This paper presents a comprehensive analysis of hybrid electric vehicle (HEV) classification and energy management strategies (EMS), with a particular emphasis on the application and potential of genetic algorithms (GAs) in optimizing energy management strategies for hybrid electric vehicles. Initially, the paper categorizes hybrid electric vehicles based on mixing rates and power source configurations, elucidating the operational principles and the range of applicability for different hybrid electric vehicle types. Following this, the two primary categories of energy management strategies—rule-based and optimization-based—are introduced, emphasizing their significance in enhancing energy efficiency and performance, while also acknowledging their inherent limitations. Furthermore, the advantages of utilizing genetic algorithms in optimizing energy management systems for hybrid vehicles are underscored. As a global optimization technique, genetic algorithms are capable of effectively addressing complex multi-objective problems by circumventing local optima and identifying the global optimal solution. The adaptability and versatility of genetic algorithms allow them to conduct real-time optimization across diverse driving conditions. Genetic algorithms play a pivotal role in hybrid vehicle energy management and exhibit a promising future. When combined with other optimization techniques, genetic algorithms can augment the optimization potential for tackling complex tasks. Nonetheless, the advancement of this technique is confronted with challenges such as cost, battery longevity, and charging infrastructure, which significantly influence its widespread adoption and application.

Nowadays, there is an increasing focus on enhancing the economy of hybrid electric vehicles (HEVs). This study builds a framework model for the parameter optimization of hybrid powertrains in user driving cycles. Unlike the optimization under standard driving cycles, the applied user driving cycle incarnates realistic driving situations, and the optimization results are more realistic. Firstly, the user driving cycle with high accuracy is constructed based on actual driving data, which provides a basis for the performance analysis of HEV. Secondly, the HEV model with good power and economy is constructed under user driving cycles. Finally, a multi-strategy improved whale optimization algorithm (MIWOA) is proposed, which can guarantee better economy of HEV compared with the original whale optimization algorithm (WOA). The economy optimization of HEV is completed by MIWOA under user driving cycles, and the hybrid vehicle economy parameters that are more in line with the user’s actual driving conditions are obtained. After optimization, the 100 km equivalent fuel consumption (EFC) of HEV is reduced by 5.20%, which effectively improves the vehicle’s economy. This study demonstrates the effectiveness of the MIWOA method in improving economy and contributes a fresh thought and method for the economic optimization of the hybrid powertrain.

Flash boiling atomization has been considered a potential technique to enhance state-of-the-art atomization performance. It is usually believed that the phase change during flash boiling atomization can improve the disintegration of the bulk liquid. Many investigations have indicated that flash boiling atomization acts on the secondary breakup of the liquid droplets via micro-explosion mechanisms. However, recent investigations indicated that vapor bubbles inside the nozzle could directly affect the primary atomization of the bulk liquid. Therefore, this investigation focuses on the phase change process inside the nozzle via high-speed microscopic measurements. A two-dimensional slit nozzle with an expansion chamber was used in two dimensions to approximate the nozzle and the near field. Rim structures were observed in the measurements, and the transition from vapor bubbles to liquid rims was presented and analyzed.