This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Iterative Dynamic Programming Based Model Predictive Control of Energy Efficient Cruising for Electric Vehicle with Terrain Preview
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
As a global optimization method, dynamic programming (DP) can be employed to seek the optimal velocity with minimum energy consumption for EV on given driving cycles. Due to its terrible computational burden, conventional DP is not suitable for real-time implementation especially with higher dimensions. In this paper, we propose an iterative dynamic programming (IDP) approach to reduce computing time firstly. The IDP can obtain the optimal control laws alike the conventional DP by converging the optimal control strategy iteratively and save considerable computing time. Second, the developed IDP and model predictive control (MPC) are combined to establish a real-time cruising controller called IDP-MPC for an EV with terrain preview. In the predictive controller, we use the IDP to solve a constrained finite horizon nonlinear optimization problem. Finally, to assess the performance of the proposed cruising controller, simulation on a realistic urban expressway road terrain is implemented. Energy-saving potential of the IDP-MPC controller is explored by comparing to DP and constant speed (CS) cruising controllers. The comparative study indicates that the IDP-MPC controller can obtain near-optimal energy-saving capacity compared to DP controller.
CitationJu, F., Zhuang, W., Wang, L., and Wang, Q., "Iterative Dynamic Programming Based Model Predictive Control of Energy Efficient Cruising for Electric Vehicle with Terrain Preview," SAE Technical Paper 2020-01-0132, 2020, https://doi.org/10.4271/2020-01-0132.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
|[Unnamed Dataset 3]|
- Ge, X., Gu, H., and Wang, Y. , “The Impact of RoHS on Electric Vehicles in the Chinese Automotive Market,” SAE Technical Paper 2016-01-8124 , 2016, doi:https://doi.org/10.4271/2016-01-8124.
- Ouyang, D., Zhang, Q., and Ou, X. , “Review of Market Surveys on Consumer Behavior of Purchasing and Using Electric Vehicle in China,” Energy Procedia 152:612-617, 2018, doi:https://doi.org/10.1016/j.egypro.2018.09.219.
- Kim, S., Shin, D., Yoon, H., Bae, H. et al. , “Development of Eco-Driving Guide System,” SAE Technical Paper 2011-28-0034 , 2011, doi:https://doi.org/10.4271/2011-28-0034.
- Sato, S., Suzuki, H., Miya, M., and Iida, N. , “Analysis of the Effect of Eco-Driving with Early Shift-Up on Real-World Emission,” SAE Technical Paper 2010-01-2279 , 2010, doi:https://doi.org/10.4271/2010-01-2279.
- Han, J., Sciarretta, A., Leon, O., De, N., and Thibault, L. , “Safe- and Eco-Driving Control for Connected and Automated Electric Vehicles Using Analytical State-Constrained Optimal Solution,” IEEE Trans. Intell. Vehicles 3:163-172, 2018, doi:10.1109/TIV.2018.2804162.
- Schwickart, T., Voos, H., Hadji-Minaglou, J., Darouach, M. et al. , “Design and Simulation of a Real-Time Implementable Energy-Efficient Model-Predictive Cruise Controller for Electric Vehicles,” Journal of the Franklin Institute 352(2):603-625, 2015, doi:https://doi.org/10.1016/j.jfranklin.2014.07.001.
- Rios-Torres, J., Sauras-Perez, P., Alfaro, R., Taiber, J. et al. , “Eco-Driving System for Energy Efficient Driving of an Electric Bus,” SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 8:79-89, 2015, doi:https://doi.org/10.4271/2015-01-0158.
- Sarmento, A., Bianca, G., Leonardo, C., and Leonardo, N. , “The Autonomous Vehicle Challenges for Emergent Market,” SAE Technical Paper 2017-36-0436 , 2017, doi:https://doi.org/10.4271/2017-36-0436.
- Imanishi, Y., Tashiro, N., Iihoshi, Y., and Okada, T. , “Development of Predictive Powertrain State Switching Control for Eco-Saving ACC,” SAE Technical Paper 2017-01-0024 , 2017, doi:https://doi.org/10.4271/2017-01-0024.
- Vahidi, A. and Sciarretta, A. , “Energy Saving Potentials of Connected and Automated Vehicles,” Transportation Research Part C: Emerging Technologies 95:822-843, 2018, doi:https://doi.org/10.1016/j.trc.2018.09.001.
- Xu, S., Li, S., Cheng, B., and Li, K. , “Instantaneous Feedback Control for a Fuel-Prioritized Vehicle Cruising System on Highways with a Varying Slope,” IEEE Transactions on Intelligent Transportation Systems 18(5):1210-1220, 2016, doi:10.1109/TITS.2016.2600641.
- Borhan, H., Vahidi, A., Phillips, A., Kuang, M. et al. , “MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle,” IEEE Transactions on Control Systems Technology 20(3):593-603, 2011.
- Xu, S. and Peng, H. , “Design and Comparison of Fuel-Saving Speed Planning Algorithms for Automated Vehicles,” IEEE Access 6:9070-9080, 2018, doi:10.1109/ACCESS.2018.2805883.
- Fröberg, A., Hellström, E., and Nielsen, L. , “Explicit Fuel Optimal Speed Profiles for Heavy Trucks on a Set of Topographic Road Profiles,” SAE Technical Paper 2006-01-1071 , 2006, doi:https://doi.org/10.4271/2006-01-1071.
- Ju, F., Zhuang, W., Wang, L., and Zhang, Z. , “Optimal Sizing and Adaptive Energy Management of a Novel Four-Wheel-Drive Hybrid Powertrain,” Energy 187:116008, 2019, doi:https://doi.org/10.1016/j.energy.2019.116008.
- Chen, Y., Li, X., Wiet, C., and Wang, J. , “Energy Management and Driving Strategy for In-Wheel Motor Electric Ground Vehicles with Terrain Profile Preview,” IEEE Transactions on Industrial Informatics 10(3):1938-1947, 2013, doi:10.1109/TVT.2013.2287102.
- Chen, Z., Mi, C., Xu, J., Gong, X. et al. , “Energy Management for a Power-Split Plug-in Hybrid Electric Vehicle Based on Dynamic Programming and Neural Networks,” IEEE Transactions on Vehicular Technology 63(4):1567-1580, 2013, doi:10.1109/TVT.2013.2287102.
- Zhu, C., Lu, F., Zhang, H., Sun, J. et al. , “A Real-Time Battery Thermal Management Strategy for Connected and Automated Hybrid Electric Vehicles (CAHEVs) Based on Iterative Dynamic Programming,” IEEE Transactions on Vehicular Technology 67(9):8077-8084, 2018, doi:10.1109/TVT.2018.2844368.