Journal
IEEE-ASME TRANSACTIONS ON MECHATRONICS
Volume 26, Issue 3, Pages 1271-1282Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2021.3070330
Keywords
Fuels; Hybrid electric vehicles; Coolants; Heat engines; Batteries; Vehicle dynamics; Real-time systems; Connected and automated vehicle (CAV); hierarchical model predictive control (HMPC); hybrid electric vehicle (HEV); integrated power and thermal management (i-PTM)
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Funding
- National Nature Science Foundation of China [61903152, U1864201, 61773009]
- Jilin Provincial Science Foundation of China [20200201162JC, JJKH20200986KJ, 20190302105GX, 20200201285JC]
- Funds for Joint Project of Jilin Province
- Jilin University [SXGJSF2017-2-1-1]
- Exploration Foundation of State Key Laboratory of Automotive Simulation and Control
- Interdisciplinary Integration and Innovation Project of JLU [JLUXKJC2020202]
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This article focuses on developing a real-time control framework for integrated power and thermal management of a connected hybrid electric vehicle, utilizing vehicle speed preview and dynamic programming to optimize fuel consumption. The two-layer hierarchical model predictive controller provides near-optimal solutions with reduced computational burden.
Cabin heating requirement and engine efficiency degradation in cold weather lead to considerable increase in fuel consumption of hybrid electric vehicles (HEVs). This article focuses on the development of a real-time control framework for integrated power and thermal management (i-PTM) of a connected HEV through incorporating vehicle speed preview informed by traffic connectivity. To evaluate the ceiling of the fuel economy improvement via i-PTM, forward dynamic programming (FDP) is adopted as a benchmark study to find the global optimization solution and then implemented into a centralized model predictive controller (CMPC). Regarding the slow response associated with the coupled electric-thermal dynamics in the HEV, a two-layer hierarchical MPC (HMPC) framework is developed, which exploits vehicle speed preview predictions over short and long prediction horizons to optimize fuel consumption while satisfying power and cabin heating demand. The simulation results show that the proposed HMPC provides near-optimal solutions in reference to the benchmark while reduces up to 79% computation burden compared with CMPC. Additionally, compared to a baseline power management (PM) controller, up to 5.46% improvement on fuel economy can be achieved by HMPC for congested driving scenarios. Finally, the real-time feasibility of the HMPC is assessed in a dSPACE rapid prototyping system.
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