19 research outputs found
Exploiting Chordality in Optimization Algorithms for Model Predictive Control
In this chapter we show that chordal structure can be used to devise
efficient optimization methods for many common model predictive control
problems. The chordal structure is used both for computing search directions
efficiently as well as for distributing all the other computations in an
interior-point method for solving the problem. The chordal structure can stem
both from the sequential nature of the problem as well as from distributed
formulations of the problem related to scenario trees or other formulations.
The framework enables efficient parallel computations.Comment: arXiv admin note: text overlap with arXiv:1502.0638
Optimal energy management for hybrid electric aircraft
A convex formulation is proposed for optimal energy management in aircraft
with hybrid propulsion systems consisting of gas turbine and electric motor
components. By combining a point-mass aircraft dynamical model with models of
electrical and mechanical powertrain losses, the fuel consumed over a planned
future flight path is minimised subject to constraints on the battery, electric
motor and gas turbine. The resulting optimisation problem is used to define a
predictive energy management control law that takes into account the variation
in aircraft mass during flight. A simulation study based on a representative
100-seat aircraft with a prototype parallel hybrid electric propulsion system
is used to investigate the properties of the controller. We show that an
optimisation-based control strategy can provide significant fuel savings over
heuristic energy management strategies in this context
Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach
This paper proposes a learning-based finite control set model predictive control (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in a DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented to address one of the ongoing challenges in FCS-MPC of the converters, i.e., optimal design of the weighting coefficients appearing in the FCS-MPC objective function for each converter. A deep deterministic policy gradient method is employed to learn the optimal weighting coefficient design policy. A Markov decision method formulates the DRL problem. The DRL agent is trained for each converter in the MG, and the weighting coefficients are obtained based on reward computation with the interactions between the MG and agent. The proposed strategy is wholly distributed, wherein agents exchange data with other agents, implying a multi-agent DRL problem. The proposed control scheme offers several advantages, including preventing the dependency of the converter control system on the operating point conditions, plug-and-play capability, and robustness against the MG uncertainties and unknown load dynamics
Optimal Power Allocation in Battery/Supercapacitor Electric Vehicles using Convex Optimization
This paper presents a framework for optimizing the power allocation between a
battery and supercapacitor in an electric vehicle energy storage system. A
convex optimal control formulation is proposed that minimizes total energy
consumption whilst enforcing hard constraints on power output and total energy
stored in the battery and supercapacitor. An alternating direction method of
multipliers (ADMM) algorithm is proposed, for which the computational and
memory requirements scale linearly with the length of the prediction horizon
(and can be reduced using parallel processing). The optimal controller is
compared with a low-pass filter against an all-battery baseline in numerical
simulations, where it is shown to provide significant improvement in battery
degradation (inferred through reductions of 71.4% in peak battery power, 21.0%
in root-mean-squared battery power, and 13.7% in battery throughput), and a
reduction of 5.7% in energy consumption. It is also shown that the ADMM
algorithm can solve the optimization problem in a fraction of a second for
prediction horizons of more than 15 minutes, and is therefore a promising
candidate for online receding-horizon control
Power allocation strategy based on decentralized convex optimization in modular fuel cell systems for vehicular applications
Recently, modular powertrains have come under attentions in fuel cell vehicles to increase the reliability and efficiency of the system. However, modularity consists of hardware and software, and the existing powertrains only deal with the hardware side. To benefit from the full potential of modularity, the software side, which is related to the design of a suitable decentralized power allocation strategy (PAS), also needs to be taken into consideration. In the present study, a novel decentralized convex optimization (DCO) framework based on auxiliary problem principle (APP) is suggested to solve a multi-objective PAS problem in a modular fuel cell vehicle (MFCV). The suggested decentralized APP (D-APP) is leveraged for accelerating the computational time of solving the complex problem. Moreover, it enhances the durability and the robustness of the modular powertrain system as it can deal with the malfunction of the power sources. Herein, the operational principle of the suggested D-APP for the PAS problem is elaborated. Moreover, a small-scale test bench based on a light-duty electric vehicle is developed and several simulations and experimental validations are performed to verify the advantages of the proposed strategy compared to the existing centralized ones
Comparison of decentralized ADMM optimization algorithms for power allocation in modular fuel cell vehicles
The advanced modular powertrains are envisioned as primary part of future hybrid fuel cell vehicles (FCVs). The existing papers in the literature solely cope with the hardware side of modularity, while the software side is also vital to capitalize on the total capacity of these powertrains. Driven by this motivation, this article puts forward a comparative study of two novel decentralized convex optimization frameworks based on alternating direction method of multipliers (ADMM) to solve a multi-objective power allocation strategy (PAS) problem in a modular FCV (MFCV). The MFCV in this article is composed of two fuel cell (FC) stacks and a battery pack. Despite the existing centralized strategies for such a modular system, this manuscript proposes two decentralized PASs (Dec-PASs) based on Consensus ADMM (C-ADMM) and Proximal Jacobian ADMM (PJ-ADMM) to bridge the gap regarding the appreciation of modularity in software terms. Herein, after formulating the central PAS optimization problem, the principle of utilizing such decentralized algorithms is presented in detail. Subsequently, the performance of the proposed Dec-PASs is examined through several numerical simulations as well as experiments on a developed small-scale test bench. The obtained results illustrate that decomposition into decentralized forms enables solving the complex PAS optimization problem faster and provides modularity and flexibility. Furthermore, the proposed Dec-PASs can cope with fault and malfunction and thus augment the durability and robustness of modular powertrain systems