Personalized Eco-Driving for Intelligent Electric Vehicles 2018-01-1625
Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed. Finally, MyEco-ACC is constructed and optimized via theory of Nonlinear Model Prediction Control (NMPC). Regenerated energy is taken as the indicator for energy consumption and the key parameter in driving style model is taken as the comfort indicator. Samples with 80 drivers obtained from the field test with both RT3000 family and RT-Range are used for analysis and further employed for the identification of driving style model. A co-simulation environment consisting of Carsim2016.1-RT ® and Mathwork Simulink® is established to verify the proposed personalized eco-driving strategy. Test results show that driving styles can be identified effectively and the driving style model has a high fidelity. Furthermore, simulation results show that the root mean square of ego vehicle acceleration aw,0.49Hz based on MyEco-ACC are close to those of the human drivers. The values of energy recycling efficiency based on MyEco-ACC range from 35.9% to 37.6% and close to that based on Eco-ACC but apparently higher than that based on ACC in the same simulation conditions.