**1. Introduction**

Climate change and the sustainable development of energy are the most serious international issues in the 21st century. The hybrid electric vehicle (HEV) is one of the key technologies for vehicle energy saving. Combining with the internal combustion engine (ICE) and high efficiency electric motor, hybrid electric vehicles have better fuel economy than traditional vehicles. The HEVs are also more achievable than electric vehicles (EVs) under current limitations of battery.

With ICE and electric motors, the operating modes to control engine and motors are necessary designs for the hybrid power system, and the switching between the modes would change its power flow and the operation of the components. A planetary gear set (PGS) is often applied to the configuration of HEV. The most representative design is Toyota Prius released in 1997. In 2005, the Advanced Hybrid System-II (AHS-II), also known as the two-mode hybrid system, was developed by General Motors. AHS-II offers an additional set of electric-continue-variable-transmission (eCVT) mode of operation, and significantly reduce the energy loss in high speed [1]. Arata et al. [2] analyzed two different power-split hybrid-electric vehicle (HEV) powertrains using backward-looking simulations, and compared the Toyota Hybrid System II (THS-II) and the General Motors Allison Hybrid System II (GM AHSII).

Fuel economy and lower pollution emissions are critical issues. Vehicle manufactures are investing in energy management strategy (EMS) to ensure that the system components operate in their safe working range, and, at the same time, to maintain a high energy conversion efficiency to obtain better fuel economy and lower pollution emissions. The EMS can be divided into two categories, rule-based control strategy (RB) and optimization control strategy. Each of these two categories can be further divided into two subcategories. RB is subdivided into fuzzy control and heuristic control, while the optimization strategy is subdivided into global optimization and real-time or online optimization control [3,4]. RB does not require lengthy numerical calculation time [5,6], and can simultaneously monitor a number of parameters, which are usually associated with engine on/off control, and engine and motor operating points [7,8]. However, the fuel consumption is not optimized. For optimization control strategy, the most representative strategy for global optimization is dynamic programming (DP). However, the algorithm requires the information of full driving time, it is difficult to apply for a real vehicle control [9,10]. Chen et al. [11] utilized online control rules but based on offline optimization results of DP for a plug-in HEV to prolong driving range up to 2.86% and reduce the energy consumption up to 5.77%. For real-time optimized control strategy, Equivalent Consumption Minimization Strategy (ECMS) [12] aims at optimal power distribution (between engines and motors) and ensures that the battery pack has sufficient power. Compared to RB, ECMS can have a better fuel economy [13]. Zeng et al. [14] proposed an adaptive simplified-ECMS-based strategy along with particle swarm optimization (PSO) algorithm to optimize PHEV system. The method effectively shortened the calculation time and improved fuel consumption by 16.43%, compared to the Charge Sustaining-Charge Depleting (CS-CD)-based strategy. Dong [15] developed a real-time optimal energy management approach for HEVs and PHEVs using an adaptive coefficient tuning strategy, and validated results using both Model-in-Loop (MIL) and HIL environment. Lu et al. [16] introduced the weighted sum method and no-preference method to solve the multiobjective optimization problem of plug-in electric vehicles and validated with ADVISOR software. Xu et al. [17] developed a fuzzy control strategy for parallel hybrid electric vehicle. The control strategy was adjusted with GA. It was verified that GA could effectively improve the efficiency of the engine and fuel consumption.

This study implemented the AHS-II two-mode system as the transmission structure to establish a Simulink vehicle model, and applied ECMS for EMS to achieve optimal fuel economy.
