基于粒子群算法的电动汽车DCT升档动力协调控制

该文建立了纯电动汽车双离合器自动变速器(DCT,double clutch transmission)升档过程动力学模型,以滑摩功与冲击度为评价因素,建立两者加权和形式的评价指标。针对纯电动汽车动力协调换挡优化对象强非线性,在基于梯度寻优的传统优化算法难以奏效情况下,利用粒子群优化算法对电机参与下的双离合器升档过程进行优化。优化过程中,使用傅里叶基向量分解方法将离合器转矩轨线、电机转矩轨线分解为基向量的线性组合,使用粒子群优化算法优化基向量系数,实现对双离合器传递转矩轨线与电机输出转矩轨线的规划,藉此提出了电动汽车双离合器变速器升档过程中离合器和电机转矩协调控制方法。最后,经过实车试验进行验证,结果表明该方法能够有效减少9%的滑摩功,使冲击度控制在国家标准之内,并缩短换档时间,改善换档品质。

Coordinated control of upshift power of double clutch transmission for electric vehicle based on particle swarm optimization

A dynamic model of double clutch transmission during upshift process was established in this paper. The friction work and shock degree were used to establish the evaluating indicator in the form of weighted sum. Considering that the optimizing object of the electric vehicle integrated powertrain shifting control has strong nonlinearity, and that traditional optimizing methods based on gradient algorithm can hardly acquire good result, a particle swarm optimization method to optimize the upshift processing of double clutch transmission was used, in which motor was involved. In the optimizing process, Fourier base vector decomposition method was used to decompose the clutch torque trace and the motor torque trace to linear combination of base vectors. The particle swarm optimization algorithm was used to optimize these base vectors coefficients in order to scheme out the torque traces of DCT and electric motor output. And then an integrated control method of motor and double clutch transmission was proposed. With the vehicle test result, the new control method reduced the friction work by 9%, and kept shock degree within a reasonable range which satisfied the national standard. The optimized gear shifting time was also reduced and the quality of gear shifting was improved.