An iterative dynamic programming approach for the global optimal control of hybrid electric vehicles under real-time constraints

Wahl, Hans-Georg; Gauterin, Frank

doi:10.1109/ivs.2013.6629531

Cited by 27 publications

(15 citation statements)

References 7 publications

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“…The predicted N step finite-horizon velocity profile can change at any instance for reasons like new traffic participants or speed limits entering the electronic horizon, which leads to replanning of the longitudinal trajectory. Therefore, the problem is solved for the finite-horizon every timestep while only the first result u 0 is used, similarly to a model predictive control algorithm [3]. The formulation of the enhanced problem includes all necessary information to optimize energy efficiency as well as wear and comfort.…”

Section: B Formulation Of An Enhanced Optimization Problemmentioning

confidence: 99%

“…RELATED WORK In hybrid electric vehicles (HEV), operation strategies aim to improve the energy efficiency by controlling torque split between EM and ICE. [3] shows that an adaptive cruise control based on model predictive control can improve the efficiency significantly due to an anticipatory driving strategy and the optimized operation of the powertrain components. Other work such as [4], [5], where the velocity is not controlled by the optimization algorithm but by the driver, is improving the efficiency of HEVs by control algorithms, which take the prediction of near-future driving situations into account.…”

Section: Introductionmentioning

confidence: 99%

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Energy-efficient torque distribution for axle-individually propelled electric vehicles

Koehler

Viehl

Bringmann

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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We propose a novel operation strategy for electric vehicles with axle-individual electric machines to improve their energy efficiency in typical driving situations. The developed algorithm is allocating a total torque requested by a velocity controlling system or the driver to the electric machines such that the energy loss is reduced compared to an equal distribution. By taking near-future forecasts into account, the predictive nature of the algorithm leads to a minimized number of clutching processes compared to previous work and thereby contributes to increased comfort and minimized component wear. Overall, an average reduction of up to 25 % in the electric machine losses can be achieved for the ARTEMIS driving cycles. At the same time, a reduction of the clutching operations by 70 % is possible due to the forecast, compared to algorithms only considering the momentary state. I. INTRODUCTIONAn increased market penetration of electric vehicles (EV) can offer a major contribution to CO 2 reduction, provided the energy used to charge these vehicles is generated from regenerative sources. However, a primary obstacle for current EVs is the low energy density of the storage media. The dominating application of batteries as energy storage devices leads to a strongly limited range of these battery electric vehicles (BEV). Current middle-class BEVs offer a range of around 150 km, which is significantly less than that of conventional vehicles propelled by an internal combustion engine (ICE). Besides improving the battery technology itself, research is focusing on both operation and driving strategies to advance energy efficiency of BEVs and hence increase their cruising range.The presented work introduces an operation strategy to optimize the efficiency of BEVs propelled axle-individually by two electric machines (EM). The drive elements are used both for propulsion and for regenerative braking. If a longitudinal acceleration is requested, the availability of two drive elements for providing a combined torque leads to an over-actuated system. A distribution factor is introduced, deciding how much torque each machine is generating to satisfy the request. Using this degree of freedom in an adaptive way -based on the powertrain characteristicsleads to an efficiency gain of the powertrain. It is shown in the course of the paper that the energy losses due to machine inefficiency can be reduced in average by up to 25 % by utilizing an optimized torque distribution. As the bulk of the energy benefit is gained by switching between the use of one or two EMs, a naive approach can lead to frequent clutching

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Section: B Formulation Of An Enhanced Optimization Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-efficient torque distribution for axle-individually propelled electric vehicles

Koehler

Viehl

Bringmann

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

View full text Add to dashboard Cite

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“…For control of hybrid electric vehicle (HEV), iDP was used as dynamic optimization in a model predictive controller (MPC) (23) . With a short range of vehicle operation, iDP was reported to have potential in real-time applications.…”

Section: Introductionmentioning

confidence: 99%

“…With a short range of vehicle operation, iDP was reported to have potential in real-time applications. To reduce the required memory and computational time, the state space is reduced as narrow as possible (23) . However, in many cases, where the state space can not be reduced, basic iDP must calculate all points in the state space, even they are not in the forward-reachable state space.…”

Section: Introductionmentioning

confidence: 99%

Iterative Dynamic Programming for Optimal Control Problem with Isoperimetric Constraint and Its Application to Optimal Eco-driving Control of Electric Vehicle

et al. 2018

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An iterative dynamic programming (iDP) is proposed along with an adaptive objective function for solving optimal control problem (OCP) with isoperimetric constraint. Its application is investigated for optimal eco-driving control problem in electric vehicle (EV). The proposed method reduces the computational effort and enhances the global convergence of using iDP. Numerical calculations show that for the same computational time, the proposed method guarantees higher quality of solution when compared to basic iDP. In addition, the proposed method also achieves high accuracy of optimal solution with much less computational time than basic dynamic programming (DP), thus allowing for potential on-line implementation. Furthermore, the proposed iDP is successful in solving the optimal eco-driving control problem of EV with very long operational range.

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“…IDP is a numeric method based on an iterated application of the Dynamic Programming approach, which involves: the choice of grid points for the control variables for each time instant inside the control horizon and the forward integration of the model; the application of Bellman's optimality principle proceeding backwards in time in order to choose the best control policy among the considered ones; the iteration of both these operations by refining the size of the search grid and focusing it around the best control policy found at each iteration. IDP has been applied for instance to chemical applications and optimization of biotechnological processes [44][45][46][47], vehicle control [48] and motion planning [49]. It has been applied to optimal control problems involving various classes of nonlinearities and high-dimensional systems while accounting for issues like the low sensitivity of the objective function with respect to the optimization variables and the splitting of the optimization horizon into stages of time-varying length.…”

Section: Introductionmentioning

confidence: 99%

Analysis and constrained optimal impulsive control of a Holling-II type trophic system with two sources

Galbusera

Pasquali

2015

Journal of the Franklin Institute

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An iterative dynamic programming approach for the global optimal control of hybrid electric vehicles under real-time constraints

Cited by 27 publications

References 7 publications

Energy-efficient torque distribution for axle-individually propelled electric vehicles

Energy-efficient torque distribution for axle-individually propelled electric vehicles

Iterative Dynamic Programming for Optimal Control Problem with Isoperimetric Constraint and Its Application to Optimal Eco-driving Control of Electric Vehicle

Analysis and constrained optimal impulsive control of a Holling-II type trophic system with two sources

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