scholarly journals Eco-Driving Optimal Controller for Autonomy Tracking of Two-Wheel Electric Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Y. Bello ◽  
T. Azib ◽  
C. Larouci ◽  
M. Boukhnifer ◽  
N. Rizoug ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Consumption Rate ◽  
Driver Behavior ◽  
Optimal Controller ◽  
Model Predictive Controller ◽  
Speed Limits ◽  
Additional Information ◽  
Predictive Controller ◽  
The Cost ◽  
Energy Consumption Rate

The eco-driving profiles are algorithms able to use additional information in order to create recommendations or limitation over the driver capabilities. They increase the autonomy of the vehicle but currently their usage is not related to the autonomy required by the driver. For this reason, in this paper, the eco-driving challenge is translated into two-layer optimal controller designed for pure electric vehicles. This controller is oriented to ensure that the energy available is enough to complete a demanded trip, adding speed limits to control the energy consumption rate. The mechanical and electrical models required are exposed and analyzed. The cost function is optimized to correspond to the needs of each trip according to driver behavior, vehicle, and traject information. The optimal controller proposed in this paper is a nonlinear model predictive controller (NMPC) associated with a nonlinear unidimensional optimization. The combination of both algorithms allows increasing around 50% the autonomy with a limitation of the 30% of the speed and acceleration capabilities. Also, the algorithm is able to ensure a final autonomy with a 1.25% of error in the presence of sensor and actuator noise.

Mode-switch model predictive controller with “pre-contact” method for alleviating driveline vibration of electric vehicles considering backlash

2020 ◽  
Vol 234 (8) ◽  
pp. 2176-2194
Author(s):  
Xingyang Lu ◽  
Tongli Lu ◽  
Benben Chai
Keyword(s):  
Electric Vehicles ◽  
Bench Test ◽  
Contact Method ◽  
State Variables ◽  
Mode Switch ◽  
Contact Mode ◽  
Model Predictive Controller ◽  
Essential Information ◽  
Motor Torque ◽  
Predictive Controller

The backlash between engaging components in a driveline is inevitable and contributes to the nonlinearity of the driveline. The existing motor controllers of an electric vehicle usually ignore the backlash, which often brings impacts and vibration. This paper proposes an active driveline vibration controller for electric vehicles. A nonlinear driveline model considering backlash and wheel slip ratio is established in MATLAB/Simulink, and the results of bench test proved that the model could effectively reflect the transient dynamics of the electric driveline. Based on this model, a dual extended Kalman filter observer is designed to estimate both the system state variables and vehicle mass, which are essential information for the controller design. Then, a mode-switch model predictive controller based on two linearized models is proposed to alleviate the impacts and vibration caused by the transient change of motor torque. The proposed controller would identify whether the driveline is operating in “contact mode” or “backlash mode” and thus generates an optimal motor torque by solving a Quadratic Programing. Note that the control targets and model structures in two modes are different. Furthermore, a “pre-contact” method is proposed as an additional part to handle the condition when motor command torque is zero. Simulation results demonstrate that the proposed controller can effectively alleviate the impacts and vibration in the electric driveline while keeping the torque delay negligible. Moreover, the robustness of the proposed controller against estimation errors and system noises are discussed.

scholarly journals Research on the Output Characteristics of Emerging Market Entities Serving the Development of New Energy

2021 ◽  
Vol 329 ◽  
pp. 01075
Author(s):  
Shuo Yin ◽  
Xing Chen ◽  
Zhe Chai ◽  
Yao Lu ◽  
Danni Zhang
Keyword(s):  
Electric Vehicles ◽  
Consumption Rate ◽  
Emerging Market ◽  
Power Market ◽  
Market Reform ◽  
Distributed Energy ◽  
Healthy Development ◽  
New Energy ◽  
Output Characteristics ◽  
Energy Consumption Rate

The power emerging market entities connected to the grid in a decentralized manner can increase the local new energy consumption rate. Under the background of accelerating the development of new energy, it is urgent to clarify the output characteristics of emerging market entities such as wind power, photovoltaics, energy storage, and electric vehicles to adapt to the development of distributed energy and the progress of power market reform. Major emerging entities contribute to mathematical analysis, promote the transaction design of emerging entities, and promote the rapid and healthy development of new energy.

scholarly journals A Real-Time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

2020 ◽  
Vol 69 (5) ◽  
pp. 4935-4946 ◽  
Author(s):  
Ningyuan Guo ◽  
Basilio Lenzo ◽  
Xudong Zhang ◽  
Yuan Zou ◽  
Ruiqing Zhai ◽  
...  
Keyword(s):  
Real Time ◽  
Electric Vehicles ◽  
Nonlinear Model ◽  
Model Predictive Controller ◽  
Motion Optimization ◽  
Predictive Controller

Energy After COVID

2020 ◽  
Vol 119 (820) ◽  
pp. 317-322
Author(s):  
Michael T. Klare
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
White Collar ◽  
Peak Oil ◽  
Business Travel ◽  
Reduce Energy Consumption ◽  
The Cost ◽  
Energy Companies

By transforming patterns of travel and work around the world, the COVID-19 pandemic is accelerating the transition to renewable energy and the decline of fossil fuels. Lockdowns brought car commuting and plane travel to a near halt, and the mass experiment in which white-collar employees have been working from home may permanently reduce energy consumption for business travel. Renewable energy and electric vehicles were already gaining market share before the pandemic. Under pressure from investors, major energy companies have started writing off fossil fuel reserves as stranded assets that are no longer worth the cost of extracting. These shifts may indicate that “peak oil demand” has arrived earlier than expected.

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