A Comparison of two Predictive Approaches to Control the Longitudinal Dynamics of Electric Vehicles

Eckstein, Julian; Peitz, Sebastian; Schäfer, Kai; Friedel, Patrick; Köhler, Ulrich; Molo, Mirko Hessel-von; Ober‐Blöbaum, Sina; Dellnitz, Michael

doi:10.1016/j.protcy.2016.08.059

Cited by 15 publications

(17 citation statements)

References 7 publications

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“…We here want to demonstrate the superiority of multiobjective approaches over scalar-valued MPC using the example of autonomously-driving electric vehicles [47,48,153]. The problem there is to find the set of optimal engine torque profiles such that the velocity is maximized while the energy consumption is minimized.…”

Section: Example: Autonomous Drivingmentioning

confidence: 99%

“…The system dynamics are described by a four-dimensional, highly nonlinear ODE for the vehicle velocity, the battery state of charge and two battery voltage drops, cf. [153] for details. Numerical investigations reveal several symmetries in the system such that the only relevant state for a scenario is the current velocity, whereas all other states only have a minor influence on the solution of the MOP.…”

Section: Example: Autonomous Drivingmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction

Peitz

Dellnitz

2018

MCA

Self Cite

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Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control, which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging, which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview of recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.

show abstract

Section: Example: Autonomous Drivingmentioning

confidence: 99%

Section: Example: Autonomous Drivingmentioning

confidence: 99%

A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction

Peitz

Dellnitz

2018

MCA

Self Cite

View full text Add to dashboard Cite

show abstract

“…We here want to demonstrate the superiority of multiobjective approaches over scalar-valued MPC using the example of autonomously driving electric vehicles [47,48,153]. The problem there is to find the set of optimal engine torque profiles such that the velocity is maximized while the energy consumption is minimized.…”

Section: Example: Autonomous Drivingmentioning

confidence: 99%

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

Peitz¹,

Dellnitz²

2018

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract:Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto optimal solutions have led to a wide range of new applications related to optimal and feedback control which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview over recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.

show abstract

“…We here want to demonstrate the superiority of multiobjective approaches over scalar-valued MPC using the example of autonomously driving electric vehicles [42,43,143]. The problem there is to find the set of optimal engine torque profiles such that the velocity is maximized while the energy consumption is minimized.…”

Section: Example: Autonomous Drivingmentioning

confidence: 99%

“…The system dynamics are described by a four-dimensional, highly nonlinear ODE for the vehicle velocity, the battery state of charge and two battery voltage drops, cf. [143] for details. Numerical investigations reveal several symmetries in the system such that the only relevant state for a scenario is the current velocity whereas all other states only have a minor influence on the solution of the MOP.…”

Section: Example: Autonomous Drivingmentioning

confidence: 99%

A Survey of Recent Trends in Multiobjective Optimization – Surrogate Models, Feedback Control and Objective Reduction

Peitz¹,

Dellnitz²

2018

Preprint

Self Cite

View full text Add to dashboard Cite

Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto optimal solutions have led to a wide range of new applications related to optimal and feedback control which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview over recent developments in accelerating multiobjective optimization for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. In the latter case, various promising model predictive control approaches have been proposed. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.

show abstract

A Comparison of two Predictive Approaches to Control the Longitudinal Dynamics of Electric Vehicles

Cited by 15 publications

References 7 publications

A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction

A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

A Survey of Recent Trends in Multiobjective Optimization – Surrogate Models, Feedback Control and Objective Reduction

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A Comparison of two Predictive Approaches to Control the Longitudinal Dynamics of Electric Vehicles

Cited by 15 publications

References 7 publications

A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction

A Survey of Recent Trends in Multiobjective Optimal Control—Surrogate Models, Feedback Control and Objective Reduction

A Survey of Recent Trends in Multiobjective Optimal Control -- Surrogate Models, Feedback Control and Objective Reduction

A Survey of Recent Trends in Multiobjective Optimization&nbsp;&ndash; Surrogate Models, Feedback Control and Objective Reduction

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A Survey of Recent Trends in Multiobjective Optimization – Surrogate Models, Feedback Control and Objective Reduction