Co-Optimization Scheme for Hybrid Electric Vehicles Powertrain and Exhaust Emission Control System Using Future Speed Prediction

Journal Article · Mon Jan 04 23:00:00 EST 2021 · IEEE Transactions on Intelligent Vehicles

DOI:https://doi.org/10.1109/tiv.2021.3049296· OSTI ID:1756274

Hong, Wanshi ^[1]; Chakraborty, Indrasis ^[2]; Wang, Hong ^[3]; Gang, Tao ^[4]

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1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
4. Univ. of Virginia, Charlottesville, VA (United States)

Hybrid electric vehicles (HEVs) have been an effective solution for improved vehicle fuel efficiency and reduced emission pollution. In this paper, a co-optimization scheme is proposed to optimize fuel efficiency for HEVs. Here, the proposed optimization scheme uses obtainable future speed prediction as the basis to optimally tune control parameters for the existing powertrain control system. The ramp-up time of the catalyst temperature to reach its light-off level in the exhaust emission system is also considered as an additional optimization constraint to reduce emission. The Toyota Prius Hybrid Simulink model which is an integrated model for a powertrain and exhaust emission system is validated using real data from several real driving cycle scenarios. Then, to simplify the formulation of the proposed algorithm, the model for the optimization for powertrain and exhaust emission systems is represented by a set of equivalent neural network (NN) models learned using the data generated from the well-validated Toyota Prius Hybrid Simulink model. Using NN models, a co-optimization algorithm is established that provides an optimal tuning of some fuel-sensitive powertrain control parameters using future speed prediction, leading to a novel co-optimization algorithm, achieving on average a further 9.22% fuel savings for the Toyota Prius Hybrid Simulink model.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Advanced Research Projects Agency - Energy (ARPA-E)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1756274
• Journal Information: IEEE Transactions on Intelligent Vehicles, Journal Name: IEEE Transactions on Intelligent Vehicles Journal Issue: 1 Vol. 6; ISSN 2379-8858
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

33 ADVANCED PROPULSION SYSTEMS
Hybrid Electric Vehicle
Neural Network
Parameter Optimization