This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Personalized Eco-Driving for Intelligent Electric Vehicles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published August 07, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
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.
CitationSun, B., Deng, W., He, R., Wu, J. et al., "Personalized Eco-Driving for Intelligent Electric Vehicles," SAE Technical Paper 2018-01-1625, 2018, https://doi.org/10.4271/2018-01-1625.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- Sarlioglu, Bulent et al., “Driving toward Accessibility: A Review of Technological Improvements for Electric Machines, Power Electronics, and Batteries for Electric and Hybrid Vehicles,” IEEE Industry Applications Magazine 23.1:14-25, 2017.
- Lulhe, A.M. and Date, T.N., “A Technology Review Paper for Drives Used in Electrical Vehicle (EV) & Hybrid Electrical Vehicles (HEV)," 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2015. IEEE.
- Xiao, L. and Gao, F., “A Comprehensive Review of the Development of Adaptive Cruise Control Systems,” Vehicle System Dynamics 48(10):1167-1192, 2010.
- Wang, J. et al., “A Framework of Vehicle Trajectory Replanning in Lane Exchanging with Considerations of Driver Characteristics,” IEEE Transactions on Vehicular Technology 66(5):3583-3596, 2017.
- Meseguer, J.E. et al., “DrivingStyles: A Mobile Platform for Driving Styles and Fuel Consumption Characterization,” Journal of Communications and Networks 19(2):162-168, 2017.
- Li, K. et al., “Intelligent Environment-Friendly Vehicles: Concept and Case Studies,” IEEE Transactions on Intelligent Transportation Systems 13(1):318-328, 2012.
- Brackstone, M. and McDonald, M., “Car-Following: A Historical Review,” Transportation Research Part F: Traffic Psychology and Behaviour 2(4):181-196, 1999.
- Wang, W. and Xi, J., “A Rapid Pattern-Recognition Method for Driving Styles Using Clustering-Based Support Vector Machines,” American Control Conference (ACC), 2016, 2016. IEEE.
- Brombacher, P. et al., “Driving Event Detection and Driving Style Classification Using Artificial Neural Networks,” 2017 IEEE International Conference on Industrial Technology (ICIT), 2017. IEEE.
- Takano, W. et al., “Recognition of Human Driving Behaviors Based on Stochastic Symbolization of Time Series Signal,” Intelligent Robots and Systems, IEEE/RSJ International Conference on IROS 2008, 2008. IEEE.
- Nishiwaki, Y. et al., “Generation of Pedal Operation Patterns of Individual Drivers in Car-Following for Personalized Cruise Control,” 2007 IEEE Intelligent Vehicles Symposium, 2007. IEEE.
- Alrifaee, B., Jodar, J.G., and Abel, D., “Predictive Cruise Control for Energy Saving in REEV Using V2I Information,” 2015 23th Mediterranean Conference on Control and Automation (MED), 2015. IEEE.
- Ganji, B. et al., “Adaptive Cruise Control of a HEV Using Sliding Mode Control,” Expert Systems with Applications 41(2):607-615, 2014.
- Liang, C. et al., “Research on Adaptive Cruise Control Strategy for Electric Vehicle Based on Optimization of Regenerative Braking,” Journal of Zhejiang University: Engineering Science Edition 51(8):1596-1602, 2017.
- Luo, L., Chen, J., and Zhang, F., “Integrated Adaptive Cruise Control Design Considering the Optimization of Switching between Throttle and Brake,” 2016 IEEE Intelligent Vehicles Symposium (IV), 2016. IEEE.
- Abdullah, R. et al., “Autonomous Intelligent Cruise Control Using a Novel Multiple-Controller Framework Incorporating Fuzzy-Logic-Based Switching and Tuning,” Neurocomputing 71(13):2727-2741, 2008.
- Hammerstein, A., “Nichtlineare integralgleichungen nebst anwendungen,” Acta mathematica 54(1):117-176, 1930.
- Mutoh, N. and Akashi, H., “Electric and Mechanical Brake Cooperative Control Method for FRID EVs under Various Severe Road Conditions,” IECon 2011-37th Annual Conference on IEEE Industrial Electronics Society, 2011. IEEE.
- United Nations Economic Commission for Europe, Transport Division, “On Uniform Provisions Concerning the Approval of Vehicles of Categories M, N and O with Regard to Braking,” 2003.
- Martinez, J.J. and Canudas-De-Wit, C., “A Safe Longitudinal Control for Adaptive Cruise Control and Stop-and-Go Scenarios,” IEEE Transactions on Control Systems Technology 15(2):246-258, 2007.
- Liang, C. et al., “Research on Braking Energy Regeneration Evaluation and Test Method of Pure Electric Vehicle,” Journal of Huazhong University of Science and Technology: Natural Science Edition 42(1):18-22, 2014.
- Sun, B., et al., “Research on the Classification and Identification of Driver’s Driving Style,” 2017 10th International Symposium on Computational Intelligence and Design (ISCID), 2017.