DESA: Dependable, Efficient, Scalable Architecture for Management of Large-Scale Batteries

Kim, Hahnsang; Shin, Kang G.

doi:10.1109/tii.2011.2166771

Cited by 77 publications

(38 citation statements)

References 11 publications

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“…To ensure safe and reliable operation, an effective battery management system (BMS) is required to monitor and control these cells. Much of the BMS functionalities, such as the state of charge (SOC) estimation, state of health estimation, cell monitoring and balancing techniques [3][4][5][6][7][8], have been sophisticatedly developed for applications. Nevertheless, due to the nonlinear battery characteristics and unpredictable operating conditions, accurate and reliable battery state of energy (SOE) and maximum available energy estimations still pose significant challenges.…”

Section: Introductionmentioning

confidence: 99%

Novel methods for estimating lithium-ion battery state of energy and maximum available energy

et al. 2016

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h i g h l i g h t sStudy on temperature, current, aging dependencies of maximum available energy. Study on the various factors dependencies of relationships between SOE and SOC. A quantitative relationship between SOE and SOC is proposed for SOE estimation. Estimate maximum available energy by means of moving-window energy-integral. The robustness and feasibility of the proposed approaches are systematic evaluated. Keywords: Battery management system (BMS) State of energy (SOE) State of charge (SOC) Maximum available energy a b s t r a c tThe battery state of energy (SOE) allows a direct determination of the ratio between the remaining and maximum available energy of a battery, which is critical for energy optimization and management in energy storage systems. In this paper, the ambient temperature, battery discharge/charge current rate and cell aging level dependencies of battery maximum available energy and SOE are comprehensively analyzed. An explicit quantitative relationship between SOE and state of charge (SOC) for LiMn 2 O 4 battery cells is proposed for SOE estimation, and a moving-window energy-integral technique is incorporated to estimate battery maximum available energy. Experimental results show that the proposed approaches can estimate battery maximum available energy and SOE with high precision. The robustness of the proposed approaches against various operation conditions and cell aging levels is systematically evaluated.

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Section: Introductionmentioning

confidence: 99%

Novel methods for estimating lithium-ion battery state of energy and maximum available energy

et al. 2016

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“…Gao and Ehsani [23] compared these two types of batteries considering all electric range and charge depletion range operations. Kim and Shin [24] designed a BMS for large-scale battery packs. Manenti et al [25] proposed a BMS architecture based on cell redundancy.…”

Section: B Phev/pev Battery Technologymentioning

confidence: 99%

A Survey on the Electrification of Transportation in a Smart Grid Environment

Rahimi-Eichi

Zeng

et al. 2012

IEEE Trans. Ind. Inf.

689

296

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Economics and environmental incentives, as well as advances in technology, are reshaping the traditional view of industrial systems. The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) into the market brings up many technical problems that are highly related to industrial information technologies within the next ten years. There is a need for an in-depth understanding of the electrification of transportation in the industrial environment. It is important to consolidate the practical and the conceptual knowledge of industrial informatics in order to support the emerging electric vehicle (EV) technologies. This paper presents a comprehensive overview of the electrification of transportation in an industrial environment. In addition, it provides a comprehensive survey of the EVs in the field of industrial informatics systems, namely: 1) charging infrastructure and PHEV/PEV batteries; 2) intelligent energy management; 3) vehicle-to-grid; and 4) communication requirements. Moreover, this paper presents a future perspective of industrial information technologies to accelerate the market introduction and penetration of advanced electric drive vehicles.Index Terms-Battery, charging infrastructure, communication, electric vehicle (EV), energy management, plug-in electric vehicle (PEV), plug-in hybrid electric vehicle (PHEV), smart grid, vehicle-to-grid (V2G).

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“…The BMS uses internal battery pack sensors to measure the voltages and temperatures of individual cells within the pack to ensure that all cells are charging, or discharging, at level rates and are operating at an acceptable State of Health (SoH), which is vital to optimum battery performance. This improves the overall performance of the battery pack, while reducing pack failures due to a single mismanaged cell [1]. However, BMS hardware can be costly as well as being susceptible to mechanical failures [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Battery management, which includes monitoring and health maintenance, presents a challenge since the number of battery cells and corresponding weight required to power a vehicle generally require a battery pack that is distributed across a significant part of the undercarriage, or other space of similar size. Each battery cell must be managed individually so that the group behavior of the battery pack can be adjusted to ensure maximum performance while preventing premature pack failure due to individual cell failure [1]. It is then necessary for battery packs and/or vehicles to incorporate a Battery Management System (BMS) that relies on information from each battery cell within a pack.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of a wireless battery management system (WBMS)

Shell

Henderson

Verra

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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This paper introduces a wireless battery management system (BMS) based on Bluetooth technology. With the burgeoning use of battery packs in electric and hybrid vehicles, battery management has become a significant area for improvement. Typical battery packs consist of many individual battery cells, and total-pack performance can only be optimized by ensuring level charge and discharge performance across all individual constituent cells. Current battery pack technology relies on wired sensor connections to all individual cells to track temperature and voltage of each cell, and adjust charge and discharge rates accordingly. Packs with large numbers of individual cells typically have thousands of wire terminations that are susceptible to mechanical failure. This can lead to premature pack failure. Moreover, when an individual battery cell fails under this scenario, and entire pack rebuild is required. The proposed wireless battery management minimizes the failure points in a battery pack. Furthermore, it makes individual components more readily replaced without an entire rebuild, while also eliminating the impact of system modifications on bulky wiring harnesses. The implemented system performed similarly to a wired system in comparison tests, while saving weight and significantly reducing failure points.

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DESA: Dependable, Efficient, Scalable Architecture for Management of Large-Scale Batteries

Cited by 77 publications

References 11 publications

Novel methods for estimating lithium-ion battery state of energy and maximum available energy

Novel methods for estimating lithium-ion battery state of energy and maximum available energy

A Survey on the Electrification of Transportation in a Smart Grid Environment

Implementation of a wireless battery management system (WBMS)

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