Rapid assessment of the fuel economy capability of parallel and series-parallel hybrid electric vehicles

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.