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连静,毕江嫚,李琳辉,等. 丘陵地区对纯电动汽车经济性分析及能耗优化方法研究[J]. 科学技术与工程, 2020, 20(30): 12603-12606.
Lianjing,Bijiangman,et al.Economic Analysis and Energy Consumption Optimization of Electric Vehicle in Hilly Area[J].Science Technology and Engineering,2020,20(30):12603-12606.
丘陵地区对纯电动汽车经济性分析及能耗优化方法研究
Economic Analysis and Energy Consumption Optimization of Electric Vehicle in Hilly Area
投稿时间:2020-03-21  修订日期:2020-08-05
DOI:
中文关键词:  纯电动汽车  坡度  行车经济性  动态规划
英文关键词:electric vehicle  slope  driving economy  Dynamic programming
基金项目:国家重点研发计划项目(2018YFE0105500)
           
作者单位
连静 大连理工大学
毕江嫚 大连理工大学
李琳辉 大连理工大学
周雅夫 大连理工大学
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中文摘要:
      为了分析丘陵地区对纯电动汽车的经济性影响以及如何提高能耗利用率,针对道路坡度,研究了一种基于动态规划优化控制电机转矩的方法,完成了优化能耗的目标。首先构建了纯电动汽车模型,设计了虚拟坡度,应用动态规划算法合理分配电机转矩及车速,并从车速方面分析了坡度对行车经济性的影响;之后,在虚拟坡度下,使用无坡度道路下的最佳经济车速控制仿真车辆,求解虚拟坡度下的汽车能耗,并将此方法计算的能耗与上一步优化方法得出的能耗做对比,证明了所提出的优化算法能够有效地降低能耗;最后,在实际路况上验证了提出的能耗优化算法的合理性。实验结果表明,针对丘陵地区提出的动态规划算法,能够全局优化能耗,有效提高驾驶经济性。
英文摘要:
      In order to analyze the impact of hilly areas on the economy of electric vehicles and how to improve the utilization rate of energy consumption, a method of optimizing motor torque based on dynamic programming was studied for road slope, and the goal of optimizing energy consumption was achieved. In this paper, the electric vehicle model was constructed, the virtual gradient was designed, the motor torque was allocated reasonably by using the dynamic programming algorithm to optimize the vehicle speed, and the influence of slope on the driving economy was analyzed from the aspect of vehicle speed. Then, under the virtual slope, the optimal economic speed control simulation vehicle under the non slope road was used to solve the vehicle energy consumption under the virtual slope, and the energy consumption calculated by this method was compared with the energy consumption obtained by the previous optimization method, which proved that the proposed optimization algorithm can effectively reduce the energy consumption. Finally, the rationality of the proposed energy consumption optimization algorithm was verified in the actual road conditions. Experimental results show that the dynamic programming algorithm proposed for hilly areas can optimize energy consumption globally and effectively improve driving economy.
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