Regenerative Braking Control Strategy for Hybrid and Electric Vehicles Using Artificial Neural Networks

2014 ◽  
pp. 103-112 ◽  
Author(s):  
Sanketh S. Shetty ◽  
Orkun Karabasoglu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Regenerative Braking ◽  
Artificial Neural ◽  
Braking Control

Analyses of the Relation Between Degree of Mixing and Regenerative Braking in Hybrid Electric Vehicles

2014 ◽  
Vol 926-930 ◽  
pp. 743-746 ◽  
Author(s):  
Jing Ming Zhang ◽  
Jin Long Liu ◽  
Ming Zhi Xue
Keyword(s):  
Electric Vehicles ◽  
Control Strategy ◽  
Recovery Rate ◽  
Hybrid Electric Vehicles ◽  
Front Wheel ◽  
Regenerative Braking ◽  
Theoretical Derivation ◽  
Matlab Simulink ◽  
Hybrid Electric ◽  
Braking Control

The introduction of driving motors brings in the function of regenerative braking for Hybrid Electric Vehicles (HEV). In order to study the further information of regenerative braking, the relation between the degree of mixing in HEV and the recovery rate of regenerative braking was analyzed. The study object was the front-wheel driving HEV with the wire-control composite regenerative braking control strategy. Conclusions were deduced through the theoretical derivation. The braking model was established on the platform in MATLAB/SIMULINK and it was simulated within a HEV. The results indicate that the recovery rate would increase if the degree of mixing rises.

Study on Regenerative Braking Control Strategy for EV Based on the Vehicle Braking Mechanics

2012 ◽  
Vol 490-495 ◽  
pp. 1783-1787
Author(s):  
Guan Feng Li ◽  
Hong Xia Wang
Keyword(s):  
Energy Consumption ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Energy Recovery ◽  
Regenerative Braking ◽  
Force Distribution ◽  
Braking Force ◽  
Braking Control ◽  
Output Characteristics ◽  
Braking Energy Recovery

In order to improve the recovery of braking energy in electric vehicles, a braking force distribution control strategy is proposed which the braking force proportion of the front and rear wheels are distributed according to the brake strength, by analyzing the vehicle braking mechanics and related braking regulation, and combining with the motor output characteristics. A simulation is carried out with SIMULINK/ADVISOR, the results show that, comparing with ADVISOR braking force distribution control strategy, the control strategy not only meets braking stability well，but also there are obvious advantages in energy consumption per 100 kilometers，the rate of braking energy recovery and utilization.

scholarly journals Using Artificial Neural Networks for Indoor Climate Control in the Field of Preventive Conservation

2019 ◽  
Vol 111 ◽  
pp. 04054
Author(s):  
Simon Harasty ◽  
Andreas Daniel Böttcher ◽  
Steven Lambeck
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control Strategy ◽  
Ann Model ◽  
Indoor Climate ◽  
Climate Control ◽  
Building Stock ◽  
Preventive Conservation ◽  
Predictive Controller ◽  
Artificial Neural

In the field of preventive conservation, a main goal is the conservation of cultural heritage by establishing an appropriate indoor climate. Especially in applications with limited possibilities for the usage of HVAC systems, an optimization of the control strategy is needed. Because the changes in temperature and humidity are slow, the usage of predictive controller can be beneficial. Due to the availability of already gathered data, data driven models like artificial neural networks (ANN) are suitable as model. In this paper four different approaches for optimizing the control strategy regarding the requirements of preventive conservation are presented. The first approach is the modelling of the indoor climate of a building using an ANN. As further improvement and second application the adaption of a weather forecast to a local forecast is shown. Since the building stock has the biggest influence on the linkage between outdoor and indoor climate next to the air change rates, an ANN model for a building’s wall represents the third application. Finally, the potential for reducing the need for computational power by using an ANN instead of a non-linear optimization for the predictive controller is presented.

A PMSM fuzzy logic regenerative braking control strategy for electric vehicles

2021 ◽  
pp. 1-9
Author(s):  
Feng Liu
Keyword(s):  
Fuzzy Logic ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Recovery Rate ◽  
Energy Recovery ◽  
Motor Vehicle ◽  
Regenerative Braking ◽  
Storage Device ◽  
Vehicle Speed ◽  
Braking Control

Regenerative braking system is a system by which an energy conversion device is used to convert kinetic energy into electrical energy and store it in an energy storage device for use when the motor vehicle is driving. To improve the energy recovery rate of pure electric vehicles, a series regenerative braking control strategy based on PMSM fuzzy logic is proposed in this paper. According to this strategy, the motor braking shall be used as much as possible based on ensuring braking stability, 4 braking zones shall be divided according to the braking intensity, and different braking force distribution strategies shall be used, while comprehensively considering influencing factors such as vehicle speed, ECE regulations, battery, and motor characteristics. Simulink and Cruise are used for modeling and united simulation. The results show that the built model is accurate and reliable. The energy recovery rate can be improved effectively and the cruising range of pure electric vehicles can be extended based on proposed series regenerative braking control strategy.

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