## 1 Introduction  
Hybridized vehicles are considered nowadays as an efficient technological solution to reduce gas emissions and non-renewable energy consumption. Plug-in hybrid vehicles are among the most studied alternatives to traditional vehicles as they allow the transfer of at least a part of the needed energy from fossil fuel to electricity. A plug-in hybrid electric vehicle (PHEV) is meanwhile a complex system as it combines two different energy sources: an internal combustion engine (ICE) with one or multiple electric motors (EM) powered by a rechargeable battery with important capacity providing zero gas emission (ZE) and an important all-electric-range (AER). The battery can be recharged via an external electrical outlet or powered either by the electric motor working as a generator during the deceleration and regenerative braking phase or by the internal combustion engine through a generator. During its use, the PHEV can operate on three modes generally depending on the state-of-charge (SoC) behavior which are:  
* Pure electric vehicle mode (EV), only the battery supplies the needed energy to the vehicle; the ICE is off. * Blended mode (BL): The ICE is on but supplies moderate power to allow the battery Soc depleting. * Charge-sustaining mode (CS): The ICE is the main energy source; the battery Soc is maintained at a given level. These three modes are presented in Figure 1[1]. Although the extensive use of EV mode results in simpler control and enhances electric energy consumption, studies and simulation results highlight the importance of blended mode in achieving energy savings in PHEV [2; 3; 4]. To ensure good battery management and optimal control, energy management strategy (EMS) algorithms are introduced to perform better power split between the two different sources of energy. They can be grouped in the literature into two categories: rule-based and optimization-based controllers. Rule-based algorithms consist of the implementation of a set of rules established from the PHEV operating modes (Figure 1) [1] while having expert knowledge of the system. While they are easy to implement, their establishment without prior knowledge of the road cycle does not present optimal results. A comparative study was introduced by [5; 6] showing its limits for PHEV. In [1], an application of a rule-based algorithm was introduced for parallel PHEV extended from the RL-based strategy for HEV. A parametric study was conducted pointing out the RL-based limitations and the variation effect of its dependent variables (SoC (state of charge) of the battery, LFS (Load Following Strategy) parameters) on fuel usage and initial trip battery level. The results show the combined importance of the selected phases of the PHEV (CD-CS), the battery Soc initial level, and the cycle driven in the efficiency of increasing the vehicle's electric range and fuel economy. On the other hand, optimization-based strategies are designed to achieve optimal control between the two energy sources via the minimization of a defined cost function in accordance with the required performances and the available parameters of the vehicle components. The optimization methods are grouped into two families. The first consists of global optimization controllers requiring a priori knowledge of the driving cycle (instantaneous speed profile), and a huge calculation capacity. The second uses real-time optimization controllers that rely on the real drive conditions without any prior cycle knowledge [2]. Several optimization strategies have been developed in the literature. The main algorithms are dynamic programming (DP) [7], equivalent fuel consumption minimization (ECMS) [8], simulated annealing (SA), genetic algorithms (GA), neural networks (NN) [9; 10], slip mode control (SMC) [5], and model predictive control (MPC) [11; 12]. [PERSON] in [13] uses PSO (Particle Swarm Optimization) algorithm to optimize rule-based control parameters for power-split Plug-in Hybrid Electric Vehicles.