Gear-shifting in a novel modular multi-speed transmission for electric vehicles using linear quadratic integral control

2018 ◽  
Vol 128 ◽  
pp. 359-367
Author(s):  
M. Roozegar ◽  
J. Angeles
Keyword(s):  
Electric Vehicles ◽  
Linear Quadratic ◽  
Integral Control ◽  
Quadratic Integral

scholarly journals Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5538
Author(s):  
Bảo-Huy Nguyễn ◽  
João Pedro F. Trovão ◽  
Ronan German ◽  
Alain Bouscayrol
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Engine Fuel ◽  
Linear Quadratic ◽  
Loop Control ◽  
Parallel Hybrid ◽  
Linear Quadratic Regulation ◽  
Hybrid Electric

Optimization-based methods are of interest for developing energy management strategies due to their high performance for hybrid electric vehicles. However, these methods are often complicated and may require strong computational efforts, which can prevent them from real-world applications. This paper proposes a novel real-time optimization-based torque distribution strategy for a parallel hybrid truck. The strategy aims to minimize the engine fuel consumption while ensuring battery charge-sustaining by using linear quadratic regulation in a closed-loop control scheme. Furthermore, by reformulating the problem, the obtained strategy does not require the information of the engine efficiency map like the previous works in literature. The obtained strategy is simple, straightforward, and therefore easy to be implemented in real-time platforms. The proposed method is evaluated via simulation by comparison to dynamic programming as a benchmark. Furthermore, the real-time ability of the proposed strategy is experimentally validated by using power hardware-in-the-loop simulation.

scholarly journals Implementation and intelligent gain tuning feedback–based optimal torque control of a rotary parallel robot

2021 ◽  
pp. 107754632110191
Author(s):  
Farzam Tajdari ◽  
Naeim Ebrahimi Toulkani
Keyword(s):  
Real Time ◽  
Tracking Error ◽  
Parallel Robot ◽  
Torque Control ◽  
Test Bed ◽  
Linear Quadratic ◽  
Control Effort ◽  
The Real ◽  
Unknown Disturbances ◽  
Quadratic Integral

Aiming at operating optimally minimizing error of tracking and designing control effort, this study presents a novel generalizable methodology of an optimal torque control for a 6-degree-of-freedom Stewart platform with rotary actuators. In the proposed approach, a linear quadratic integral regulator with the least sensitivity to controller parameter choices is designed, associated with an online artificial neural network gain tuning. The nonlinear system is implemented in ADAMS, and the controller is formulated in MATLAB to minimize the real-time tracking error robustly. To validate the controller performance, MATLAB and ADAMS are linked together and the performance of the controller on the simulated system is validated as real time. Practically, the Stewart robot is fabricated and the proposed controller is implemented. The method is assessed by simulation experiments, exhibiting the viability of the developed methodology and highlighting an improvement of 45% averagely, from the optimum and zero-error convergence points of view. Consequently, the experiment results allow demonstrating the robustness of the controller method, in the presence of the motor torque saturation, the uncertainties, and unknown disturbances such as intrinsic properties of the real test bed.

scholarly journals Data-Driven Model-Free Adaptive Control of Z-Source Inverters

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7438
Author(s):  
Yasin Asadi ◽  
Amirhossein Ahmadi ◽  
Sasan Mohammadi ◽  
Ali Moradi Amani ◽  
Mousa Marzband ◽  
...  
Keyword(s):  
State Feedback ◽  
Adaptive Controller ◽  
Green Energy ◽  
Data Driven ◽  
Control Action ◽  
Linear Quadratic ◽  
Model Based ◽  
Model Free ◽  
Quadratic Integral ◽  
Lyapunov Sense

The universal paradigm shift towards green energy has accelerated the development of modern algorithms and technologies, among them converters such as Z-Source Inverters (ZSI) are playing an important role. ZSIs are single-stage inverters which are capable of performing both buck and boost operations through an impedance network that enables the shoot-through state. Despite all advantages, these inverters are associated with the non-minimum phase feature imposing heavy restrictions on their closed-loop response. Moreover, uncertainties such as parameter perturbation, unmodeled dynamics, and load disturbances may degrade their performance or even lead to instability, especially when model-based controllers are applied. To tackle these issues, a data-driven model-free adaptive controller is proposed in this paper which guarantees stability and the desired performance of the inverter in the presence of uncertainties. It performs the control action in two steps: First, a model of the system is updated using the current input and output signals of the system. Based on this updated model, the control action is re-tuned to achieve the desired performance. The convergence and stability of the proposed control system are proved in the Lyapunov sense. Experiments corroborate the effectiveness and superiority of the presented method over model-based controllers including PI, state feedback, and optimal robust linear quadratic integral controllers in terms of various metrics.

