Optimization techniques of battery packs using re-configurability: A review

Viswanathan, Vijayaragavan; Palaniswamy, Lakshimi Narayanan; Leelavinodhan, Padma Balaji

doi:10.1016/j.est.2019.03.002

Cited by 27 publications

(10 citation statements)

References 29 publications

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“…Gates may experience more dynamic and larger range of voltages, and measurement units are expected to provide higher resolution to enable reliable feedback. The reviews like [6]- [10] summarize cell modeling, estimation of SoC and SoH, control hardware and their topologies, fault accommodation, control design, scaling or granularity, and communication. Generally, the reconfiguration algorithms are strongly dependent on the circuit topology.…”

Section: A Battery Management Systemsmentioning

confidence: 99%

Review of Control Algorithms for Reconfigurable Battery Systems with an Industrial Example

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Papageorgiou

Rohde³

et al. 2021

2021 56th International Universities Power Engineering Conference (UPEC)

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Battery cells within battery energy storage systems (BESS) do not have homogeneous attributes, and the lowest capacity ones limit the performance and lifetime of the whole pack. Modern battery management systems (BMS) solve this problem with balancing, while providing the required service, and safe operation to the user. Reconfigurable battery systems (RBS) are BESSs that involve a BMS with reconfiguration. Reconfiguration uses feedback to determine the circuit switching logic. This paper presents a structured review of the control algorithms for RBSs. The RBSs are divided into groups according to their control strategies and control implementations. Finding the adequate control strategy requires well-defined objectives and control design. The control implementation focuses on physical and architectural aspects, like the reconfiguration frequency, the balancing operation and the control topology. The considerations and categories are discussed with the advantages, disadvantages and academic examples, and then an innovative industrial BMS is introduced.

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Section: A Battery Management Systemsmentioning

confidence: 99%

Review of Control Algorithms for Reconfigurable Battery Systems with an Industrial Example

Pinter

Papageorgiou

Rohde³

et al. 2021

2021 56th International Universities Power Engineering Conference (UPEC)

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“…The topologies of reconfigurable battery management systems are investigated by Viswanathan et. al., with an emphasis on load interface protocols and load scheduling optimization approaches [16]. Sanjari et.…”

Section: B Literature Reviewmentioning

confidence: 99%

Adaptive Demand Response Management System using Polymorphic Bayesian Inference Supported Multilayer Analytics

Sarin¹,

Mani²

2021

IJACSA

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The most significant limitation of stand-alone microgrid systems is the challenge of meeting unexpected additional demands. If demand exceeds the capacity of a standalone system, the system may be unable to satisfy demand. This issue is alleviated in grid-connected technology since the utility system will provide more power if it is demanded. As a result, load scheduling is an integral element of the demand response of a standalone system. There are two components to this problem. If the capacity of a battery-supported power system is restricted, for the period of time that the source is available, it will not be able to meet the entire demand. Appropriately the demand is dispersed across a period of time until the next charge becomes available. Some demands may be disregarded in order to accomplish peak load trimming or if the system is incapable of meeting demand without compromising other important technical and consumer objectives. This is a challenging assignment. This article aims to develop an Adaptive Demand Response Management System (ADRMS) capable of load scheduling and load shedding using an interwoven multidimensional Bayesian inference supported by multiple mathematical models. A two-stage hardware architecture is being developed, with the first hardware measuring demand and source capacity before sending the data to the second hardware via LPWAN for mathematical analysis. In the first phase, two approaches are used to forecast demand: Gaussian Naive Bayes Model (GNBM) and Bayesian Structural Time Series analysis. GNBM is rapid but fails to properly forecast the output when there is zero frequency error whereas BSTS can offer more precise results than GNBM but is slower. Hence two approaches are employed in tandem. The next stage is to assign demand source integration. This is accomplished using Bayesian Reinforcement Learning (BRL), which is based on a number of incentives, including anomaly, cost factors, usefulness, reliability, and size. All Bayesian models are subjected to much of the common Bayes rule, resulting in the formulation of a blended polymorphism model that reduces computing time and memory allocation, and improves processing reliability. The Isolation Forest (IF) method is used to identify and avoid vulnerable loads by determining demand anomalies. The last step employs a Dynamic Preemptive Priority Round Robin (DPPRR) algorithm for preemptive priority based load scheduling based on forecasted data to allocate the next loads to be added.

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“…Improper equalization will increase as the batteries age, leading to an effective reduction in service life of up to 10 times, for a usable period of 5 years. 24 Reconfigurable connection topologies have generated increased interest in the scientific community in recent years, 24,25 mainly due to the identified advantages that they offer compared to static connection schemes: disconnecting already damaged cells, improved overall pack reliability and usable lifetime, and easier equalization management by selective disconnection of cells. The most important feature of reconfigurable battery packs is their ability to alter the cells connection configuration dynamically, based on real-time changes in load demand.…”

Section: Reconfigurable Battery Packs For Critical Applicationsmentioning

confidence: 99%

Novel battery wear leveling method for large‐scale reconfigurable battery packs

et al. 2020

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Summary As the market and the application areas of high capacity battery energy storage systems are rapidly increasing, there is a correspondingly high interest in the topic of minimizing battery state of health degradation in battery packs. In this article, a novel method for battery management in large‐scale battery packs is introduced, aiming to minimize battery degradation by enforcing a special wear leveling (WL) policy, adapted from the flash memory arrays. Using this method in conjunction with a hybrid mathematical‐electrochemical battery model, a reconfigurable battery management system (BMS) is proposed and evaluated. The results of the performance analysis and in‐depth comparisons with other state‐of‐the‐art solution shows that the proposed method achieves significantly longer operating times for the battery packs—for example, 415% improvement over the classical BMS in the load current variation scenario. As the computing and memory requirements are relatively low, the new battery WL method can also be implemented on embedded systems with limited resources.

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Optimization techniques of battery packs using re-configurability: A review

Cited by 27 publications

References 29 publications

Review of Control Algorithms for Reconfigurable Battery Systems with an Industrial Example

Review of Control Algorithms for Reconfigurable Battery Systems with an Industrial Example

Adaptive Demand Response Management System using Polymorphic Bayesian Inference Supported Multilayer Analytics

Novel battery wear leveling method for large‐scale reconfigurable battery packs

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