split powertrain system is developed and implemented in Simulink first. An optimal control problem is formulated, which is further reduced to an optimal control problem with a single-variable objective function and a single-state subject to both dynamic constraint and boundary constraint. The reduced optimal control problem is then solved by an on-line (real-time) implementable approach based on Pontryagin’s Minimum Principal (PMP), where the costate p is adapted based on SOC feedback. Simulation results on standard driving cycles are compared using the proposed optimal control strategy and a rule-based control strategy. The results have shown significant improvement in fuel economy comparing to the baseline vehicle model, and the proposed online (real-time) PMP control algorithm with an adaptive costate p is very close to the optimal PMP solution with a constant costate. The proposed optimal control has a fast computation speed and calculates the optimal decision “dynamically” without the necessity of knowing future driving cycle information and can be practically implemented in real-time.

scholarly journals Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2278 ◽  
Author(s):  
Hsiu-Ying Hwang ◽  
Jia-Shiun Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Swarm Optimization ◽  
Time Optimization ◽  
Optimization Control ◽  
Power Split ◽  
Hybrid Electric ◽  
Real Time Optimization

This research focused on real-time optimization control to improve the fuel consumption of power-split hybrid electric vehicles. Particle swarm optimization (PSO) was implemented to reduce fuel consumption for real-time optimization control. The engine torque was design-variable to manage the energy distribution of dual energy sources. The AHS II power-split hybrid electric system was used as the powertrain system. The hybrid electric vehicle model was built using Matlab/Simulink. The simulation was performed according to US FTP-75 regulations. The PSO design objective was to minimize the equivalent fuel rate with the driving system still meeting the dynamic performance requirements. Through dynamic vehicle simulation and PSO, the required torque value for the whole drivetrain system and corresponding high-efficiency engine operating point can be found. With that, the two motor/generators (M/Gs) supplemented the rest required torques. The composite fuel economy of the PSO algorithm was 46.8 mpg, which is a 9.4% improvement over the base control model. The PSO control strategy could quickly converge and that feature makes PSO a good fit to be used in real-time control applications.

Battery Dynamic Modeling and Real-Time State-of-Charge Estimation in Hybrid Electric Vehicle Application

2001 ◽  
Author(s):  
Gino Paganelli ◽  
Yann G. Guezennec ◽  
Hansung Kim ◽  
Avra Brahma
Keyword(s):  
Real Time ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Soc Estimation ◽  
Current Voltage ◽  
Battery Capacity ◽  
Power Split ◽  
On Line ◽  
Hybrid Electric ◽  
State Of Charge Estimation

Abstract On-line Sate-of-Charge (SOC) estimation in pure electric or hybrid-electric (HEV) vehicles is a challenging problem, due to the very dynamic nature of the current/voltage history under actual driving conditions. However, on-line, reliable SOC estimation is critical in these applications, particularly in charge-sustaining HEV, where the battery capacity is relatively small and where the energy management strategy needs an accurate SOC estimation in order to optimize the power split between the ICE and EM. The research described in this paper focuses on two aspects of the same problem. The first aspect is the development, calibration and validation of a dynamic battery model which represents the essential dynamic behavior of the battery pack HEV application. The second part of this paper is the development, implementation and validation of an appropriate in-vehicle SOC estimator for use with our supervisory HEV controller. While the implementation approaches for theses two facets of the same problem are significantly different due to the real-time computational requirements, they represent the same battery dynamics. This algorithm has been applied to a real charge-sustaining HEV vehicle and the experimental results are presented.

scholarly journals Diagnostics and Prognostics of Energy Conversion Processes via Knowledge-Based Systems

Proceedings ◽  
2020 ◽  
Vol 58 (1) ◽  
pp. 1
Author(s):  
Roberto Melli ◽  
Enrico Sciubba
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
System Analysis ◽  
Intelligent System ◽  
Time Derivative ◽  
Time History ◽  
Control Procedure ◽  
Time Data ◽  
Ongoing Research

This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. The expert system is implemented in the CLIPS environment and is denominated PROMISA as the acronym for Prognostic Module for Intelligent System Analysis. It generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem. The expert procedure, working where and if necessary with the support of a process simulator, derives from the available real-time data a list of selected performance indicators for each plant component. For a set of faults, pre-defined with the help of the plant operator (Domain Expert), proper rules are defined in order to establish whether the component is working correctly; in several instances, since one single failure (symptom) can originate from more than one fault (cause), complex sets of rules expressing the combination of multiple indices have been introduced in the knowledge base as well. Creeping faults are detected by analyzing the trend of the variation of an indicator over a pre-assigned interval of time. Whenever the value of this ‘‘discrete time derivative’’ becomes ‘‘high’’ with respect to a specified limit value, a ‘‘latent creeping fault’’ condition is prognosticated. The expert system architecture is based on an object-oriented paradigm. The knowledge base (facts and rules) is clustered—the chunks of knowledge pertain to individual components. A graphic user interface (GUI) allows the user to interrogate PROMISA about its rules, procedures, classes and objects, and about its inference path. The paper also presents the results of some simulation tests.

Optimal control of a novel uninterrupted multi-speed transmission for hybrid electric mining trucks

2019 ◽  
Vol 233 (12) ◽  
pp. 3235-3245 ◽  
Author(s):  
Weiwei Yang ◽  
Jiejunyi Liang ◽  
Jue Yang ◽  
Nong Zhang
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Control Strategy ◽  
Dynamic Models ◽  
Fuel Efficiency ◽  
Real Time Control ◽  
Traction Motor ◽  
Time Control ◽  
Power Split ◽  
Shift Strategy

Considering the energy consumption and specific performance requirements of mining trucks, a novel uninterrupted multi-speed transmission is proposed in this paper, which is composed of a power-split device, and a three-speed lay-shaft transmission with a traction motor. The power-split device is adapted to enhance the efficiency of the engine by adjusting the gear ratio continuously. The three-speed lay-shaft transmission is designed based on the efficiency map of traction motor to guarantee the drivability. The combination of the power-split device and three-speed lay-shaft transmission can realize uninterrupted gear shifting with the proposed shift strategy, which benefits from the proposed adjunct function by adequately compensating the torque hole. The detailed dynamic models of the system are built to verify the effectiveness of the proposed shift strategy. To evaluate the maximum fuel efficiency that the proposed uninterrupted multi-speed transmission could achieve, dynamic programming is implemented as the baseline. Due to the “dimension curse” of dynamic programming, a real-time control strategy is designed, which can significantly improve the computing efficiency. The simulation results demonstrate that the proposed uninterrupted multi-speed transmission with dynamic programming and real-time control strategy can improve fuel efficiency by 11.63% and 8.51% compared with conventional automated manual transmission system, respectively.

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