Multihorizon Model Predictive Control: An Application to Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Hu, Qiuhao; Amini, Mohammad Reza; Kolmanovsky, Ilya; Sun, Jing; Wiese, Ashley; Seeds, Julia Buckland

doi:10.1109/tcst.2021.3091887

Cited by 31 publications

(7 citation statements)

References 50 publications

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“…Also, the inclusion of predictive data to plan the vehicle's battery usage for the upcoming driving mission has been studied [13]. The integration of thermal effects in the power distribution decision process is investigated in [14,15]. The authors of [16] show that in the case of a diesel HEV, the decision of the EMS also has a large influence on its NO x emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Management of Hydrogen Hybrid Electric Vehicles—Online-Capable Control

Machacek,

Yasar,

Widmer

et al. 2024

Energies

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The results shown in this paper extend our research group’s previous work, which presents the theoretically achievable hydrogen engine-out NOxeo (H2-NOxeo) Pareto front of a hydrogen hybrid electric vehicle (H2-HEV). While the Pareto front is calculated offline, which requires significant computing power and time, this work presents an online-capable algorithm to tackle the energy management of a H2-HEV with explicit consideration of the H2-NOxeo trade-off. Through the inclusion of realistic predictive data on the upcoming driving mission, a model predictive control algorithm (MPC) is utilized to effectively tackle the conflicting goal of achieving low hydrogen consumption while simultaneously minimizing NOxeo. In a case study, it is shown that MPC is able to satisfy user-defined NOxeo limits over the course of various driving missions. Moreover, a comparison with the optimal Pareto front highlights MPC’s ability to achieve close-to-optimal fuel performance for any desired cumulated NOxeo target on four realistic routes for passenger cars.

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Section: Literature Reviewmentioning

confidence: 99%

Energy Management of Hydrogen Hybrid Electric Vehicles—Online-Capable Control

Machacek,

Yasar,

Widmer

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…Also, the inclusion of predictive data to plan the vehicle's battery usage for the upcoming driving mission was studied [13]. The integration of thermal effects in the power distribution decision process is investigated in [14] and [15]. The authors of [16] show that, in the case of a Diesel HEV, the decision of the EMS also has a large influence on its NO x emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Management of Hydrogen Hybrid Electric Vehicles – Online-Capable Control

Machacek,

Yasar,

Widmer

et al. 2024

Preprint

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The results shown in this paper extend our research group's previous work, which presents the theoretically achievable hydrogen engine-out NOx (H2-NOx) Pareto front of a hydrogen hybrid electric vehicle (H2-HEV). While the Pareto front is calculated offline, which requires significant computing power and time, this work presents an online-capable algorithm to tackle the energy management of a H2-HEV with explicit consideration of the H2-NOx trade-off. Through the inclusion of realistic predictive data on the upcoming driving mission, a model predictive control algorithm (MPC) is utilized to effectively tackle the conflicting goal of achieving low hydrogen consumption while simultaneously minimizing the NOx. In a case study it is shown that the MPC is able to satisfy user-defined NOx limits over the course of various driving missions. Moreover, a comparison to the optimal Pareto front highlights the MPC's ability to achieve close-to-optimal fuel-performance for any desired cumulated NOx target on four realistic routes for passenger cars.

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“…To this end, some additional results on optimization of EVs heat pump-based thermal management system can be noted [26][27][28][29][30]. One can distinguish primarily the use of NMPC techniques for heat pump-based thermal management systems of EVs [31][32][33][34][35]. These results are complemented by model-based and model-free control methods for the thermal management system of EVs and the improved exploitation of the heat from the vehicles' batteries [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control of heat pumps in electric vehicles

Rigatos,

Siano,

Cuccurullo

et al. 2024

Preprint

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Heat pumps can be used for thermal management of electric vehicles (EVs) in a low-cost and environmental friendly manner. Heat pumps can exploit the heat that is diffused by the vehicle’s batteries so as to perform temperature control of the passengers cabin. A heat pump-based thermal management system of EVs has the dual objective of (a) stabilizing temperature in the passengers’ cabin at the desirable levels so as to ensure the passengers’ comfort and (b) stabilizing temperature at the batteries’ side so as to avoid degradation of the batteries power storage capacity and of their regenerative charging capability. So far nonlinear model predictive control (NMPC) methods have been mainly applied for the nonlinear optimal control problem of heat-pumps in EVs. In the present article a novel nonlinear optimal control method is proposed for solving the nonlinear optimal control problem of heat pumps. This method is computationally simple, has clear implementation stages and is of proven global stability. The dynamic model of the heat pump undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. Linearization takes place at each sampling instance, around a temporary operating point which is defined by the present value of the heat pump’s state vector and by the last sampled value of the control inputs vector. For the approximately linearized model of the heat pump an H-infinity stabilizing controller is designed. The feedback gains of the controller are computed through the solution of an algebraic Riccati equation, at each time-step of the control method. The global stability properties of the control scheme are proven through Lyapunov analysis. Moreover, to enable state estimation-based control, the H-infinity Kalman Filter is used as a robust observer. The method achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs.

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Multihorizon Model Predictive Control: An Application to Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Cited by 31 publications

References 50 publications

Energy Management of Hydrogen Hybrid Electric Vehicles—Online-Capable Control

Energy Management of Hydrogen Hybrid Electric Vehicles—Online-Capable Control

Energy Management of Hydrogen Hybrid Electric Vehicles – Online-Capable Control

Nonlinear optimal control of heat pumps in electric vehicles

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