Control-Oriented Modeling and Fuel Optimal Control of a Series Hybrid Bus

Anatone, Michele; Cipollone, Roberto; Donati, Andrea; Sciarretta, Antonio

doi:10.4271/2005-01-1163

Cited by 24 publications

(16 citation statements)

References 7 publications

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“…Technically, the desired output trajectory is known a priori when the vehicle follows a certain drive cycle. Dynamic programming (DP) [3][4][5] and the analytical optimal control techniques [6][7][8] can then be used to obtain the theoretical optimal results. The results obtained through DP are unbeatable but, unfortunately, is an optimal input but not a control algorithm, and thus is not suitable for real-time implementation.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle

et al. 2013

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This paper compares two optimal energy management methods for parallel hybrid electric vehicles using an Automatic Manual Transmission (AMT). A control-oriented model of the powertrain and vehicle dynamics is built first. The energy management is formulated as a typical optimal control problem to trade off the fuel consumption and gear shifting frequency under admissible constraints. The Dynamic Programming (DP) and Pontryagin's Minimum Principle (PMP) are applied to obtain the optimal solutions. Tuning with the appropriate co-states, the PMP solution is found to be very close to that from DP. The solution for the gear shifting in PMP has an algebraic expression associated with the vehicular velocity and can be implemented more efficiently in the control algorithm. The computation time of PMP is significantly less than DP.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle

et al. 2013

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“…The choice for the integrand 0 1 ( , , , , ) E L t x u u v depends on the optimization objective, such as minimizing fuel consumption as in [Anatone et al (2005), Diop et al (2002)], or a combination of fuel consumption and emissions as in [Koot et al (2005), Lin et al (2003)], or equivalent fuel consumption [Pisu and Rizzoni (2007)]. For the work reported here, an integral quadratic PI uses the same integrand for both modes of operation,…”

Section: Performance Index For Pmcpmentioning

confidence: 97%

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

Uthaichana

Bengea

DeCarlo

et al. 2008

2008 American Control Conference

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Based on a two-mode operation model of a parallel hybrid electric vehicle (PHEV) developed in previous work, this paper presents a hybrid optimal control solution for the power management problem of a PHEV. The optimal power flows between the vehicle's main subsystems are computed as solutions of a switching-system optimization (nonlinear programming problem) formulated at the supervisory level assuming a hierarchical vehicle control structure. The resulting equations are numerically solved using collocation methods. The approach is illustrated using the optimal and MPC tracking of a sawtooth velocity driving profile. Unlike other work, the assumption that there is always sufficient power to achieve perfect tracking is not made in this work.

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“…Notice that T w (t) and ω w (t), which depend on the vehicle model, are computed thanks to a quasistatic approach (Beck et al 2007;Tate and Boyd 2007;Anatone et al 2005):…”

Section: System Modellingmentioning

confidence: 99%

Design optimization and optimal control for hybrid vehicles

2009

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In the context of growing environmental concerns, hybrid-electric vehicles appear to be one of the most promising technologies for reducing fuel consumption and pollutant emissions.This paper presents a parametric study focused on variations of the size of the powertrain components, and optimization of the power split between the engine and electric motor with respect to fuel consumption. To take into account the ability of the engine to be turned off, and the energy consumed to start the engine, we consider a second state to represent the engine: this state permits to obtain a more realistic engine model than it is usually done. Results are obtained for a prescribed vehicle cycle thanks to a dynamic programming algorithm based on a reduced model, and furnish the optimal power repartition at each time step regarding fuel consumption under constraints on the battery state of charge, and may then be used to determine the best components of a given powertrain.To control the energy sources in real driving conditions, when the future is unknown, a real-time control strategy is used: the Equivalent Consumption Minimization Strategy (ECMS). In this strategy, the battery is being considered as an auxiliary reversible fuel reservoir, using a scaling parameter which can be deduced from dynamic programming results. Offline optimization results and ECMS are compared for a realistic hybrid vehicle application.

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Control-Oriented Modeling and Fuel Optimal Control of a Series Hybrid Bus

Cited by 24 publications

References 7 publications

Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle

Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle

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

Design optimization and optimal control for hybrid vehicles

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