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Iterative Dynamic Programming Based Model Predictive Control of Energy Efficient Cruising for Electric Vehicle with Terrain Preview
Technical Paper
2020-01-0132
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
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.
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Ju, 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
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References
- 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 10.1109/TVT.2018.2844368