1. Paper title: Evolutionary algorithm based on-line PHEV energy management system with self-adaptive SOC control ------------------------------------------------------------------------------- 2. Author contact details: Xuewei Qi Department of Electrical and Computer Engineering, University of California-Riverside, Riverside, CA, 92507, USA xqi001@ucr.edu Matthew J. Barth Department of Electrical and Computer Engineering, University of California-Riverside, Riverside, CA, 92507, USA barth@ece.ucr.edu Guoyuan Wu Center of Environmental Research and Technology University of California-Riverside, Riverside, CA, 92507, USA gywu@cert.ucr.edu Kanok Boriboonsomsin Center of Environmental Research and Technology University of California-Riverside, Riverside, CA, 92507, USA kanok@cert.ucr.edu ------------------------------------------------------------------------------- 3. Corresponding author: Xuewei Qi xqi001@ucr.edu ------------------------------------------------------------------------------- 4. Paper abstract: In recent years, transportation-related energy,consumption and air quality degradation problems have gained an increasingly amount of public concern. Recently,there has been significant interest in plug-in hybrid electric vehicles (PHEVs) as an effective way to decrease the dependence on fossil fuel and to reduce emissions of greenhouse gases (GHG) in USA. The energy management system (EMS) is crucial to a plug-in hybrid electric vehicle (PHEV) in reducing its fuel consumption and pollutant emissions. The EMS determines how energy flows in a hybrid powertrain should be managed in response to a variety of driving conditions. In the development of EMS, the battery state-of- charge (SOC) control strategy plays a critical role. This paper proposes a novel evolutionary algorithm (EA)-based EMS with self-adaptive SOC control strategy for PHEVs, which can achieve the optimal fuel efficiency without trip length (by time) information. Numerical studies show that this proposed system can save up to 13% fuel, compared to other on-line EMS with different SOC control strategies. Further analysis indicates that the proposed system is less sensitive to the errors in predicting propulsion power in real-time, which is favorable for on-line implementation. ------------------------------------------------------------------------------- 5. Relevant criteria: A,E,G ------------------------------------------------------------------------------- 6. Statement: (A) In this work, we designed, for the first time, a EA-based real-time energy management system controller with the following innovative features (1) cost effective real-time performance, (2) a self-adaptive SOC control strategy that does not require any previewed trip information, and (3) high robustness to the propulsion power demand prediction. And this EA-based EMS controller has been filed in the UCR disclosure and record of invention form and are in the process of patent application currently. (E) The proposed method has been extnesivly evaluated and compared with the currently most commercialized model (binary control model) in a real-world commute trip driving. The results show that the proposed model can outperform the binary control model by saving additional 13% fuels on the same driving trip. (G) The power-split between engine power and vehicle battery power in a powertrain of plugin hybrid electric vehicle is very complicated due to its high complexity of powertrain model and the high dimensionality of search space. The proposed EA based EMS significantly reduce the computational cost to implement the real-time optimization. In addition, the proposed model does not require too much trip information (e.g., trip length or duration) which is always required by other existing state-of-the-art methods. This significantly enhance its applicability in real-world driving. ----------------------------------------------------------------------------- 7. Citation: X. Qi, G. Wu, K. Boriboonsomsin and M. J. Barth, "Evolutionary algorithm based on-line PHEV energy management system with self-adaptive SOC control" 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, 2015, pp. 425-430. ------------------------------------------------------------------------------- 8. Any prize money, if any, is to be divided equally among the co-authors. ------------------------------------------------------------------------------- 9. "Best" statement: We believe the problems solved in this paper is virtually an impossible challenge for a human. And this is very good example for applying evolutionary algorithm to real-world engineering problem that involves high-dimensional real-time optimization. ------------------------------------------------------------------------------- 10. Method used: Esltimation Distribution Algorithms (EDA).