Abstract
The multisource electromechanical coupling renders the energy management of plug-in hybrid electric vehicles (PHEVs) highly nonlinear and complex. Furthermore, the complicated nonlinear management process highly depends on knowledge of driving conditions and hinders the control strategies efficiently applied instantaneously, leading to massive challenges in energy-saving improvement of PHEVs. To address these issues, a novel learning-based model predictive control (LMPC) strategy is developed for a serial–parallel PHEV with the reinforced optimal control effect in real-time applications. Rather than employing the velocity-prediction-based MPC methods favored in the literature, an original reference-tracking-based MPC solution is proposed with strong instant application capacity. To guarantee the optimal control effect, an online learning process is implemented in MPC via the Gaussian process (GP) model to address the uncertainties during state estimation. The tracking reference in the LMPC-based control problem in PHEV is achieved by a microscopic traffic flow analysis (MTFA) method. The simulation results validate that the proposed method can optimally manage energy flow within vehicle power sources in real time, highlighting its anticipated preferable performance.
Highlights
N OWADAYS, plug-in hybrid electric vehicle (PHEVs) have boosted to be top-ranking solutions in auto industry to promote energy consumption economy and mitigate global warming concern [1, 2]
microscopic traffic flow analysis (MTFA) proposed in this paper has considered much impact from environment and particular driver behaviors, making it rather suitable for model predictive control (MPC) based control in PHEV
The results show that the ordinary MPC without correction in inner state prediction leads to more constraint violation than that by learning based model predictive control (LMPC), which is mainly caused by the discretization error inherited from the inflexible state model
Summary
N OWADAYS, plug-in hybrid electric vehicle (PHEVs) have boosted to be top-ranking solutions in auto industry to promote energy consumption economy and mitigate global warming concern [1, 2]. In the referencetracking based MPC, the complex control process in short horizons (e.g., the same or close to sampling time in engineering practice) can be achieved rapidly by efficient solvers with the target of simultaneously minimizing certain optimization targets and mitigating the difference between current state and reference [18]. The LMPC, with super capacity in reference-tracking based adaptive control, is applied to accomplish the energy flow management in the vehicle, raising efficient optimal solution in real time via minimizing the tracking gap and energy consumption.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Transportation Electrification
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
