Optimization and control of battery-flywheel compound energy storage system during an electric vehicle braking

Combining the advantages of battery's high specific energy and flywheel system's high specific power, synthetically considering the effects of non-linear time-varying factors such as battery's state of charge (SOC), open circuit voltage (OCV) and heat loss as well as flywheel's rotating speed and its motor characteristic, the mathematical models of a battery-flywheel compound energy storage system are established. Taking the recovered braking energy of the system as an objective, an energy optimization method based on GA is proposed to obtain the optimal electric braking torque and current distribution factor under different working conditions, which realizes the current distribution between the battery and the flywheel as well as the allocation between the mechanical braking torque and the electric braking torque. Simultaneously, a double neural networks-based adaptive PI vector control method is proposed to regulate the rotating speed of the flywheel motor. Research results demonstrate that using the proposed methods the overall recovered energy increases by 1.17times and the maximum charging current of the battery decreases by 42.27% compared with a single battery system, and the stability and robustness of the flywheel system are significantly improved, which provides theoretical and technical references for making the energy management plan of electric vehicles. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

By combining the advantages of battery and flywheel systems, mathematical models for a battery-flywheel compound energy storage system were established. An energy optimization method based on GA and a double neural networks-based adaptive PI vector control method were proposed to optimize energy recovery, current distribution, and flywheel motor speed regulation, improving overall energy efficiency and system stability.