Torque control strategy incorporating charge torque and optimization for fuel consumption and emissions reduction in parallel hybrid electric vehicles

Kitayama, Satoshi; Saikyo, Marina; Nishio, Yu; Tsutsumi, Keisuke

doi:10.1007/s00158-015-1394-x

Cited by 12 publications

(6 citation statements)

References 14 publications

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“…But problems such as the limited driving range, life span and charging of the battery seriously affect the development of EVs. Considering the limits of the battery technology, hybrid electric vehicles (HEVs) are still the important measure in the transportation field (Kitayama et al, 2017; Zhang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Model predictive control-based control strategy to reduce driving-mode switching times for parallel hybrid electric vehicle

Song

2020

Transactions of the Institute of Measurement and Control

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Multi-power sources are included in hybrid electrical vehicles, which leads to multi-driving modes co-existing when driving the vehicle. However, the frequent driving mode switching (DMS) will probably need the engine to be started frequently, which can result in extra fuel consumption. So, avoiding unnecessary DMS should be fully considered when designing the control strategy. For solving this problem, a model predictive control (MPC) strategy integrating Markov chain driving intention identification is put forward. First, the component models of the powertrain system are established. Second, according to the real driving cycle data, a driving intention model based on the Markov chain is designed according to the real driving cycle data. Then the MPC-based control strategy aiming at reducing DMS times is proposed by integrating the cost of DMS. Finally, the proposed control strategy is contrasted with three other control strategies to verify its validity in reducing the mode switching times and improving the fuel economy.

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Section: Introductionmentioning

confidence: 99%

Model predictive control-based control strategy to reduce driving-mode switching times for parallel hybrid electric vehicle

Song

2020

Transactions of the Institute of Measurement and Control

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“…For passing rate and energy saving, Kitayama et al used the radial basis function network (RBF) to optimize the variables in the torque control strategy of the internal combustion engine to improve the fuel economy for a parallel HEV [18,19]. Lin et al proposed a framework of an adaptive control strategy for a series-parallel hybrid electric bus (SPHEB), based on the extracted hierarchical energy management strategy from dynamic programming (DP) and combined with driving pattern recognition (DPR) to improve the fuel economy [20].…”

Section: Introductionmentioning

confidence: 99%

“…Fu et al proposed a parameter matching optimization method for hybrid electric vehicles based on multi-objective optimization (MOO), which used the weight coefficient method to transform the multi-objective optimization problem of fuel consumption and emissions into a single objective optimization problem to reduce significantly the fuel consumption and emissions of a vehicle simultaneously [23]. In [18][19][20][21][22][23], different energy management strategies were proposed for different hybrid vehicles, and the reduction of fuel consumption and emissions was verified by simulation analysis. The fusion algorithm of fleet control and energy saving control should also be considered.…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy Design and Simulation Validation of Hybrid Electric Vehicle Driving in an Intelligent Fleet

Lai

Huo

2019

Electronics

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This paper proposes a combination method of longitudinal control and fuel management for an intelligent Hybrid Electric Vehicle (HEV) fleet. This method can reduce the fuel consumption while maintaining the distance and speed for each vehicle in the fleet. An HEV system efficiency model was established to simulate the impact of different working modes. Based on the principle of optimal vehicle system efficiency, the energy management control strategy of HEV was designed. Then, the driver model of the piloting vehicle and the following vehicle was built by using an intelligent fuzzy control method. Finally, the intelligent fleet model and energy matching model of HEV were integrated with the simulation platform that was developed based on MATLAB/Simulink/Stateflow. The validity of the energy matching strategy of HEV under the principle of optimal system efficiency was verified by simulation results, and the purpose of improving the driving safety, traffic efficiency, and fuel economy of the fleet was achieved. Comparing with the conventional control strategy, the proposed method saved 7.79% of fuel for the HEV fleet. Meanwhile, the distance ranges between the vehicles were from 12 meters to 15 meters, which improved the driving safety, passing rate, and fuel economy.

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“…There is a significant body of prior research on SOC optimization based on MPC. In , MPC is employed to resolve the energy management problem of a Plug‐in Hybrid Electric Bus (PHEB), and the battery SOC is restrained to fluctuate near a reference trajectory to ensure the global performance of MPC. A method to preplan the trajectory of SOC is proposed in .…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Model Predictive Control for Parallel Hybrid Electrical Vehicles

Song

et al. 2018

Asian Journal of Control

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One purpose of the control strategy for a parallel hybrid electric vehicle (PHEV) is to control the state of charge (SOC) of the battery to achieve a maximum powertrain efficiency. Because of the nonlinearity of the powertrain, the control strategy should implement nonlinear optimization in real time. This paper presents a new hierarchical optimal control design to execute real-time optimization on the basis of a model predictive control concept. The proposed control architecture suggests a two-layer control subjected to the related constraints according to the changing rate of different parameters. The prime controller optimizes the best trajectory of the state of charge according to the load detected by a load prediction module, while the second controller will apply the output signal from the prime controller along with the signal from the prediction module and the driver inputs to control the powertrain. Additionally, a new methodology to predict the future load imposed on the wheels is introduced. Through simulation and experiment results, it is shown that the proposed prediction control can effectively reduce fuel consumption and exhaust emission.

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Torque control strategy incorporating charge torque and optimization for fuel consumption and emissions reduction in parallel hybrid electric vehicles

Cited by 12 publications

References 14 publications

Model predictive control-based control strategy to reduce driving-mode switching times for parallel hybrid electric vehicle

Model predictive control-based control strategy to reduce driving-mode switching times for parallel hybrid electric vehicle

Energy Management Strategy Design and Simulation Validation of Hybrid Electric Vehicle Driving in an Intelligent Fleet

Hierarchical Model Predictive Control for Parallel Hybrid Electrical Vehicles

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