scholarly journals Nonlinear Controllers for a Light-Weighted All-Electric Vehicle Using Chebyshev Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Vikas Sharma ◽  
Shubhi Purwar
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Aerodynamic Drag ◽  
Rolling Resistance ◽  
Adaptive Controller ◽  
Dc Motor ◽  
Resistance Coefficient ◽  
Chebyshev Neural Network ◽  
Nonlinear Controllers ◽  
Aerodynamic Drag Coefficient

Two nonlinear controllers are proposed for a light-weighted all-electric vehicle: Chebyshev neural network based backstepping controller and Chebyshev neural network based optimal adaptive controller. The electric vehicle (EV) is driven by DC motor. Both the controllers use Chebyshev neural network (CNN) to estimate the unknown nonlinearities. The unknown nonlinearities arise as it is not possible to precisely model the dynamics of an EV. Mass of passengers, resistance in the armature winding of the DC motor, aerodynamic drag coefficient and rolling resistance coefficient are assumed to be varying with time. The learning algorithms are derived from Lyapunov stability analysis, so that system-tracking stability and error convergence can be assured in the closed-loop system. The control algorithms for the EV system are developed and a driving cycle test is performed to test the control performance. The effectiveness of the proposed controllers is shown through simulation results.

scholarly journals An Ultra-Efficient Lightweight Electric Vehicle—Power Demand Analysis to Enable Lightweight Construction

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 766
Author(s):  
Pietro Stabile ◽  
Federico Ballo ◽  
Gianpiero Mastinu ◽  
Massimiliano Gobbi
Keyword(s):  
Energy Consumption ◽  
Environmental Impact ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Aerodynamic Drag ◽  
Lightweight Design ◽  
Inertial Force ◽  
Rolling Resistance ◽  
Resistance Coefficient ◽  
Power Demand

A detailed analysis of the power demand of an ultraefficient lightweight-battery electric vehicle is performed. The aim is to overcome the problem of lightweight electric vehicles that may have a relatively bad environmental impact if their power demand is not extremely reduced. In particular, electric vehicles have a higher environmental impact during the production phase, which should be balanced by a lower impact during the service life by means of a lightweight design. As an example of an ultraefficient electric vehicle, a prototype for the Shell Eco-marathon competition is considered. A “tank-to-wheel” multiphysics model (thermo-electro-mechanical) of the vehicle was developed in “Matlab-Simscape”. The model includes the battery, the DC motors, the motor controller and the vehicle drag forces. A preliminary model validation was performed by considering experimental data acquisitions completed during the 2019 Shell Eco-marathon European competition at the Brooklands Circuit (UK). Numerical simulations are employed to assess the sharing of the energy consumption among the main dissipation sources. From the analysis, we found that the main sources of mechanical dissipation (i.e., rolling resistance, gravitational/inertial force and aerodynamic drag) have the same role in the defining the power consumption of such kind of vehicles. Moreover, the effect of the main vehicle parameters (i.e., mass, aerodynamic coefficient and tire rolling resistance coefficient) on the energy consumption was analyzed through a sensitivity analysis. Results showed a linear correlation between the variation of the parameters and the power demand, with mass exhibiting the highest influence. The results of this study provide fundamental information to address critical decisions for designing new and more efficient lightweight vehicles, as they allow the designer to clearly identify which are the main parameters to keep under control during the design phase and which are the most promising areas of action.

Rolling resistance, vertical load and optimal number of wheels in human-powered vehicle design

2016 ◽  
Vol 231 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Paolo Baldissera ◽  
Cristiana Delprete
Keyword(s):  
Aerodynamic Drag ◽  
Rolling Resistance ◽  
Resistance Coefficient ◽  
Optimal Number ◽  
Vehicle Design ◽  
Definition Of ◽  
Human Powered ◽  
The Relationship ◽  
Resistance Coefficients ◽  
Strict Definition

Even if it makes a smaller contribution than aerodynamic drag, rolling resistance plays a non-negligible role in the efficiency of human-powered vehicles, whether they are designed for daily commuting or to set speed records. The literature, experimental evidence and models show that the rolling resistance coefficient of cycling wheels strongly depends on the supported load, suggesting that the number of wheels and the load distribution could play a role in vehicle design and in road-test data analysis. Starting with an in-depth look at the relationship between a single wheel and overall vehicle rolling resistance coefficients, an analysis is proposed and discussed with the aim of minimizing the rolling resistance of a vehicle. Finally, a parametric surface response model for rolling resistance is obtained as a function of wheel size and the number of wheels. The overall analysis overturns the popular assumption according to which ‘the more wheels, the more rolling resistance’, at least according to a strict definition of the phenomenon.

scholarly journals Prediction of Fouling in Condenser Based on Fuzzy Stage Identification and Chebyshev Neural Network

2013 ◽  
Vol 13 (2) ◽  
pp. 94-99 ◽  
Author(s):  
Shaosheng Fan ◽  
Qingchang Zhong
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Parameter Optimization ◽  
Adaptive Algorithm ◽  
External Disturbance ◽  
Operational Parameters ◽  
Chebyshev Neural Network ◽  
Adaptive Parameter ◽  
Novel Approach ◽  
Variation Trends

The prediction of fouling in condenser is heavily influenced by the periodic fouling process and dynamics change of the operational parameters, to deal with this problem, a novel approach based on fuzzy stage identification and Chebyshev neural network is proposed. In the approach, the overall fouling is separated into hard fouling and soft fouling, the variation trends of these two kinds of fouling are approximated by using Chebyshev neural network, respectively, in order to make the prediction model more accurate and robust, a fuzzy stage identification method and adaptive algorithm considering external disturbance are introduced, based on the approach, a prediction model is constructed and experiment on an actual condenser is carried out, the results show the proposed approach is more effective than asymptotic fouling model and adaptive parameter optimization prediction model.

Sign in / Sign up

Export Citation Format

Share Document