Efficiently solving the off-line control problem represents a crucial step to predict the fuel economy capability of hybrid electric vehicles (HEVs). Optimal HEV control approaches implemented in literature usually prove to be either computationally inefficient or sub-optimal. Moreover, they often neglect drivability and comfort associated to the generated control actions over time. This paper therefore aims at introducing a rapid near-optimal approach to solve the off-line control problem for parallel and series-parallel HEV powertrains while accounting for drivability criteria such as the frequency of gear shifts and the number of activations of the thermal engine. The performance of the introduced slope-weighted energy-based rapid control analysis (SERCA) algorithm is compared with the global optimal benchmark provided by dynamic programming (DP) for both the parallel and the series-parallel HEV layouts over different driving missions. Results demonstrate how the SERCA algorithm can produce comparable control results with respect to DP by limiting the increase in the estimated fuel consumption within 2.2%. The corresponding computational time can be simultaneously reduced by around 99.5% while ensuring a limited number of gear shifts and engine activations over time. Engineers could therefore potentially implement the proposed SERCA algorithm in design and calibration procedures of parallel and series-parallel HEVs to accelerate the overall vehicle development process.

有效解决离线控制问题代表了预测混合动力汽车（HEV）燃油经济性的关键步骤。文献中实施的最佳HEV控制方法通常被证明在计算上效率低下或次优。而且，随着时间的推移，它们经常忽略与所产生的控制动作相关的驾驶性能和舒适性。因此，本文旨在介绍一种快速的接近最优的方法，以解决并联和串联-并联混合动力汽车动力总成的离线控制问题，同时考虑诸如变速频率和热机启动次数之类的可驾驶性标准。将引入的基于坡度加权的基于能量的快速控制分析（SERCA）算法的性能与动态编程（DP）为不同驾驶任务上的并联和串联-并联混合动力汽车布局提供的全局最佳基准进行比较。结果表明，SERCA算法如何通过将估计的燃油消耗增加限制在2.2％以内，从而产生与DP相当的控制结果。相应的计算时间可以同时减少约99.5％，同时确保随时间推移有限的换档和发动机启动次数。因此，工程师可以在并联和串联-并联混合动力汽车的设计和校准程序中潜在地实施建议的SERCA算法，以加快整个车辆的开发过程。