机车油电混合动力系统的优化匹配

Optimal sizing of diesel-electric hybrid powertrain for locomotive

混合动力系统的配置参数将影响机车的全寿命周期成本,该参数是开发混合动力机车的关键。传统的双环优化匹配方法,虽然能够求解出优化的配置参数,但利用动态规划算法作为优化能量管理的内环,导致了优化计算的时间十分漫长。为了解决该问题,本文提出了一种快速的双环优化匹配方法。在外环,粒子群算法以减少全寿命周期成本为目标,优化配置参数。在内环,庞特里亚金极小值原理(Pontryagin’s minimum principle,PMP)以减少列车油耗、提高电池寿命为目标,针对外环优化过程中的每一种配置参数均优化其能量管理,并将优化结果反馈给外环。仿真表明,与传统的双环优化匹配方法相比,文章提出的快速双环优化匹配方法不仅能够获取与其相同的优化配置参数,而且计算速度得到了极大改进。

The sizing parameters of the hybrid powertrain affect the life cycle cost of locomotives,which is critical for the development of hybrid locomotives.Although the traditional bi-loop optimal sizing method can generate the optimal parameters,the dynamic programming algorithms used as the inner-loop to improve energy management strategy results in a long computation time.A fast bi-loop optimal sizing method was proposed in this paper.In the outer loop,the particle swarm algorithm was applied to optimize the sizing parameters,aimed at reducing the life cycle cost.In the inner loop,the Pontryagin’s minimum principle(PMP)was applied to optimize the energy management for each sizing parameter generated in the outer loop,aimed at reducing train fuel consumption and improving battery life.Then the optimal results were fed back to the outer loop.It was shown from simulations that the fast bi-loop optimal sizing method proposed in this paper can not only generate the same optimal sizing parameters as the traditional bi-loop optimal sizing method,but also significantly improve the computation speed.