Multi-Objective Optimization of Energy Management Strategy on Hybrid Energy Storage System Based on Radau Pseudospectral Method

Liu, Yanwei; Li, Zhenye; Lin, Zhongqin; Zhao, Kegang; Zhu, Yicheng

doi:10.1109/access.2019.2935188

Cited by 27 publications

(14 citation statements)

References 20 publications

(27 reference statements)

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“…The EER ranges from roughly 10-20 for high efficiency units [109] which, given that 1 Btu per hour = 0.293 watts, correlates to a energy efficiency (η HVAC ) of 300-700%. A modified version of the heat dissipation equation for a BESS enclosure is shown in: (47) where U EN is the thermal transmittance between the enclosure and the environment, T EN is the enclosure temperature, p irr is solar irradiance, p HVAC is the ac power load of the HVAC unit, and η HVAC is the HVAC's energy efficiency. This approach assumes constant airflow and temperature in the HVAC's heat exchanger.…”

Section: B Enclosure Thermal Modelmentioning

confidence: 99%

Battery Energy Storage Models for Optimal Control

et al. 2019

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As batteries become more prevalent in grid energy storage applications, the controllers that decide when to charge and discharge become critical to maximizing their utilization. Controller design for these applications is based on models that mathematically represent the physical dynamics and constraints of batteries. Unrepresented dynamics in these models can lead to suboptimal control. Our goal is to examine the state-of-the-art with respect to the models used in optimal control of battery energy storage systems (BESSs). This review helps engineers navigate the range of available design choices and helps researchers by identifying gaps in the state-of-the-art. BESS models can be classified by physical domain: state-of-charge (SoC), temperature, and degradation. SoC models can be further classified by the units they use to define capacity: electrical energy, electrical charge, and chemical concentration. Most energy based SoC models are linear, with variations in ways of representing efficiency and the limits on power. The charge based SoC models include many variations of equivalent circuits for predicting battery string voltage. SoC models based on chemical concentrations use material properties and physical parameters in the cell design to predict battery voltage and charge capacity. Temperature is modeled through a combination of heat generation and heat transfer. Heat is generated through changes in entropy, overpotential losses, and resistive heating. Heat is transferred through conduction, radiation, and convection. Variations in thermal models are based on which generation and transfer mechanisms are represented and the number and physical significance of finite elements in the model. Modeling battery degradation can be done empirically or based on underlying physical mechanisms. Empirical stress factor models isolate the impacts of time, current, SoC, temperature, and depth-of-discharge (DoD) on battery state-of-health (SoH). Through a few simplifying assumptions, these stress factors can be represented using regularization norms. Physical degradation models can further be classified into models of side-reactions and those of material fatigue. This article demonstrates the importance of model selection to optimal control by providing several example controller designs. Simpler models may overestimate or underestimate the capabilities of the battery system. Adding details can improve accuracy at the expense of model complexity, and computation time. Our analysis identifies six gaps: deficiency of real-world data in control literature, lack of understanding in how to balance modeling detail with the number of representative cells, underdeveloped model uncertainty based risk-averse and robust control of BESS, underdevelopment of nonlinear energy based SoC models, lack of hysteresis in voltage models used for control, lack of entropy heating and cooling in thermal modeling, and deficiency of knowledge in what combination of empirical degradation stress factors is most accurate. These gaps are opportunities ...

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Section: B Enclosure Thermal Modelmentioning

confidence: 99%

Battery Energy Storage Models for Optimal Control

et al. 2019

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“…Wei et al 22 adopt it to solve the energy management problem for series HEVs, and Liu et al 23 used it for solving the energy management problem for HESS vehicles considering battery aging. However, the research objects of the above researches are vehicles without transmission mechanisms, thus the control variables are continuous.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

Liu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

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“…The Legendre-Gauss pseudospectral method was used by Zhang et al (2019) to design controllers for the trajectory optimization of unmanned helicopter formations. The convergence rate of the Legendre-Radau pseudospectral method was established by Hager et al (2015) and applied to the multiobjective optimization of the energy management strategy by Liu et al (2019). Liu et al (2015) proposed the hp pseudospectral method for solving optimal control problems.…”

Section: Introductionmentioning

confidence: 99%

Convergence for the optimal control problems using collocation at Legendre-Gauss points

Mai

2021

Transactions of the Institute of Measurement and Control

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The convergence of the novel Legendre-Gauss method is established for solving a continuous optimal control problem using collocation at Legendre-Gauss points. The method allows for changes in the number of Legendre-Gauss points to meet the error tolerance. The continuous optimal control problem is first discretized into a nonlinear programming problem at Gauss collocations by the Legendre-Gauss method. Subsequently, we prove the convergence of the Legendre-Gauss algorithm under the assumption that the continuous optimal control problem has a smooth solution. Compared with those of the shooting method, the single step method, and the general pseudospectral method, the numerical example shows that the Legendre-Gauss method has higher computational efficiency and accuracy in solving the optimal control problem.

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Multi-Objective Optimization of Energy Management Strategy on Hybrid Energy Storage System Based on Radau Pseudospectral Method

Cited by 27 publications

References 20 publications

Battery Energy Storage Models for Optimal Control

Battery Energy Storage Models for Optimal Control

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

Convergence for the optimal control problems using collocation at Legendre-Gauss points

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