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| Special issue on on-board optimization strategy designmethods for connected hybrid electric vehicles |
| TielongShen1,CarlosGuardiola2,FuguoXu3 |
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| (1 Sophia University, Tokyo, Japan;2 Universitat Politècncia de València, Valencia, Spain;3 Tokyo City University, Tokyo, Japan) |
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| DOI：https://doi.org/10.1007/s11768-022-00093-z |
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| Editorial: Special issue on on-board optimization strategy designmethods for connected hybrid electric vehicles |
| Tielong Shen1,Carlos Guardiola2,Fuguo Xu3 |
| (1 Sophia University, Tokyo, Japan;2 Universitat Politècncia de València, Valencia, Spain;3 Tokyo City University, Tokyo, Japan) |
| 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.... |
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