Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real time traffic information prediction

Zhang, Hongjie; Liang, Chao; Fu, Zicheng; Xu, Nan; Guo, Chong; Zhang, Xuezhao; Chen, Zhouhuan; Wang, Peng

doi:10.1016/j.mechatronics.2017.08.008

Cited by 67 publications

(34 citation statements)

References 25 publications

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“…Dynamic programming (DP) [13] and Pontryagin's minimum principle (PMP) [14] are the classic examples of global optimization strategies. The equivalent consumption minimization strategy (ECMS) [15] and convex programming (CP) [16] are typical instantaneous optimization strategies. Global optimization strategies can offer optimal solutions for the problem.…”

Section: Dmentioning

confidence: 99%

A Hierarchical Energy Management Strategy Based on Model Predictive Control for Plug-In Hybrid Electric Vehicles

et al. 2019

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This paper presents a prescient energy management strategy based on the model predictive control (MPC) for the parallel plug-in hybrid electric vehicles (PHEVs). In this hierarchical strategy, dynamic programming (DP), with its improved calculation speed, is chosen as the solution algorithm to calculate the optimal power distribution combinations in the predicted receding horizon and under the given terminal battery state-of-charge (SOC) terminal constraint. A synthesized velocity profile prediction (SVPP) method is adopted. The macroscopically and microcosmically predicted velocities obtained by the participatory sensing data (PSD)-based method and the Markov chain (MC), respectively, are synthesized by the linear regression method, obtaining the final velocity profile. In the linear regression step, a particle filter (PF) is implemented for the parameter estimation. According to the characteristics of the driving conditions and components, the terminal battery SOC in each control horizon is constrained by a novel method. Finally, we demonstrate the capability of the proposed scheme in terms of fuel economy improvement by comparing the value of this metric with those of other strategies through simulation.

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Section: Dmentioning

confidence: 99%

A Hierarchical Energy Management Strategy Based on Model Predictive Control for Plug-In Hybrid Electric Vehicles

et al. 2019

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“…As a result, the equivalent factor should be tuned accordingly. In this paper, the equivalent factor is adjusted by the method in our former work in [74]. After collecting enough PEHV customized traffic data, the route weight of each route segment can be calculated.…”

Section: Energy Management Strategymentioning

confidence: 99%

An Economical Route Planning Method for Plug-In Hybrid Electric Vehicle in Real World

Zhang

Liang

et al. 2017

Energies

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Relieving the adverse effects of automobiles on the environment and natural resources has drawn the attention of numerous researchers. This paper seeks a new path to reach a target by focusing on the synergy of the vehicle and the environment. A real-time economical route planning method for a plug-in hybrid electric vehicle (PHEV) is proposed. Three main contributions have been made. Firstly, a real comparison test is performed to provide rudimentary understanding of the difference in energy usage and route planning between PHEVs and conventional vehicles. Secondly, an approach to obtain PHEV customized data is developed for road weight calculation, which is the essential step in route planning. This method incorporates traffic data from conventional vehicles with the PHEV simulation model, obtaining the required data. Thirdly, the travel expense estimation model (TEEM) is designed. The TEEM could be applied to calculate the road weight of each road segment considering the impact on energy consumption with respect to environmental factors, providing the grounds for route planning. The proposed method to plan an economical route is evaluated, and the results justify its validation and ability to improve fuel economy.

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“…Driver Behavior: To optimize the performance of overall system, the driver behavior must be considered [15], [16]. Because the PHEV usually runs in fixed route which has repetitiveness of the city route and similar terrain of a certain region, the stochastic feature of driver behavior can be obtained from the historical driving data [17].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Iterative Learning Control for Energy Management of Plug-In Hybrid Electric Vehicle

et al. 2019

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A novel optimal energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV) is proposed in this paper, which takes the battery health into consideration for prolonging its service life. The integrated control framework combines batch-wise iterative learning control (ILC) and time-wise model predictive control (MPC), referred to as 2D-MPILC. The major advantages of the proposed method are shown with better performance as well as faster convergence speed by taking into account the time-wise feedback control within the current batch. Then, the MPILC method is applied for the PHEV with the ability to make continuous period-to-period improvements. Its performances will approach dynamic programming (DP)based method after a learning process with satisfying real-time processing capacity. The results in real-world city bus routines verify the effectiveness of the proposed EMS for greatly improving the performance of the PHEV. INDEX TERMS Plug-in hybrid electric vehicle, model predictive iterative learning control, battery aging, nonlinear optimization, 2-D Lyapunov stability theory.

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Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real time traffic information prediction

Cited by 67 publications

References 25 publications

A Hierarchical Energy Management Strategy Based on Model Predictive Control for Plug-In Hybrid Electric Vehicles

A Hierarchical Energy Management Strategy Based on Model Predictive Control for Plug-In Hybrid Electric Vehicles

An Economical Route Planning Method for Plug-In Hybrid Electric Vehicle in Real World

Model Predictive Iterative Learning Control for Energy Management of Plug-In Hybrid Electric Vehicle

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