Xiong Jinfeng, Song Jingbin, Zhang Zhiqiang
College of Transportation Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, China.
PLoS One. 2025 Mar 28;20(3):e0320537. doi: 10.1371/journal.pone.0320537. eCollection 2025.
Braking energy recovery is crucial for improving the energy efficiency and extending the range of electric vehicles. If a large amount of braking energy is wasted, it will lead to problems such as reduced range and increased battery burden for electric vehicles. Therefore, an electric vehicle braking energy recovery control model that integrates fuzzy control algorithm with genetic firefly algorithm is proposed. Experimental analysis showed that the decrease in the state of charge of the model was 12.44%, and the braking energy recovery rate reached 52.1% in practical applications. Based on the above data, the proposed method can effectively control the amount of energy recovery. In addition, when the system chip value was 10%, the total amount of recovered energy at the battery end was the highest. Conversely, the total amount of recovered energy at the battery end was relatively small. In summary, the designed electric vehicle braking energy recovery control model can effectively control the amount of braking energy recovery of electric vehicles, ensuring the maximum recovery while also considering the durability and driving stability of the vehicle battery. The method can effectively extend mileage range in the electric vehicle industry, promoting the development and technological innovation of the new energy industry.
制动能量回收对于提高电动汽车的能源效率和延长续航里程至关重要。如果大量制动能量被浪费,将导致电动汽车出现续航里程降低和电池负担增加等问题。因此,提出了一种将模糊控制算法与遗传萤火虫算法相结合的电动汽车制动能量回收控制模型。实验分析表明,在实际应用中该模型的荷电状态下降了12.44%,制动能量回收率达到了52.1%。基于上述数据,所提方法能够有效控制能量回收量。此外,当系统芯片值为10%时,电池端的回收能量总量最高。相反,电池端的回收能量总量相对较小。综上所述,所设计的电动汽车制动能量回收控制模型能够有效控制电动汽车的制动能量回收量,在确保最大回收量的同时还考虑了车辆电池的耐用性和行驶稳定性。该方法能够有效延长电动汽车行业的续航里程,推动新能源行业的发展和技术创新。