Control‐relevant parameter estimation application to a model‐based PHEV power management system

Taghavipour, Amir; Masoudi, Ramin; Azad, Nasser L.; McPhee, John

doi:10.1002/oca.2320

Cited by 8 publications

(6 citation statements)

References 56 publications

(95 reference statements)

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“…As a result, the powertrain runs the vehicle, which is represented by the longitudinal dynamics, and the essential information is fed back to both high and low-level controllers. 20 During the whole driving cycle, the battery power is monitored as shown in Fig 1.…”

Section: A Control-oriented Model Of the Plug-in Hybrid Electric Vehiclementioning

confidence: 99%

“…Figure 3 shows the estimated power signal to the battery in an energy management process of the control-oriented PHEV model. 20 There are two key points in the battery model that add complications to the reduction process: (1) highly nonlinear battery differential equations coupled with algebraic relations and (2) non-deterministic behavior of the input signal. So, we perform a stochastic analysis to characterize the input to the battery model.…”

Section: A Control-oriented Model Of the Plug-in Hybrid Electric Vehiclementioning

confidence: 99%

“…The control-relevant parameter estimation is a convenient approach to overcome the challenge of deriving a relatively simple and adequately accurate control-oriented model. 20 The majority of its applications are tied to the equations time-step and the weighting functions because the cost-function is refreshed during each iteration; the optimization method has a direct impact on the results obtaining from the controller. 21,22 Smith et al 23 presented a one-dimensional electrochemistry-based lithium-ion battery model, which is computationally efficient and reliable for control systems application.…”

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confidence: 99%

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A Robust Model Order Reduction Scheme for Lithium-Ion Batteries in Control-Oriented Vehicle Models

Masoudi,

Taghavipour,

Azad

et al. 2024

J. Electrochem. Soc.

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The role of batteries in electrification of vehicles is eminent; thus, a dynamic model that represents the physics-based phenomena of the battery system at a minimum computational cost is essential in the model-based design of electrified vehicle control systems.Furthermore, robustness of the reduced-order battery model when maintaining the dominant physics-based phenomena governing the dynamic behavior of the battery system is crucial. Characterization of the power signal applied to the lithium-ion battery in the energy management controller of a plug-in hybrid electric vehicle shows that there is a dominant frequency range in the input signal to the battery. This key feature can be considered as a basis to construct a reduced-order model in which the training input is different from the original power signal. The original idea in this paper is to generate the training input by applying a low-pass filter to the white-noise random signal to maintain the same dominant frequency range observed in the original power signal. Response of the reduced-order model, constructed using the proper orthogonal decomposition, compared to the high-fidelity battery model shows promising results; a maximum relative error of 1% was obtained for the battery state of charge while simulation time was reduced by 42.9%.

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Section: A Control-oriented Model Of the Plug-in Hybrid Electric Vehiclementioning

confidence: 99%

Section: A Control-oriented Model Of the Plug-in Hybrid Electric Vehiclementioning

confidence: 99%

mentioning

confidence: 99%

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A Robust Model Order Reduction Scheme for Lithium-Ion Batteries in Control-Oriented Vehicle Models

Masoudi,

Taghavipour,

Azad

et al. 2024

J. Electrochem. Soc.

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“…The state of charge (SoC) of storage subsystem i at the end of each sampling interval should be always within acceptable limits, 40 that is,

S o C_{b, i}^{\min} \leq S o C_{b, i} (t) \leq S o C_{b, i}^{\max}, i = 1, \dots, n_{b} .

where

S o C_{b, i}^{\min}

and

S o C_{b, i}^{\max}

are minimum and maximum SoC, respectively. The

S o C_{b, i}^{\min}

determine the reliability level of the system, as it means that there is always a certain amount of energy stored in the battery for emergencies 15 .…”

Section: System Modeling and Problem Formulationmentioning

confidence: 99%

A distributed optimal power management system for microgrids with plug&play capabilities

2021

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In this article we consider the problem of managing the power flow among different components of a microgrid while ensuring that the demands of all loads are satisfied. In order to address this problem, we propose a distributed scheme where only one sensor (for each subgrid in the microgrid) broadcasts a single scalar variable though a wireless communication network (e.g., WiFi, ZigBee, cellular communication network). More precisely, in the proposed scheme each generation/storage subsystem is able to adjust its operating set‐point only making use of the feedback from the sensors. This architecture provides the system with plug&play capabilities. Interestingly enough, the proposed scheme provides the optimal power flow for the whole microgrid without any information on the demanded load. In this article we apply the proposed approach to hybrid ac/dc microgrids; however, the scheme is general and can be applied to any type of microgrid. The effectiveness of the proposed scheme is evaluated through extensive simulations and compared with a centralized optimal strategy.

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“…For several practical control issues, many scholars and engineers usually discuss the delay effect on systems due to poor control performance. Referring to Reference 1, the stability criteria of delayed systems have been extensively investigated on state‐space and frequency domains. Generally, the criteria 2‐4 have been developed for stability problem of the systems with constant delays.…”

Section: Introductionmentioning

confidence: 99%

Delay‐dependent robust control of stochastic systems with convex polynomial uncertainty

Chang

Hsu

et al. 2020

Optim Control Appl Methods

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Summary This article addresses a robust control problem of time‐varying stochastic systems with time‐delays. Through Linear Parameter Varying (LPV) modeling approach, the time‐varying parameters can be described via the convex combination. Therefore, the LPV stochastic system is interpreted by a weighting function and multiplicative noised linear systems. Furthermore, stabilization problem for the systems is investigated via Gain‐Scheduled (GS) technique to increase robustness. For the problem, some sufficient conditions are derived by Integral Lyapunov‐Krasovskii Function (ILKF) such that the conservatism caused by derivative of weighting function is ignored. Besides, an Iterative Linear Matrix Inequality algorithm is used to determine the auxiliary variable such that the problem can be solved via the convex calculation. Finally, some simulations are provided to demonstrate effectiveness and applicability of this article.

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Control‐relevant parameter estimation application to a model‐based PHEV power management system

Cited by 8 publications

References 56 publications

A Robust Model Order Reduction Scheme for Lithium-Ion Batteries in Control-Oriented Vehicle Models

A Robust Model Order Reduction Scheme for Lithium-Ion Batteries in Control-Oriented Vehicle Models

A distributed optimal power management system for microgrids with plug&play capabilities

Delay‐dependent robust control of stochastic systems with convex polynomial uncertainty

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