Currently, the parallel hybrid electric vehicle (PHEV) is the most common type of architecture on the hybrid vehicle market. Therefore, a PHEV can be a solution to reduce emission and fuel consumption. The main challenge in the development of HEVs is the power management between the components that ensure vehicle movement. Energy management is now highly necessary by applying a control strategy (CS) in the vehicle’s traction chain, which directly affects the PHEV emission and fuel economy. The CSs have different performances, namely the control of the different power sources operation mode and the control of the battery state of charge. For this purpose, we propose a fuzzy logic CS to optimize emissions (FLCS-em) for PHEV. To assess this approach, we compare it with the most commonly used and recent EMS, in particular the strategy to optimize fuel use (FLCS-f), the efficiency optimization strategy (FLCS-eff) and the electric assist CS (EACS), in urban and highway driving cycles. The results show that the elaborate FLCS-em, characterized by a limited number of rulers, provide significant advantage than CSs mentioned in terms of the efficiency of PHEV performance and emissions and fuel consumption minimization.

目前，并联式混合动力电动汽车（PHEV）是混合动力汽车市场上最常见的架构类型。因此，PHEV 可以成为减少排放和燃料消耗的解决方案。HEV 开发的主要挑战是确保车辆运动的组件之间的电源管理。现在，通过在车辆的牵引链中应用控制策略 (CS) 来进行能源管理非常必要，这直接影响 PHEV 排放和燃油经济性。CS具有不同的性能，即对不同电源工作模式的控制和对电池充电状态的控制。为此，我们提出了一种模糊逻辑 CS 来优化 PHEV 的排放 (FLCS-em)。为了评估这种方法，我们将其与最常用和最近的 EMS 进行比较，特别是在城市和高速公路驾驶循环中优化燃料使用的策略 (FLCS-f)、效率优化策略 (FLCS-eff) 和电动辅助 CS (EACS)。结果表明，以有限数量的标尺为特征的精心设计的 FLCS-em 在 PHEV 性能和排放效率以及燃料消耗最小化方面比提到的 CS 具有显着优势。