Distributed reconfigurable Battery System Management Architectures

Steinhorst, Sebastian; Shao, Zili; Chakraborty, Samarjit; Kauer, Matthias; Li, Shuai; Lukasiewycz, Martin; Narayanaswamy, Swaminathan; Rafique, Muhammad Usman; Wang, Qixin

doi:10.1109/aspdac.2016.7428049

Cited by 39 publications

(28 citation statements)

References 19 publications

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“…As a result, it becomes challenging to model the exact dynamic behavior of switches. In some studies, for simplicity, R DS(on) is simply ignored [25] or assumed constant [37]. However, accurate switch modeling is still worth exploiting when R DS(on) is comparable with the internal resistance of battery cells and imposes considerable influence on the total resistances.…”

Section: A Modeling Of Rbssmentioning

confidence: 99%

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Han¹,

Wik²,

Kersten³

et al. 2020

Preprint

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<div>Batteries are widely applied to the energy storage and power supply in portable electronics, transportation, power systems, communication networks, etc. They are particularly demanded in the emerging technologies of vehicle electrification and renewable energy integration for a green and sustainable society. To meet various voltage, power, and energy requirements in large-scale applications, multiple battery cells have to be connected in series and/or parallel. While battery technology has advanced significantly in the past decade, existing battery management systems (BMSs) mainly focus on state monitoring and control of battery systems packed in fixed configurations. In fixed configurations, though, the battery system performance is in principle limited by the weakest cells, which can leave large parts severely underutilized. Allowing dynamic reconfiguration of battery cells, on the other hand, allows individual and flexible manipulation of the battery system at cell, module, and pack levels, which may open up a new paradigm for battery management. Following this trend, this paper provides an overview of next-generation BMSs featuring dynamic reconfiguration. Motivated by numerous potential benefits of reconfigurable battery systems (RBSs), the hardware designs, management principles, and optimization algorithms for RBSs are sequentially and systematically discussed. Theoretical and practical challenges during the design and implementation of RBSs are highlighted in the end to stimulate future research and development.</div>

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Section: A Modeling Of Rbssmentioning

confidence: 99%

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Han¹,

Wik²,

Kersten³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…BMS architectures can be classified as centralized or decentralized and their structure (BMS electrical circuit) can be classified as static or dynamic. The main advantages and disadvantages of each architecture and structure can be found in [48]. One of the main functions of the BMS is to balance/equalize the cells that integrate the pack to maximize the usable capacity in each charge and discharge cycle.…”

Section: Storage Systemsmentioning

confidence: 99%

“…One of the main functions of the BMS is to balance/equalize the cells that integrate the pack to maximize the usable capacity in each charge and discharge cycle. In addition to maximizing the usable capacity, balancing avoids overloading and over-discharge problems that result in reduced cell life and, in extreme cases, complete destruction, possibly an explosion [48]. Figure 6 illustrates the implemented BMS: a centralized architecture with a balancing methodology called switched shunt resistor.…”

Section: Storage Systemsmentioning

confidence: 99%

Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

et al. 2019

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Standalone microgrids with photovoltaic (PV) solutions could be a promising solution for powering up off-grid communities. However, this type of application requires the use of energy storage systems (ESS) to manage the intermittency of PV production. The most commonly used ESSs are lithium-ion batteries (Li-ion), but this technology has a low lifespan, mostly caused by the imposed stress. To reduce the stress on Li-ion batteries and extend their lifespan, hybrid energy storage systems (HESS) began to emerge. Although the utilization of HESSs has demonstrated great potential to make up for the limitations of Li-ion batteries, a proper power management strategy is key to achieving the HESS objectives and ensuring a harmonized system operation. This paper proposes a novel power management strategy based on an artificial neural network for a standalone PV system with Li-ion batteries and super-capacitors (SC) HESS. A typical standalone PV system is used to demonstrate and validate the performance of the proposed power management strategy. To demonstrate its effectiveness, computational simulations with short and long duration were performed. The results show a minimization in Li-ion battery dynamic stress and peak current, leading to an increased lifespan of Li-ion batteries. Moreover, the proposed power management strategy increases the level of SC utilization in comparison with other well-established strategies in the literature.

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“…Lithium-ion batteries are widely used in electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) owing to their high energy density, high power density, low self-discharge rate, and long cycle life [1,2]. The energy and power of a single lithium ion cell is far from being sufficient for vehicular use, thus, a multitude of cells are connected in parallel or in series as a battery module, and tens or hundreds of modules are connected in series as a battery pack in EVs and PHEVs [3]. However, the cell-to-cell variations, intrinsic or external, induces significant distributions of state of charge (SOC) and temperature among these battery modules.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Efficient Estimation of Lithium-Ion Battery State of Charge with Alternate Adaptive Extended Kalman Filter and Ampere-Hour Counting Methods

Liu

Zhang

et al. 2019

Energies

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State of charge (SOC) estimation is a key issue in battery management systems. The challenge lies in balancing the trade-off between accuracy and computation cost. To this end, we propose an alternate method by combining the ampere-hour integral (AHI) method which has low computation cost, and the adaptive extended Kalman filter (AEKF) method, which has high accuracy. The technical viability of this alternate method is verified on a LiMnO2-LiNiO2 battery module with a nominal capacity of 130 Ah under the New European Driving Cycle (NEDC) condition. Drifts in current and voltage measurement are considered. The experimental results show that the absolute SOC error using the AHI method monotonously increases from 0% to 7.2% with the computation time of 10 s while the calculation time is obtained on a ThinkPad E450 PC with an Intel Core i7-5500U CPU @2.40 GHz and 16.0 GB RAM. The absolute SOC error of the AEKF method maintains within 3.5% with the computation time of 49 s. Therefore, the alternate method almost maintains the same SOC accuracy compared to the AEKF method which reduces the maximum absolute SOC error by 50% compared to the AHI method. Therefore, the alternate method almost has the same computation time compared with the AHI method which reduces the computation time by nearly 75% compared to the AEKF method.

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Distributed reconfigurable Battery System Management Architectures

Cited by 39 publications

References 19 publications

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Next-Generation Battery Management Systems: Dynamic Reconfiguration

Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

Accurate and Efficient Estimation of Lithium-Ion Battery State of Charge with Alternate Adaptive Extended Kalman Filter and Ampere-Hour Counting Methods

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