An integrated control strategy of path following and lateral motion stabilization for autonomous distributed drive electric vehicles

2019 ◽  
pp. 095440701988416
Author(s):  
Yuan Zou ◽  
Ningyuan Guo ◽  
Xudong Zhang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Integrated Control ◽  
Path Following ◽  
Control Technique ◽  
Programming Algorithm ◽  
Linear Quadratic ◽  
And Control

This article proposes an integrated control strategy of autonomous distributed drive electric vehicles. First, to handle the multi-constraints and integrated problem of path following and the yaw motion control, a model predictive control technique is applied to determine optimal front wheels’ steering angle and external yaw moment synthetically and synchronously. For ensuring the desired path-tracking performance and vehicle lateral stability, a series of imperative state constraints and control references are transferred in the form of a matrix and imposed into the rolling optimization mechanism of model predictive control, where the detailed derivation is also illustrated and analyzed. Then, the quadratic programming algorithm is employed to optimize and distribute each in-wheel motor’s torque output. Finally, numerical simulation validations are carried out and analyzed in depth by comparing with a linear quadratic regulator–based strategy, proving the effectiveness and control efficacy of the proposed strategy.

Optimization control for dynamic vibration absorbers and active suspensions of in-wheel-motor-driven electric vehicles

2020 ◽  
Vol 234 (9) ◽  
pp. 2377-2392
Author(s):  
Mingchun Liu ◽  
Yuanzhi Zhang ◽  
Juhua Huang ◽  
Caizhi Zhang
Keyword(s):  
Electric Vehicles ◽  
Ride Comfort ◽  
Linear Quadratic Regulator ◽  
Vibration Absorber ◽  
Linear Quadratic ◽  
Dynamic Vibration Absorber ◽  
Dynamic Vibration ◽  
Optimization Control ◽  
Motor Vibration ◽  
Suspension Performance

This study addresses the challenges of ride comfort improvement and in-wheel-motor vibration suppression in in-wheel-motor-driven electric vehicles. First, a mathematical model of a quarter vehicle equipped with a dynamic vibration absorber and an active suspension is developed. Then, a two-stage optimization control method is proposed to improve the coupled dynamic vibration absorber–suspension performance. In the first stage, a linear quadratic regulator controller based on particle swarm optimization is designed for the dynamic vibration absorber to suppress the in-wheel-motor vibration, in which the dynamic vibration absorber parameters and linear quadratic regulator controller weighting factors are optimally matched by using the particle swarm optimization algorithm. In the second stage, a finite-frequency H∞ controller is designed in the framework of linear matrix inequality optimization for the active suspension to improve vehicle ride comfort. Suspension performance factors, including suspension working space and road-holding ability, are taken as constraints in both stages. The proposed method simultaneously improves vehicle ride comfort and suppresses in-wheel-motor vibration. Finally, the effectiveness and superiority of the proposed method are illustrated through comparison simulations.

Health-Aware and User-Involved Battery Charging Management for Electric Vehicles Using Linear Quadratic Control

2015 ◽  
Author(s):  
Huazhen Fang ◽  
Yebin Wang
Keyword(s):  
Control Theory ◽  
Electric Vehicles ◽  
User Satisfaction ◽  
User Involvement ◽  
Linear Quadratic Control ◽  
Health Awareness ◽  
Linear Quadratic ◽  
Battery Charging ◽  
Simulation Based ◽  
Battery Systems

This paper studies control-theory-enabled charging management for battery systems in electric vehicles (EVs). Charging is a crucial factor for the battery performance and life as well as EV users’ anxiety. Existing methods run with two shortcomings: insufficiency of battery health awareness during charging, and failure to include the user into the charging loop. To address such issues, we propose to perform charging that deals with both health protection and user-specified charging needs or objectives. Capitalizing on the linear quadratic control theory, a set of charging strategies are developed. A simulation-based study demonstrates their effectiveness and potential. We expect that charging with health awareness and user involvement will improve not only the battery longevity but also user satisfaction.

Sign in / Sign up

Export Citation Format

Share Document