## 1 Introduction  
The need for the reduction in our carbon footprint has coincided with upward fuel prices, which has prompted an increased focus in the search for energy-saving solutions related to the transportation sector. Hence, unleashing the maximum potential of hybrid and electric vehicles at different fleet and standalone energy management design levels is at the forefront of research in the automotive industry [1; 2; 3]. The energy management strategy (EMS) unit is the heart of power sharing control toward addressing the consumption-friendly objectives of electric vehicle design. Compared to their conventional counterparts, hybrid electric vehicles (HEVs) can offer lower fuel consumption and still provide a similar/enhanced performance. However, as HEVs usually comprise two or more energy supply sources, developing their architectures with an efficient EMS involves a plethora of complexities related to design, integration, and implementation. Due to these cost-effective complexities, the plausibility of any control strategy requires assessment through various testing and validation procedures before embedment in the electronic control unit (ECU) of an on-road vehicle [4]. In this regard, the response and accuracy of a control strategy is of great significance for extensive verification through the use of a complete chain of tools. Toward real-world applicability, the model-based design of a plant and its EMS include various steps from the development to validation phases. This generally starts with functional system definitions and ends with deploying the design to a real operating environment under offline system modeling, control algorithm synthesis, simulation analysis, and online vehicle implementations [5]. To this end, first, a model-in-the-loop (MiL) development must be performed in which the integrated plant and the corresponding EMS controller models are built and set up in a desktop environment (i.e., MATLAB/Simulink(r)) to be examined over a complete driving cycle. This considers a totally software-based and iterative trial and error approach toward achieving an initial design with good flexibility and low cost in a short period [6]. In this phase, it is possible to perform fast EMS design alternations to investigate corresponding effects on a set of predefined objectives. Thereafter, through software-in-the-loop (SiL) evaluations, one can test the plant and controller models in a slightly more realistic environment as the corresponding C/C++ code can be generated for digital implementation tests. The possibility of design modification in this stage is slower time-wise as there is a need to go back, modify the initial model, and regenerate the C/C++ code if required. This step is of great importance to test the feasibility of the built code in terms of real-time evaluation. In this regard, convertibility of the control algorithms to C/C++ code assures that they are downloadable into digital signal processing (DSP) boards (e.g., dSPACE rapid prototyping product, \"MiroLab Box\") for processor-in-the-loop (PiL) tests as a next phase. The PiL phase considers hardware features and provides realistic situations by running the control algorithm for an emulated target behavior [7]. Hence, these steps can not only reveal coding failures, but also provide helpful insights for the hardware-in-the-loop (HiL) implementation where the vehicle behavior can be reproduced. In the HiL phase, the developed control algorithms with mathematical models running in real-time can be fully installed on a control hardware to communicate and actuate real/emulated components. A typical HiL system integrates various software and hardware-based units such as physical components (not in all testbeds), component emulators, DSP/FPGA unit, MATLAB/Simulink(r) desktop environment, real-time interface (RTI), real-time workshop (RTW), and controller area network (CAN) [8].