Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles

Wang, Ximing; He, Hongwen; Sun, Fuchun; Zhang, Jieli

doi:10.3390/en8043225

Cited by 149 publications

(72 citation statements)

References 22 publications

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“…When the battery SOC is higher than 80%, the PHEV is not allowable to implement regenerative braking. When implementing the MPC and DP strategy, the SOC ∆ and T ∆ are set to be 0.5% and 5 Nm, respectively [6]. The prediction horizon length for MPC is set to be 10s.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Model Predictive Control Based Energy Management Strategy for a Plug-In Hybrid Electric Vehicle

Zhang¹,

He²,

Wang³

2015

Proceedings of the 3rd International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2015)

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Keywords: model predictive control (MPC), plug-in hybrid electric vehicles (PHEVs), energy management strategy.Abstract. In this paper, the model predictive control (MPC) method is researched for energy management problem of plug-In hybrid electric vehicle (PHEV). Multi-step Markov prediction method is selected for the prediction. Dynamic programming (DP) is chosen to solve the optimization problem within the prediction horizon. Through the comparison of MPC result with the results of dynamic programming strategy and a rule-based strategy, it is certified that the control effect of MPC strategy is much better than the ruled-based strategy and close to the global optimal control under DP strategy.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Model Predictive Control Based Energy Management Strategy for a Plug-In Hybrid Electric Vehicle

Zhang¹,

He²,

Wang³

2015

Proceedings of the 3rd International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2015)

Self Cite

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“…Dynamic programming (DP) was applied for the optimization of vehicle fuel economy simulation [4,5]. The constraints applied on DP were P ୣ୫,୫୧୬ ሺkሻ P ୣ୫ ሺkሻ P ୣ୫,୫ୟ୶ ሺkሻ (3) P ୣ୬,୫୧୬ ሺkሻ P ୣ୬ ሺkሻ P ୣ୬,୫ୟ୶ ሺkሻ (4) SOC ୫୧୬ ሺkሻ SOCሺkሻ SOC ୫ୟ୶ ሺkሻ (5) Where P em,min and P em,max were motor minimum power and maximum power, respectively.…”

Section: Energy Management Optimization Dynamic Programming In Hybridmentioning

confidence: 99%

Energy Management Optimization of Power Split Hybrid Electric Vehicle

Chen¹,

Hwang²

2016

dtcse

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Abstract. The object of this thesis is to improve the fuel economy of a power-split hybrid driveline system with energy management. General Motors two-mode hybrid system is modeled and simulated in Matlab, and the power outputs of engine and electric motors are selected as the design targets. A rule-based controller is applied in the baseline for energy management. The optimization of fuel consumption is approached by Dynamic Programming. Then, the differences between baseline and DP are evaluated, and a modified rule-based control strategy is developed. The combined fuel economies of baseline and DP optimization are 42.06 and 55.61mpg, respectively. The potential improvement in fuel economy is 32.21%. With the modified rule-based control strategy, the fuel economy is improved 13.36%, up to 47.68mpg. The result shows that the modified control strategy improves fuel economy efficiently.

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“…One of the SMCs adjusts the engine throttle to maintain the engine speed at a certain level, while the other performs engine/generator torque control via the power converter, so that the engine works in the optimal efficiency region, despite load variations. For more details about different approaches in improving fuel efficiency and emissions of vehicles with APU, see [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

Shen

Liu

et al. 2016

Energies

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Auxiliary power units (APUs) are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP) firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC) emissions, carbon monoxide (CO) emissions and nitric oxide (NO x ) emissions. Then, the multi-objective particle swarm optimization (MOPSO) algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC), Federal test procedure (FTP) and high way fuel economy test (HWFET) driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.

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Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles

Cited by 149 publications

References 22 publications

Model Predictive Control Based Energy Management Strategy for a Plug-In Hybrid Electric Vehicle

Model Predictive Control Based Energy Management Strategy for a Plug-In Hybrid Electric Vehicle

Energy Management Optimization of Power Split Hybrid Electric Vehicle

Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

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