Predictive energy management of a power-split hybrid electric vehicle

Borhan, Hoseinali; Vahidi, Ardalan; Phillips, Anastasia; Kuang, Ming; Kolmanovsky, Ilya

doi:10.1109/acc.2009.5160451

Cited by 188 publications

(92 citation statements)

References 5 publications

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“…Hence, the controller based on DP is effective only for the specific driving cycle that is used for the rule extraction. 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.…”

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 formulation of the MPC which is used in this paper is a repeated solution of a finite horizon optimal control problem with respect to system dynamics and constraints on states and control inputs. Similar method is used in [15] and [16]. MPC is explained in details in [17] and [18].…”

Section: B Mpc Controllermentioning

confidence: 99%

Predictive control of the engine cooling system for fuel efficiency improvement

Khodabakhshian

Feng

Wikander

2014

2014 IEEE International Conference on Automation Science and Engineering (CASE)

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Abstract-The engine cooling system in trucks is one of the main sources of parasite load. Thus fuel efficiency can be improved by optimal control of engine thermal management system considering fuel consumption minimization as the objective. Although several optimal control methods have been proposed for the engine cooling system, their main emphasize is on regulating engine and coolant temperature in an acceptable range rather than minimizing fuel consumption. In contrast, this paper investigates the fuel saving potential of predictive optimal control methods for the engine cooling system of conventional trucks. Our method exploits the idea of energy buffers in the automotive system, where the engine cooling system and the battery serve as energy buffers. The advantages of this approach are the recovery of brake energy and the balance of energy sources so that the total energy loss is minimized. A model predictive controller is used as the real time controller, and the results are compared with a simple state feedback controller and a global optimal solution obtained by dynamic programming. The results show limited but notable improvement in fuel efficiency. The results also construct a base for ongoing research on energy buffer control in conventional heavy trucks.

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“…Most of those works address it through the optimal control theory, searching for the control policy that provides the minimum fuel consumption while satisfying other energetic constraints. Dynamic Programming (DP) (Mosbech, 1980;, Pontryagin Minimum Principle (PMP) (Serrao et al, 2011;Chasse and Sciarretta, 2011) or Model Predictive Control (MPC) (Borhan et al, 2009;Kermani et al, 2012) techniques have been applied to the Energy Management Problem of HEVs, hereinafter EMP. Ad hoc methods such as the Equivalent Consumption Minimization Strategy (ECMS) (Paganelli et al, 2001) have been developed to address the EMP, demonstrating similar performance to the methods purely derived from the optimal control theory.…”

Section: Introductionmentioning

confidence: 99%

A stochastic method for the energy management in hybrid electric vehicles

Payri

Guardiola

Plá

et al. 2014

Control Engineering Practice

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There are many approaches addressing the problem of optimal energy management in hybrid electric vehicles; however, most of them optimise the control strategy for particular driving cycles. This paper takes into account that the driving cycle is not a priori known to obtain a near-optimal solution. The proposed method is based on analysing the power demands in a given receding horizon to estimate future driving conditions and minimise the fuel consumption while cancelling the expected battery energy consumption after a defined time horizon. Simulations show that the proposed method allows charge sustainability providing near-optimal results.

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Predictive energy management of a power-split hybrid electric vehicle

Cited by 188 publications

References 5 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

Predictive control of the engine cooling system for fuel efficiency improvement

A stochastic method for the energy management in hybrid electric vehicles

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