Lee Wah Ching, Tsang Kim Fung, Chi Hao Ran, Hung Faan Hei, Wu Chung Kit, Chui Kwok Tai, Lau Wing Hong, Leung Yat Wah
Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China.
Sensors (Basel). 2015 Jan 12;15(1):1245-51. doi: 10.3390/s150101245.
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day.
已开发出一种用于插电式混合动力汽车（PHEV）的高燃油效率管理方案。为了实现燃油消耗的降低，设计了一种自适应遗传算法方案来对能源使用进行自适应管理。遗传算法的目标函数通过设计一个模糊逻辑控制器来实现，该控制器密切监测并模拟插电式混合动力汽车的驾驶条件和环境，从而在汽油和电力之间进行权衡以实现最佳驾驶效率。计算结果与公布数据之间的比较表明，模糊遗传算法所实现的效率比现有方案高出10%。所开发的方案若能得到充分采用，将有助于全球每天减少超过600吨的二氧化碳排放。
