# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 1 Introduction  
To address energy scarcity and environmental pollution, many countries have issued and implemented strict standards of vehicle energy consumption and automotive exhaust emissions. Although new energy vehicles (NEV) can effectively meet these standards, replacing the internal combustion engine (ICE) vehicle with NEV is difficult because of technical issues, such as small battery capacity, long charging time, and high battery cost. The automotive industry is striving to improve the efficiency of the energy management technology for ICE vehicles to ensure better fuel economy and less harmful exhaust emissions [1].  
Today, the online energy management method has become an important research direction to improve vehicle fuel economy. An energy management strategy for plug-in hybrid electric vehicles (PHEV) was proposed by [PERSON] [2], which used [PERSON]'s minimum principle (PMP) to split power. [PERSON] [3] predicted the future power demand of vehicle through a Markov chain model and then calculated power distribution between the supercapacitor and fuel cell through PMP. Similarly, [PERSON] [4] presented an optimal energy management strategy based on an approximate PMP algorithm for PHEVs. An adaptive supervisory controller was proposed by [PERSON] [5] based on PMP for the online energy management optimization of PHEV. For the implementation of an energy management strategy, [PERSON] [6] concluded that PMP computation time is significantly less than that of dynamic programming under the same conditions.  
Although the aforementioned methods can improve fuel economy effectively, they cannot directly extend to ICE vehicles because of distinctively different structures of vehicle electrical systems. The power system of ICE vehicles is composed of an alternator and a battery. An intelligent control strategy of a vehicular alternator was observed in the studies by [PERSON] (2015). The strategy reduced fuel consumption by adjusting the working mode of the alternator. [PERSON] (2015) investigated the methods of battery management that partitions the working states of the battery and monitor. However, previous studies concentrated less on the collaborative work between the alternator and the battery. [PERSON] and [PERSON] (2016) presented a model-based supervisory energy management strategy, respectively. The strategy attempted to control the duty cycle of the alternator to meet the instantaneous demand of load current. However, the strategy seems to be difficult to implement in engineering. For ICE vehicles, a vehicular energy management system based on local interconnection network (LIN) bus was proposed by [PERSON] (2016), in which a double fuzzy controller was designed to recover braking energy. However, the controller only controlled the output of the alternator according to the vehicle speed and the state of the battery charge, and it never considered the variation of the electrical load.  
Based on previous studies, a bus-based real-time energy management system is proposed for the ICE vehicle electrical system, and its real-time energy management strategy based on PMP is presented. The proposed strategy optimally distributes the power demand of electrical load between the intelligent alternator and the battery so that better fuel economy can be obtained.  
The remainder of this paper is organized as follows: Section 2 describes the bus-based power management system, and the vehicular electrical system is modeled through experiments in Section 3; the real-time energy management strategy for the power management system is proposed in Section 4, and Section 5 discusses the experimental results; finally, conclusions are provided in Section 6.