A novel coordinated control algorithm for distributed driving electric vehicles

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.

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µ-Synthesis Robust Torque Coordinated Control of Two-Wheel Driving Electric Vehicles

2009 Second International Conference on Intelligent Computation Technology and Automation ◽

10.1109/icicta.2009.681 ◽

2009 ◽

Author(s):

Xu Gang ◽

Xu Peng ◽

Guo Guifang ◽

Cao Binggang

Keyword(s):

Electric Vehicles ◽

Coordinated Control

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

Automotive Innovation ◽

10.1007/s42154-021-00160-2 ◽

2021 ◽

Author(s):

Yanfang Liu ◽

Lifeng Chen ◽

Tianyuan Cai ◽

Wenbo Sun ◽

Xiangyang Xu ◽

...

Keyword(s):

Electric Vehicles ◽

Control Algorithm

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

Energies ◽

10.3390/en11092304 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2304 ◽

Cited By ~ 6

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.

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Pressure Coordinated Control System of Regenerative Braking Based on Hardware of ABS for HEV

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.3406 ◽

2011 ◽

Vol 121-126 ◽

pp. 3406-3410 ◽

Cited By ~ 1

Author(s):

Yang Yang ◽

Da Tong Qin ◽

Jin Li

Keyword(s):

Control System ◽

Simulation Model ◽

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Coordinated Control ◽

Regenerative Braking ◽

Design And Optimization ◽

Braking System ◽

Dynamic Performances ◽

Simulation Results

A new kind of pressure coordinated control system suite of regenerative braking system for hybrid electric vehicles (HEV) is proposed in this paper on the basis of appropriate transformation on traditional hydraulic braking system with ABS. AMEsim modular simulation platform is used to build a simulation model of the system. Dynamic performances of the key components and system are simulated and analyzed. And the simulation results show the effectiveness and feasibility of the pressure coordinated control system, which lays the foundation of the design and optimization for the regenerative braking system.

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Coordinated Stability Control Strategy for Intelligent Electric Vehicles Using Vague Set Theory

Mathematical Problems in Engineering ◽

10.1155/2020/6793821 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Yilin He ◽

Jian Ma ◽

Xuan Zhao ◽

Ruoyang Song ◽

Xiaodong Liu ◽

...

Keyword(s):

Electric Vehicles ◽

Control Strategy ◽

Coordinated Control ◽

Stability Control ◽

Lateral Acceleration ◽

Vague Set ◽

Yaw Rate ◽

Stability Performance ◽

Control Target ◽

Simulation Results

Aiming at improving the tracking stability performance for intelligent electric vehicles, a novel stability coordinated control strategy based on preview characteristics is proposed in this paper. Firstly, the traditional stability control target is introduced with the two degrees of freedom model, which is realized by the sliding mode control strategy. Secondly, an auxiliary control target further amending the former one with the innovation formulation of the preview characteristics is established. At last, a multiple purpose Vague set leverages the contribution of the traditional target and the auxiliary preview target in various vehicle states. The proposed coordinated control strategy is analyzed on the MATLAB/CarSim simulation platform and verified on an intelligent electric vehicle established with A&D5435 rapid prototyping experiment platform. Simulation and experimental results indicate that the proposed control strategy based on preview characteristics can effectively improve the tracking stability performance of intelligent electric vehicles. In the double lane change simulation, the peak value of sideslip angle, yaw rate, and lateral acceleration of the vehicle is reduced by 13.2%, 11.4%, and 8.9% compared with traditional control strategy. The average deviations between the experimental and simulation results of yaw rate, lateral acceleration, and steering wheel angle are less than 10% at different speeds, which demonstrates the consistency between the experimental and the simulation results.

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Model Predictive Control-Based Coordinated Control Algorithm with a Hybrid Energy Storage System to Smooth Wind Power Fluctuations

Energies ◽

10.3390/en12234591 ◽

2019 ◽

Vol 12 (23) ◽

pp. 4591

Author(s):

Haisheng Hong ◽

Quanyuan Jiang

Keyword(s):

Predictive Control ◽

Power Output ◽

Control Algorithm ◽

Storage System ◽

Coordinated Control ◽

Energy Storage System ◽

Hybrid Energy ◽

Hybrid Energy Storage ◽

Hybrid Energy Storage System ◽

Prediction Horizon

Stochastically fluctuating wind power has an escalating impact on the stability of power grid operations. To smooth out short- and long-term fluctuations, this paper presents a coordinated control algorithm using model predictive control (MPC) to manage a hybrid energy storage system (HESS) consisting of ultra-capacitor (UC) and lithium-ion battery (LB) banks. In the HESS-computing period, the algorithm minimizes HESS operating costs in the subsequent prediction horizon by optimizing the time constant of a flexible first-delay filter (FDF) to obtain the UC power output. In the LB-computing period, the algorithm keeps the optimal time constant of the FDF from the previous period to directly obtain the power output of the UC bank to minimize the power output of the LB bank in the next prediction horizon. A relaxation technique is deployed when the problem is unsolvable. Thus, the fluctuation mitigation requirements are fulfilled with a large probability even in extreme conditions. A state-of-charge (SOC) feedback control strategy is proposed to regulate the SOC of the HESS within its proper range. Case studies and quantitative comparisons demonstrate that the proposed MPC-based algorithm uses a lower power rating and storage capacity than other conventional algorithms to satisfy one-minute and 30-min fluctuation mitigation requirements (FMR).

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