Hybrid model predictive control tracking of a sawtooth driving profile for an HEV

Uthaichana, Kasemsak; Bengea, Sorin; DeCarlo, Raymond A.; Pekarek, Steve; Žefran, Miloš

doi:10.1109/acc.2008.4586617

Cited by 16 publications

(6 citation statements)

References 35 publications

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“…The summary of the modeling equations, detailed in [31], [32], are summarized in this subsection. The input-output power relationships of subsystems consist of algebraic and dynamical equations.…”

Section: Hev State Model Overviewmentioning

confidence: 99%

Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle

Uthaichana¹,

DeCarlo²,

Bengea³

et al. 2018

Preprint

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This paper presents a nonlinear-model based hybrid optimal control technique to compute a suboptimal power-split strategy for power/energy management in a parallel hybrid electric vehicle (PHEV). The power-split strategy is obtained as model predictive control solution to the power management control problem (PMCP) of the PHEV, i.e., to decide upon the power distribution among the internal combustion engine, an electric drive, and other subsystems. A hierarchical control structure of the hybrid vehicle, i.e., supervisory level and local or subsystem level is assumed in this study.The PMCP consists of a dynamical nonlinear model, and a performance index, both of which are formulated for power flows at the supervisory level.The model is described as a bi-modal switched system, consistent with the operating mode of the electric ED. The performance index prescribing the desired behavior penalizes vehicle tracking errors, fuel consumption, and frictional losses, as well as sustaining the battery state of charge (SOC). The power-split strategy is obtained by first creating the embedded optimal control problem (EOCP) from the original bi-modal switched system model with the performance index. Direct collocation is applied to transform the problem into a nonlinear programming problem. A nonlinear predictive control technique (NMPC) in conjunction with a sequential quadratic programming solver is used to compute suboptimal numerical solutions to the PMCP. Methods for approximating the numerical solution to the EOCP with trajectories of the original bi-modal PHEV are also presented in this paper. The usefulness of the approach is illustrated via simulation results on several case studies.

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Section: Hev State Model Overviewmentioning

confidence: 99%

Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle

Uthaichana¹,

DeCarlo²,

Bengea³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the literature, many researchers have examined MPC to solve the EMP for HEV/PHEVs. Nonlinear programming [33], quadratic programming [34], Pontryagin's minimum principle [35], and DP [20], [36] have all been applied. These predictive energy management strategies provide improved performance over instantaneous or rule-based energy management strategies, and are real-time implementable [17].…”

Section: B Literature Review Of Phev Energy Managementmentioning

confidence: 99%

Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles

Sun

Moura

et al. 2015

IEEE Trans. Contr. Syst. Technol.

265

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Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SOC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SOC trajectories that utilized as final state constraints in the MPC level. This PHEV energy management framework is evaluated under three different scenarios: (i) without traffic flow information, (ii) with static traffic flow information, and (iii) with dynamic traffic flow information. Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.

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“…Wei et al study the problem of traction control, that is, how to stabilize a wheeled mobile robot (WMR) subject to wheel slippage to a desired configuration [12]. Uthaichana et al present a hybrid optimal control solution for the power management problem of a parallel hybrid electric vehicle (PHEV), based on a two-mode operation model of PHEV [13].…”

Section: Introductionmentioning

confidence: 99%

Ecodriving for Reduction of Bus Transit Emission with Vehicle’s Hybrid Dynamic Model

Zheng

Zhang

2015

Mathematical Problems in Engineering

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This paper formulates a global ecodriving optimal control to advise the green driving speed for bus transit to minimize the exhaust emission using Vehicle-to-Infrastructure (V2I) communication. Assuming communication between vehicles and infrastructure (V2I) and knowledge of traffic signal timings and waiting passengers at stations are known, an optimal driving speed is proposed to minimize the total vehicle emissions of the bus route. The dwell time of the bus transit at each station which includes two parts is proposed. A traffic lights timing model is employed as constraints to control the formation of the green wave band. Vehicle specific power (VSP) model is further applied to evaluate the exhaust emission level linked with the speed and acceleration of the bus transit. An approximate sixteen-kilometer traffic network including fourteen intersections and fifteen stations of Beijing bus transit line 1 in Chaoyang District, Beijing, is chosen to investigate the performance of the developed ecodriving approach.

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Hybrid model predictive control tracking of a sawtooth driving profile for an HEV

Cited by 16 publications

References 35 publications

Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle

Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle

Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles

Ecodriving for Reduction of Bus Transit Emission with Vehicle’s Hybrid Dynamic Model

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