Control of a Parallel Hybrid Powertrain: Optimal Control

Delprat, Sébastien; Lauber, Jimmy; Guerra, Thierry Marie; Rimaux, J.

doi:10.1109/tvt.2004.827161

Cited by 381 publications

(183 citation statements)

References 3 publications

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“…To remedy this problem, the stochastic dynamic programming (SDP) method [9][10][11] and the driving pattern detection within multiple driving cycles [12] had been suggested as the possible solutions. As a general case of the Euler-Lagrange equation, Pontryagin's minimum principle (PMP) was also introduced to obtain optimal control solution for hybrid vehicles [13][14][15][16], where the Hamiltonian is considered as an analytical function. Based on the theoretical backgrounds of PMP, the equivalent consumption minimization strategy (ECMS) [17][18][19] was proposed to associate the electrical energy usage with future fuel consumption, and the equivalent fuel consumption is minimized at each time instant.…”

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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“…Methods in use include deterministic [1]- [3] and stochastic dynamic programming [4], [5], Pontryagin's minimum principle [6], [7] and game theory [8]. The optimal control generally depends on knowledge about the probability distribution of factors such as power load over a specific driving cycle, and transferring the optimal solution to a feasible real-time strategy has proved an awkward process.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Control of Series HEVs Including Resistive Losses and Varying Engine Efficiency

Neuman¹,

Wahlberg²,

Folkesson³

2009

SAE Technical Paper Series

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A model of a series HEV (Hybrid Electric Vehicle) including resistive losses in the energy storage and varying engine and generator efficiency is presented. The purpose is to study how system properties affect the optimal control of series HEVs. We show that there is an inherent trade-off in the control of such a system, based on the characteristics of the efficiency curve of the EGU (Engine and Generator Unit) together with the energy storage internal resistance, capacitance and energy level. The relation between these properties determines the optimal control of the EGU, and this is thus the mechanism behind all optimal control problems using a similar model setup. We illustrate our reasoning using deterministic dynamic programming where system properties are varied and the optimal control is studied.

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“…The control of the power flows among these devices is commonly referred to as the energy management or supervisory control [1]. There are many approaches [2] to design an energy management strategy, covering heuristic approaches [3][4][5][6] as well as optimization-based approaches, such as deterministic dynamic programming (DP) [7][8][9][10][11][12][13], Pontryagin's minimum principle (PMP) [14][15][16][17][18][19][20][21] and stochastic dynamic programming (SDP) [22][23][24][25][26]. Recently, convex optimization [27] has attracted attention in the research field of energy management for HEVs.…”

Section: Introductionmentioning

confidence: 99%

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

et al. 2014

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This paper presents a novel method to solve the energy management problem for hybrid electric vehicles (HEVs) with engine start and gearshift costs. The method is based on a combination of deterministic dynamic programming (DP) and convex optimization. As demonstrated in a case study, the method yields globally optimal results while returning the solution in much less time than the conventional DP method. In addition, the proposed method handles state constraints, which allows for the application to scenarios where the battery state of charge (SOC) reaches its boundaries.

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Control of a Parallel Hybrid Powertrain: Optimal Control

Cited by 381 publications

References 3 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

Modelling and Control of Series HEVs Including Resistive Losses and Varying Engine Efficiency

Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs

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