Electrified vehicles are considered a promising technology for energy savings and emission reductions. Both powertrain design (configuration design and component sizing) and energy management strategies (EMSs) for electrified vehicles have been studied extensively. However, powertrain design and energy management need to be examined holistically and optimized simultaneously, from a mechatronic viewpoint, for maximizing the potential of electrified powertrains. This article provides a comprehensive, critical review of the current state, and prospects of electrified powertrain design and energy management. The research status in both powertrain design and energy management development is reviewed and discussed. First, the modeling techniques for rapid configuration design are thoroughly reviewed and summarized. Then, the optimization methods for component sizing are elucidated. Next, the classical EMSs are categorized, and several near-optimal strategies used for powertrain design are elaborated. Finally, the current challenges and future trends of electrified powertrain design and control are discussed, which provides a useful reference to researchers in this area.

Plug-in hybrid electric vehicles (PHEVs) with large battery packs have significant advantages in improving fuel efficiency and lowering harmful emissions. However, battery charging and discharging performance degrades dramatically at low temperatures, resulting in increasing vehicle operating expenses, which hinders the deployment of PHEVs in severe cold regions. To address this challenge, this paper proposes an energy management strategy (EMS) that combines a battery preheating strategy to preheat the battery to a battery-friendly temperature before vehicle operation. This study provides three specific contributions. First, a high-precision electro-thermal-aging coupled model for a wide temperature range is developed, considering the effect of temperature on the battery’s available capacity. Second, the grid-and battery-powered preheating strategies are established using a flexible polyimide heating film to preheat the batteries. Finally, the particle swarm optimization (PSO) algorithm is utilized to determine the preheating time, while Pontryagin’s minimum principle (PMP) is employed to solve the multi-objective energy management problem. The efficacy of the proposed method in low-temperature driving scenarios is validated, and the link between preheating needs, cost savings, driving mileage, and changes in the price of energy carriers is also explored. Simulation results indicate that at a $-$ 20 $^{\circ}$ C ambient temperature, grid-and battery-powered preheating solutions could cut energy usage by 48.30% and 44.89%, respectively, compared to the non-preheating option.

Integration of energy management strategies (EMSs) with intelligent transportation systems exhibits a promising way to use the energy-saving potential of hybrid electric vehicles (HEVs). Battery, engine, cabin, and their heating and cooling systems are examples of dynamic thermal loads that typically exist during HEV operation. These loads are variable and highly influenced by ambient temperature and driving conditions. The primary objective of this paper is to develop a decentralized health-consciousness learning-based integrated thermal and energy management (ITEM) system that, besides minimizing fuel consumption, considers driver comfort and battery lifetime in the context of speed and temperature preview. In this regard, a multi-agent deep reinforcement learning (MADRL) framework with long short-term memory (LSTM) is proposed to consider the distinct dynamic characteristics of the ITEM system as well as capture the influence of past observations. A high-fidelity power-split HEV model established by Autonomie is utilized to implement and analyze the performance of the proposed approach. Based on the results, the multi-agent method outperforms both the rule-based and single-agent strategies. The MADRL agent can reduce battery degradation by 48% compared to rule-based while keeping the cabin temperature in the comfort zone. Furthermore, the proposed multi-agent strategy is implemented in a hardware-in-the-loop (HIL) system to evaluate its efficacy in real-world scenarios. Our experimental results demonstrate that the battery temperature and cabin temperature curves closely match the simulation results, with a maximum absolute error of 0.45 and 0.85, respectively.