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Dynamic Programming-Based Model Predictive Control of Energy Management for a Novel Plug-In Hybrid Electric Vehicle.

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Title: Dynamic Programming-Based Model Predictive Control of Energy Management for a Novel Plug-In Hybrid Electric Vehicle.
Authors: Zou, Shunzhang1 (AUTHOR), Zhang, Jun1,2 (AUTHOR) jzhang@hnu.edu.cn, Liu, Yunfeng1,2 (AUTHOR), Yang, Yu1,2 (AUTHOR), Zhou, Yunshan1 (AUTHOR), Peng, Jingyang1 (AUTHOR), Wang, Guolin1 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2487. 24p.
Subject Terms: *Dynamic programming, *Plug-in hybrid electric vehicles, *Hybrid electric vehicles, *Predictive control systems, *Radial basis functions, *Energy consumption
Abstract: To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine's generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141601
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Dynamic Programming-Based Model Predictive Control of Energy Management for a Novel Plug-In Hybrid Electric Vehicle.
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  Data: <searchLink fieldCode="AR" term="%22Zou%2C+Shunzhang%22">Zou, Shunzhang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jun%22">Zhang, Jun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jzhang@hnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Yunfeng%22">Liu, Yunfeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Yu%22">Yang, Yu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Yunshan%22">Zhou, Yunshan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Peng%2C+Jingyang%22">Peng, Jingyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Guolin%22">Wang, Guolin</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2487. 24p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Plug-in+hybrid+electric+vehicles%22">Plug-in hybrid electric vehicles</searchLink><br />*<searchLink fieldCode="DE" term="%22Hybrid+electric+vehicles%22">Hybrid electric vehicles</searchLink><br />*<searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine's generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. [ABSTRACT FROM AUTHOR]
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        Value: 10.3390/en19102487
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 2487
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      – SubjectFull: Dynamic programming
        Type: general
      – SubjectFull: Plug-in hybrid electric vehicles
        Type: general
      – SubjectFull: Hybrid electric vehicles
        Type: general
      – SubjectFull: Predictive control systems
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      – SubjectFull: Radial basis functions
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      – SubjectFull: Energy consumption
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      – TitleFull: Dynamic Programming-Based Model Predictive Control of Energy Management for a Novel Plug-In Hybrid Electric Vehicle.
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            NameFull: Zou, Shunzhang
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            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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