Optimal energy management for a hybrid energy storage system combining batteries and double layer capacitors

Romaus, Christoph; Böcker, Joachim; Witting, Katrin; Seifried, Albert; Znamenshchykov, O.

doi:10.1109/ecce.2009.5316428

Cited by 55 publications

(41 citation statements)

References 6 publications

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“…Consequently, the resulting solution methods involve solving multiple optimization problems consecutively. Scalarization can be achieved by various approaches such as the weighted-sum method, the -constraint method, normal boundary intersection, or reference point methods [4,5,15].…”

Section: Solution Methodsmentioning

confidence: 99%

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

Peitz¹,

Dellnitz²

2018

Preprint

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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.

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Section: Solution Methodsmentioning

confidence: 99%

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

Peitz¹,

Dellnitz²

2018

Preprint

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show abstract

“…Consequently, many scalar-valued constrained optimization problems have to be solved. In this paper we apply the reference point method [14] in order to transform a bicriterial optimal control problem into a sequence of scalar-valued optimal control problems and solve them using well-known optimal control techniques; see [17]. We extend our previous results obtained in [2] with respect to the following issues: Instead of the linear heat equation, general linear evolution problems are considered.…”

Section: Introductionmentioning

confidence: 99%

“…In order to do so, we follow the ideas laid out in [13,14] and make use of the (Euclidean) reference point method : Given a reference point…”

mentioning

confidence: 99%

POD-based error control for reduced-order bicriterial PDE-constrained optimization

Banholzer

Beermann

Volkwein

2017

Annual Reviews in Control

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In the present paper, a bicriterial optimal control problem governed by an abstract evolution problem and bilateral control constraints is considered. To compute Pareto optimal points and the Pareto front numerically, the (Euclidean) reference point method is applied, where many scalar constrained optimization problems have to be solved. For this reason a reduced-order approach based on proper orthogonal decomposition (POD) is utilized. An a-posteriori error analysis ensures a desired accuracy for the Pareto optimal points and for the Pareto front computed by the POD method. Numerical experiments for evolution problems with convection-diffusion illustrate the efficiency of the presented approach.

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“…For the current state-of-the-art works on the subject, the reader is referred to papers such as [10][11][12][13][14][15]. Romaus et al [10] addressed the problem of controlling the power flows of a hybrid energy storage system combining batteries and double layer capacitors, and proposed the use of self-optimization methods that can consider various operating conditions and take into account several partly conflicting objective functions. As an application domain, the reference paper [10] considered an autonomous rail-bound vehicle.…”

Section: System Configuration and Related Backgroundmentioning

confidence: 99%

“…Romaus et al [10] addressed the problem of controlling the power flows of a hybrid energy storage system combining batteries and double layer capacitors, and proposed the use of self-optimization methods that can consider various operating conditions and take into account several partly conflicting objective functions. As an application domain, the reference paper [10] considered an autonomous rail-bound vehicle. Chen et al [11] proposed a scheduling strategy for a hybrid energy storage system for a wind farm based on predictive wind speed and the particle swarm optimization (PSO) algorithm.…”

Section: System Configuration and Related Backgroundmentioning

confidence: 99%

Dynamic Power Management for Portable Hybrid Power-Supply Systems Utilizing Approximate Dynamic Programming

et al. 2015

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Abstract:Recently, the optimization of power flows in portable hybrid power-supply systems (HPSSs) has become an important issue with the advent of a variety of mobile systems and hybrid energy technologies. In this paper, a control strategy is considered for dynamically managing power flows in portable HPSSs employing batteries and supercapacitors. Our dynamic power management strategy utilizes the concept of approximate dynamic programming (ADP). ADP methods are important tools in the fields of stochastic control and machine learning, and the utilization of these tools for practical engineering problems is now an active and promising research field. We propose an ADP-based procedure based on optimization under constraints including the iterated Bellman inequalities, which can be solved by convex optimization carried out offline, to find the optimal power management rules for portable HPSSs. The effectiveness of the proposed procedure is tested through dynamic simulations for smartphone workload scenarios, and simulation results show that the proposed strategy can successfully cope with uncertain workload demands.

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Optimal energy management for a hybrid energy storage system combining batteries and double layer capacitors

Cited by 55 publications

References 6 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

POD-based error control for reduced-order bicriterial PDE-constrained optimization

Dynamic Power Management for Portable Hybrid Power-Supply Systems Utilizing Approximate Dynamic Programming

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