Machine Learning Based Hydrogen Electrolyzer Control Strategy for Solar Power Output and Battery State of Charge Regulation

2021 ◽  
Author(s):  
Miswar Akhtar Syed ◽  
Muhammad Khalid
Keyword(s):  
Machine Learning ◽  
Control Strategy ◽  
Power Output ◽  
Solar Power ◽  
State Of Charge ◽  
Charge Regulation ◽  
Battery State Of Charge

scholarly journals Research on braking energy recovery strategy of electric vehicle based on ECE regulation and I curve

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987776 ◽  
Author(s):  
Shengqin Li ◽  
Bo Yu ◽  
Xinyuan Feng
Keyword(s):  
Electric Vehicle ◽  
Control Strategy ◽  
Energy Recovery ◽  
State Of Charge ◽  
Regenerative Braking ◽  
Force Distribution ◽  
Battery State Of Charge ◽  
Braking Force ◽  
Braking Control ◽  
Braking Energy Recovery

Electric vehicles can convert the kinetic energy of the vehicle into electric energy for recycling. A reasonable braking force distribution strategy is the key to ensure braking stability and the energy recovery rate. For an electric vehicle, based on the ECE regulation curve and ideal braking force distribution (I curve), the braking force distribution strategy of the front and rear axles is designed to study the braking energy recovery control strategy. The fuzzy control method is adopted while the charging power limit of the battery is considered to correct the regenerative braking torque of the motor, the ratio of the regenerative braking force of the motor to the front axle braking force is designed according to different braking strengths, then the braking force distribution and braking energy recovery control strategies for regenerative braking and friction braking are developed. The simulation model of combined vehicle and energy recovery control strategy is established by Simulink and Cruise software. The braking energy recovery control strategy of this article is verified under different braking conditions and New European Driving Cycle conditions. The results show that the control strategy proposed in this article meets the requirements of braking stability. Under the condition of initial state of charge of 75%, the variation of state of charge of braking control strategy in this article is reduced by 8.22%, and the state of charge of braking strategy based on I curve reduces by 9.12%. The braking force distribution curves of the front and rear axle are in line with the braking characteristics, can effectively recover the braking energy, and improve the battery state of charge. Taking the using range of 95%–5% of battery state of charge as calculation target, the cruising range of vehicle with braking control strategy of this article increases to 136.64 km, which showed that the braking control strategy in this article could increase the cruising range of the electric vehicle.

Fuzzy control strategy of pure electric vehicle based on driving intention recognition

2020 ◽  
Vol 39 (4) ◽  
pp. 5131-5139
Author(s):  
Wanqiang Qi
Keyword(s):  
Fuzzy Control ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Driving Force ◽  
State Of Charge ◽  
Control Technology ◽  
Electronic Control ◽  
Work Condition ◽  
Actual Output ◽  
Battery State Of Charge

The main reason that currently hinders the commercialization of electric vehicles is a bottleneck in battery, motor and electronic control technology, however, an In-depth study of electronic control technology is one of the most effective means to break through this bottleneck at present. The purpose of this paper is to solve the problem that the pure electric vehicle is difficult to meet the driver’s acceleration intention in the urban road cycle acceleration work condition and the brake energy recovery process does not consider the battery state of charge during the deceleration work condition. Proposed a control strategy that can meet the requirements of road cycle conditions and driver’s driving intentions and take account of the vehicle operating status. Use a fuzzy control algorithm to develop a fuzzy controller that taking the motor demand speed change rate and battery state of charge as input, the motor demand torque compensation coefficient as output. The experimental results show that the modified control strategy can improve the actual output power, the actual output torque of the motor and actual driving force of the wheel under the premise of maintaining economy; it also improved the acceleration performance and climbing performance of pure electric vehicles, and can recycle braking energy efficiently. The experimental results show that the secondary development control strategy can meet the requirements of the cycle work condition CYC_ECE_EUDC for the speed and driving force and the driver’s driving intention under the premise of not sacrificing economics.

scholarly journals Lithium-Ion Battery State-of-Charge Estimator Based on FBG-Based Strain Sensor and Employing Machine Learning

2021 ◽  
Vol 21 (2) ◽  
pp. 1453-1460
Author(s):  
Bruno Rente ◽  
Matthias Fabian ◽  
Miodrag Vidakovic ◽  
Xuan Liu ◽  
Xiang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lithium Ion Battery ◽  
Strain Sensor ◽  
Lithium Ion ◽  
State Of Charge ◽  
Battery State Of Charge

scholarly journals Simple and Effective Algorithm for Battery Assisted Quasi Z-Source Inverter for Standalone PV-Pumping Systems

2020 ◽  
Vol 9 (7) ◽  
pp. 1309-1314
Keyword(s):  
Energy Storage ◽  
Control Strategy ◽  
Storage System ◽  
Energy Storage System ◽  
State Of Charge ◽  
Duty Ratio ◽  
Effective Algorithm ◽  
Solar Panels ◽  
Switching Strategy ◽  
Battery State Of Charge

A QZSI with an energy storage system is developed for standalone applications. A controller based on the battery-assisted Quasi Z-Source Inverter model is designed to achieve both MPPT from the solar panels and to control the battery State of Charge (SOC). The control strategy will control both MPPT and Battery SOC through the shoot-through duty ratio (D) and Modulation index of the inverter (M). The simple boost modulation technique is adopted for the inverter switching strategy. The performance of the designed system is verified using simulation.

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