Dynamic model of lithium polymer battery – Load resistor method for electric parameters identification

Gandolfo, Daniel; Brandão, Alexandre Santos; Patiño, Daniel; Molina, Marcelo G.

doi:10.1016/j.joei.2014.10.004

Cited by 42 publications

(14 citation statements)

References 28 publications

(32 reference statements)

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“…Such constants can be obtained from discharge tests with real batteries, as presented in [40], or applying the data-sheet values when available, and used in analytical evaluations to determine the SoC of the battery. The SoC of the battery can be calculated from the relation of the unavailable charge of the battery, q un (t) = (1 − c) · δ(t), and also from Equation (1) along with the discharge current, i(t), as follows [4]:…”

Section: Kinetic Battery Model (Kibam)mentioning

confidence: 99%

“…For instance, this type of information is highly relevant, whenever energy-aware algorithms have to be implemented in the sensor nodes. However, estimating the battery lifetime in WSN nodes is a difficult task, as several factors influence their operation (e.g., chemical composition of the battery itself, operating temperature and discharge current) [1], resulting in a non-linear behavior over time [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Lifetime of Wireless Sensor Network Nodes through the Use of Embedded Analytical Battery Models

Rodrigues

Montez

Budke

et al. 2017

JSAN

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Abstract:The operation of Wireless Sensor Networks (WSNs) is subject to multiple constraints, among which one of the most critical is available energy. Sensor nodes are typically powered by electrochemical batteries. The stored energy in battery devices is easily influenced by the operating temperature and the discharge current values. Therefore, it becomes difficult to estimate their voltage/charge behavior over time, which are relevant variables for the implementation of energy-aware policies. Nowadays, there are hardware and/or software approaches that can provide information about the battery operating conditions. However, this type of hardware-based approach increases the battery production cost, which may impair its use for sensor node implementations. The objective of this work is to propose a software-based approach to estimate both the state of charge and the voltage of batteries in WSN nodes based on the use of a temperature-dependent analytical battery model. The achieved results demonstrate the feasibility of using embedded analytical battery models to estimate the lifetime of batteries, without affecting the tasks performed by the WSN nodes.

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Section: Kinetic Battery Model (Kibam)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating the Lifetime of Wireless Sensor Network Nodes through the Use of Embedded Analytical Battery Models

Rodrigues

Montez

Budke

et al. 2017

JSAN

View full text Add to dashboard Cite

show abstract

“…With the different dynamic behavior of the batteries and their topology, specific SOC algorithms are sometimes required. There have been many development and research works in recent years to improve SOC estimation accuracy [13,[15][16][17][18]. Firstly, the standard measurement-based estimation approaches, such as the coulomb-counting or This section encompasses the design and development of a smart LIB battery-power system for SOC estimation, intelligent fault diagnosis and protection for a typical energy-storage module consisting of a 36 V battery pack module with 12-cell series LIBs (ANR26650M1-B) that can be scaled up to 120 cells in series.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability for Electric Vehicles

et al. 2017

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Lithium-ion battery (LIB) power systems have been commonly used for energy storage in electric vehicles. However, it is quite challenging to implement a robust real-time fault diagnosis and protection scheme to ensure battery safety and performance. This paper presents a resilient framework for real-time fault diagnosis and protection in a battery-power system. Based on the proposed system structure, the self-initialization scheme for state-of-charge (SOC) estimation and the fault-diagnosis scheme were tested and implemented in an actual 12-cell series battery-pack prototype. The experimental results validated that the proposed system can estimate the SOC, diagnose the fault and provide necessary protection and self-recovery actions under the load profile for an electric vehicle.

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“…The reasoning behind the use of δ is to capture the non-linear capacity variation of the battery [32]. Thereby, KiBaM can model two important battery effects:

Rate capacity refers to the applied discharge current intensity, i.e., a larger discharge current implies faster battery discharges and, therefore, reduces its lifetime.

…”

Section: Introductionmentioning

confidence: 99%

A Temperature-Dependent Battery Model for Wireless Sensor Networks

Rodrigues

Montez

Moraes

et al. 2017

Sensors

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Energy consumption is a major issue in Wireless Sensor Networks (WSNs), as nodes are powered by chemical batteries with an upper bounded lifetime. Estimating the lifetime of batteries is a difficult task, as it depends on several factors, such as operating temperatures and discharge rates. Analytical battery models can be used for estimating both the battery lifetime and the voltage behavior over time. Still, available models usually do not consider the impact of operating temperatures on the battery behavior. The target of this work is to extend the widely-used Kinetic Battery Model (KiBaM) to include the effect of temperature on the battery behavior. The proposed Temperature-Dependent KiBaM (T-KiBaM) is able to handle operating temperatures, providing better estimates for the battery lifetime and voltage behavior. The performed experimental validation shows that T-KiBaM achieves an average accuracy error smaller than 0.33%, when estimating the lifetime of Ni-MH batteries for different temperature conditions. In addition, T-KiBaM significantly improves the original KiBaM voltage model. The proposed model can be easily adapted to handle other battery technologies, enabling the consideration of different WSN deployments.

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Dynamic model of lithium polymer battery – Load resistor method for electric parameters identification

Cited by 42 publications

References 28 publications

Estimating the Lifetime of Wireless Sensor Network Nodes through the Use of Embedded Analytical Battery Models

Estimating the Lifetime of Wireless Sensor Network Nodes through the Use of Embedded Analytical Battery Models

Design and Implementation of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability for Electric Vehicles

A Temperature-Dependent Battery Model for Wireless Sensor Networks

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