Dual-pump Control Algorithm of Two-speed Powershift Transmissions in Electric Vehicles

2021 ◽  
Author(s):  
Yanfang Liu ◽  
Lifeng Chen ◽  
Tianyuan Cai ◽  
Wenbo Sun ◽  
Xiangyang Xu ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Control Algorithm

A novel coordinated control algorithm for distributed driving electric vehicles

2017 ◽  
Vol 50 (4-6) ◽  
pp. 405-421
Author(s):  
Shixin SONG ◽  
Wanchen SUN ◽  
Feng XIAO ◽  
Silun PENG ◽  
Jingyu AN ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Control Algorithm ◽  
Coordinated Control

scholarly journals An Optimal Control Algorithm with Reduced DC-Bus Current Fluctuation for Multiple Charging Modes of Electric Vehicle Charging Station

2021 ◽  
Vol 12 (3) ◽  
pp. 107
Author(s):  
Tao Chen ◽  
Peng Fu ◽  
Xiaojiao Chen ◽  
Sheng Dou ◽  
Liansheng Huang ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Control Algorithm ◽  
Current Fluctuation ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Three Phase ◽  
Optimization Control ◽  
Dc Bus ◽  
Vehicle Charging

This paper presents a systematic structure and a control strategy for the electric vehicle charging station. The system uses a three-phase three-level neutral point clamped (NPC) rectifier to drive multiple three-phase three-level NPC converters to provide electric energy for electric vehicles. This topology can realize the single-phase AC mode, three-phase AC mode, and DC mode by adding some switches to meet different charging requirements. In the case of multiple electric vehicles charging simultaneously, a system optimization control algorithm is adopted to minimize DC-bus current fluctuation by analyzing and reconstructing the DC-bus current in various charging modes. This algorithm uses the genetic algorithm (ga) as the core of computing and reduces the number of change parameter variables within a limited range. The DC-bus current fluctuation is still minimal. The charging station system structure and the proposed system-level optimization control algorithm can improve the DC-side current stability through model calculation and simulation verification.

scholarly journals QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2304 ◽  
Author(s):  
Mingfu Li ◽  
Guan-Yi Li ◽  
Hou-Ren Chen ◽  
Cheng-Wei Jiang
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Smart Home ◽  
Control Algorithm ◽  
Renewable Energy Sources ◽  
Peak Load ◽  
Electricity Bill ◽  
Home Energy Management ◽  
Smart Appliance ◽  
User Comfort

To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE.

scholarly journals A Robust Control Method for Lateral Stability Control of In-Wheel Motored Electric Vehicle Based on Sideslip Angle Observer

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yaxiong Wang ◽  
Feng Kang ◽  
Taipeng Wang ◽  
Hongbin Ren
Keyword(s):  
Robust Control ◽  
Electric Vehicles ◽  
Control Algorithm ◽  
Control Method ◽  
Stability Control ◽  
Lateral Stability ◽  
Level Control ◽  
Sideslip Angle ◽  
Safety Control ◽  
Yaw Moment

In-wheel motored powertrain on electric vehicles has more potential in maneuverability and active safety control. This paper investigates the longitudinal and lateral integrated control through the active front steering and yaw moment control systems considering the saturation characteristics of tire forces. To obtain the vehicle sideslip angle of mass center, the virtual lateral tire force sensors are designed based on the unscented Kalman filtering (UKF). And the sideslip angle is estimated by using the dynamics-based approaches. Moreover, based on the estimated vehicle state information, an upper level control system by using robust control theory is proposed to specify a desired yaw moment and correction front steering angle to work on the electric vehicles. The robustness of proposed algorithm is also analyzed. The wheel torques are distributed optimally by the wheel torque distribution control algorithm. Numerical simulation is carried out in Matlab/Simulink-Carsim cosimulation environment to demonstrate the effectiveness of the designed robust control algorithm for lateral stability control of in-wheel motored vehicle.

scholarly journals A Novel Charging and Discharging Algorithm of Plug-in Hybrid Electric Vehicles Considering Vehicle-to-Grid and Photovoltaic Generation

2019 ◽  
Vol 10 (4) ◽  
pp. 61
Author(s):  
Ahmed Aljanad ◽  
Azah Mohamed ◽  
Tamer Khatib ◽  
Afida Ayob ◽  
Hussain Shareef
Keyword(s):  
Electric Vehicles ◽  
Control Algorithm ◽  
Peak Load ◽  
Distribution Networks ◽  
Management Control ◽  
Photovoltaic Generation ◽  
Technical Problems ◽  
Vehicle To Grid ◽  
Fast Charging ◽  
The Impact

Considering, the high penetration of plug-in electric vehicles (PHEVs), the charging and discharging of PHEVs may lead to technical problems on electricity distribution networks. Therefore, the management of PHEV charging and discharging needs to be addressed to coordinate the time of PHEVs so as to be charged or discharged. This paper presents a management control method called the charging and discharging control algorithm (CDCA) to determine when and which of the PHEVs can be activated to consume power from the grid or supply power back to grid through the vehicle-to-grid technology. The proposed control algorithm considers fast charging scenario and photovoltaic generation during peak load to mitigate the impact of the vehicles. One of the important parameters considered in the CDCA is the PHEV battery state of charge (SOC). To predict the PHEV battery SOC, a particle swarm optimization-based artificial neural network is developed. Results show that the proposed CDCA gives better performance as compared to the uncoordinated charging method of vehicles in terms of maintaining the bus voltage profile during fast charging.

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