Optimal energy management in series hybrid electric vehicles

Brahma, Avra; Guezennec, Yann; Rizzoni, Giorgio

doi:10.1109/acc.2000.878772

Cited by 247 publications

(116 citation statements)

References 3 publications

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“…In a previous work we proposed a real-time strategy for a heavy series HEV based on the optimal control over standard city bus driving cycles [9]. Control of series HEVs has also been treated in [10]- [13], but none of them adopt the approach of this work.…”

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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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

View full text Add to dashboard Cite

show abstract

“…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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“…When loads are limited to a short period of time (tens of seconds or less), the variability tends to be defined by three main factors: when the demand occurs, its intensity and duration. As an example, electric vehicles show this sort of unpredictability, as there is no prior knowledge of the acceleration that the driver requires when driving, and therefore, the stochastic behaviour of the demand is taken into account when developing control strategies [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Management Strategy for Energy Storage with Stochastic Loads

2016

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Abstract:In this paper, a power management strategy (PMS) has been developed for the control of energy storage in a system subjected to loads of random duration. The PMS minimises the costs associated with the energy consumption of specific systems powered by a primary energy source and equipped with energy storage, under the assumption that the statistical distribution of load durations is known. By including the variability of the load in the cost function, it was possible to define the optimality criteria for the power flow of the storage. Numerical calculations have been performed obtaining the control strategies associated with the global minimum in energy costs, for a wide range of initial conditions of the system. The results of the calculations have been tested on a MATLAB/Simulink model of a rubber tyre gantry (RTG) crane equipped with a flywheel energy storage system (FESS) and subjected to a test cycle, which corresponds to the real operation of a crane in the Port of Felixstowe. The results of the model show increased energy savings and reduced peak power demand with respect to existing control strategies, indicating considerable potential savings for port operators in terms of energy and maintenance costs.

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Optimal energy management in series hybrid electric vehicles

Cited by 247 publications

References 3 publications

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

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

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

Optimal Power Management Strategy for Energy Storage with Stochastic Loads

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