Receding Horizon Control for Distributed Energy Management of a Hybrid Heavy-Duty Vehicle with Auxiliaries

Romijn, Tcj Constantijn; Donkers, M.C.F.; Kessels, Jtba John; Weiland, S.

doi:10.1016/j.ifacol.2015.10.029

Cited by 11 publications

(10 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [33], we proposed to take variable prediction time intervals τ n to allow for a long prediction horizon using only a limited number of prediction instants N. This is necessary to keep the optimal control problem small in the number of decision variables and real-time solvable. This method is similar to move blocking strategies [36] except that it does not only assume that the control inputs over the prediction time intervals are constant, it also assumes that it is sufficient to only constrain the dynamics at the beginning and end of each interval through (15c).…”

Section: Receding Horizon Optimal Control Problemmentioning

confidence: 99%

“…In particular, we define the optimal control problem over a receding horizon and apply a dual decomposition to this optimal control problem, as we proposed in [33], which uses the framework presented in [26]. The application of the dual decomposition results in small-sized linearly-constrained quadratic programs (LCQP) for each of the subsystems, which can be efficiently solved in real time with embedded LCQP solvers.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we will use variable prediction time intervals that allows long prediction horizons with a limited number of decision variables. These were first introduced in [33].…”

Section: Introductionmentioning

confidence: 99%

“…Contrary to [33], in this paper, we present the general CVEM problem as a quadratically-constrained linear program (QCLP). This allows the method to be applied to various vehicle configurations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

et al. 2017

View full text Add to dashboard Cite

Abstract:In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model.

show abstract

Section: Receding Horizon Optimal Control Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Therefore, we will use variable prediction time intervals that allows long prediction horizons with a limited number of decision variables. These were first introduced in [33].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Generally, MPCs are solved by gradient-based Newton's methods. For example, the nonlinear MPC is solved by sequential quadratic programming (SQP) [11]; the mixedinteger optimization problems in hybrid MPC is solved by the hybrid toolbox in MATLAB [19]; MATLAB command 'quadprog' [1], 'CVXGEN' [20] and 'qpOASES' [21] are for quadratic programming (QP) of linear constrained MPCs. However, only linear MPCs have been realized the feature of real-time implementation, running nonlinear MPC for HEV energy management in real-time controllers has yet to be demonstrated [14].…”

Section: Introductionmentioning

confidence: 99%

Cyber-Physical Energy-Saving Control for Hybrid Aircraft-Towing Tractor Based on Online Swarm Intelligent Programming

Zhou

Zhang

et al. 2018

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

show abstract

Essential predictive information for high fuel efficiency and local emission free driving with PHEVs

et al. 2019

View full text Add to dashboard Cite

Receding Horizon Control for Distributed Energy Management of a Hybrid Heavy-Duty Vehicle with Auxiliaries

Cited by 11 publications

References 11 publications

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

Cyber-Physical Energy-Saving Control for Hybrid Aircraft-Towing Tractor Based on Online Swarm Intelligent Programming

Essential predictive information for high fuel efficiency and local emission free driving with PHEVs

Contact Info

Product

Resources

About