Energy recovery strategy for regenerative braking system of intelligent four-wheel independent drive electric vehicles

Regenerative braking system can recovery energy in various electric vehicles. Considering large computation load of global optimization methods, most researches adopt instantaneous or local algorithms to optimize the recuperation energy, and incline to study straight deceleration processes. However, uncertain drivers' intentions limit the potential exploration of economy improvement, and simple test conditions do not reflect the complexity of actual driving cycles. Herein, an innovative control architecture is designed for intelligent vehicles to overcome these challenges to some extent. Compared with traditional vehicles, driverless ones would eliminate drivers' interferences, and have more freedoms to optimize energy recovery, route tracking and dynamics stability. Specifically, a series regenerative braking system is designed, and then a three-level control architecture is first proposed to coordinate three performances. In the top layer, some rules with maximum recuperation energy is exploited off-line for optimising the velocity and control commands on-line. In the middle layer, local algorithm is used to track the commands and complex routes for optimal energy from a global perspective. In the bottom layer, hydraulic and regenerative toques are allocated. Tests are conducted to demonstrate the effectiveness of the design and control schemes.

Automated summary

Regenerative braking system can recover energy in electric vehicles, but traditional optimization methods may not be sufficient to meet complex driving demands. Driverless vehicles can better optimize energy recovery, route tracking, and dynamics stability, thus improving efficiency.