Special issue on on-board optimization strategy designmethods for connected hybrid electric vehicles

Editorial: Special issue on on-board optimization strategy designmethods for connected hybrid electric vehicles

摘要:

Abstract:

As a trend in the innovation of automotive engineering, connectivity provides new opportunities and challenging issues for vehicular powertrain control due to big potential in the use of the connected information for improving energy efficiency and reducing CO2 emission. In real world driving situation, a bottleneck for achieving optimal energy efficiency via control of the power sources in the powertrain is the uncertainty in power demand, since the power demand is delivered by the driver according to the driving environment which is always with stochasticity and un-detectable event in the environment. The connectivity enables us to predict the power demand in advance by using the real-time information of vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-cloud etc., this new configuration for hybrid powertrain control excites innovative research for optimization of connected vehicles. This special issue focuses on the latest development, trends, and novel techniques for the design of on-board optimization strategies for hybrid electric vehicles (HEVs) under the connected environment. Especially, a special section is included that corrects several papers on new challenging solutions of the 6th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling (IFAC ECOSM 2021) Benchmark Competition, where a real-time energy management problem with vehicle-toeverything (V2X) information is targeted for a typical HEV. There are eleven papers collected in this special issue.The collections are divided into three groups. The first group, including seven papers, focuses on the solution of benchmark challenging problem of IFAC ECOSM 2021. F. Xu et al., proposing this benchmark problem of developing real-time optimization algorithm for HEVs in V2X communication environment, describe the detail information of benchmark challenging problem, including the simulator, the challenging issue and the evaluating standard. It is concluded that the solutions from the challengers are hierarchical optimization architectures. In the paper from X. Jiao et al., the conditional linear Gaussian is employed to predict future preceding vehicle’s speed and a DP-based energy management strategy is designed. B. Zhang et al. propose a rule-based controller to maintain safe driving and the Gaussian process regression is used to predict preceding vehicle speed, which is used for the calculation of ego demand torque in the HEV powertrain control. J. Gao et al. also develop a layered optimization framework, where ECMS is used for the torque distribution control ofHEVpowertrain. The solution fromY.Yamasaki et al. is designing a rule-based controller to determine ego vehicle’s speed and proposing the equivalent fuel consumptions in motors for HEV powertrain control. In the proposal from S. Dong et al., model predictive control (MPC) is used for speed planning and an explicit solution is obtained for HEV powertrain control. T. Namerikawa et al. develop a hierarchical MPC framework for the NOx emission reduction and fuel economy improvement....