## 3 Energy Management Strategies for HEVs
### Optimization-Based Control Strategy
#### 3.2.2 Real-Time Optimization
This approach aims to enhance the energy utilization of PHEVs while minimizing the extent of battery life deterioration. By doing so, they addressed the challenge of efficiently managing PHEV energy usage while preserving battery longevity under varying battery performance and dynamic driving scenarios. These studies highlight the importance of ECMS in optimizing EMSs for HEVs. (b) Model Predictive Control  
MPC is an efficient energy management method that utilizes system identification to develop a dynamic process model. It optimizes the current time slot by also accounting for future time slots [68]. MPC reformulates the overarching global optimal control challenge into a localized optimization problem within a prediction horizon. It achieves rolling optimization by identifying optimal vehicle dynamics parameters online, which then facilitates the updating of the predicted vehicle operating state or control parameters for the subsequent time domain. Thus, the application of the proposed method yields optimization results that demonstrate robustness and suitability for controlling uncertain and nonlinear dynamic systems, particularly in the context of energy management for HEVs. By predicting future events and taking corresponding control measures, MPC can significantly improve fuel economy and optimize vehicle performance. The MPC formulation flowchart is shown in Figure 10.  
[PERSON] et al. (2020) [168] introduced a multi-objective predictive control model framework specifically tailored for HEVs during car-following scenarios. This framework aims to optimize fuel efficiency, minimize emissions, and simultaneously guarantee the safety between vehicles. Similarly, [PERSON] et al. (2020) [169] presented a real-time MPC methodology, which leverages lookahead prediction techniques to forecast the future trajectories of preceding vehicles, thereby minimizing the economic expenditure associated with HEVs. In the realm of battery management systems (BMS), [PERSON] et al. (2020) [170] introduced an FPGA-based hardware accelerator for physics-based Extended Kalman Filter to address the computational complexity of physics-based models coupled with MPC. [PERSON] et al. (2020) [171] introduced a robust hierarchical control approach for HEV energy management under uncertain conditions. This strategy employs a pseudo-spectral optimal control method at the upper level, coupled with a tube-based MPC controller at the lower level, to ensure effective energy management in the presence of uncertainties. [PERSON] et al. (2020) [108] explored the application of nonlinear MPC for heavy-duty HEVs using a random power prediction method, demonstrating its effectiveness through hardware-in-the-loop simulations. Furthermore, [PERSON] et al. (2021) [172] formulated a two-tiered real-time optimization control methodology for the concurrent management of battery thermal  
Figure 10: MPC formulation flow diagram [167]. regulation and heating, ventilation, and air conditioning systems in interconnected and autonomous HEVs, aiming to balance the trade-off between energy conservation potential and control span. [PERSON] et al. (2022) [173] presented a sophisticated driver assistance system tailored for HEVs. This system integrates adaptive cruise control and energy management functionalities, leveraging both switched MPC and neuro-fuzzy systems to enhance driving assistance and optimize energy utilization. [PERSON] et al. (2022) [174] offered a comprehensive overview of the merits and viable application scenarios of diverse prediction and solution methodologies within EMSs that utilize MPC for HEVs. They emphasized and examined the specific implementations as well as the practical efficacy of these MPC-driven EMSs across various systems. [PERSON] et al. (2023) [175] devised a two-tiered control approach encompassing the development of an SOC reference planning model and an MPC-based real-time power allocation model.