scholarly journals A Comprehensive Review of Lithium-ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

2021 ◽  
Author(s):  
Akash Samanta ◽  
Sheldon S. Williamson
Keyword(s):  
Computational Cost ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Battery Management ◽  
Cell Temperature ◽  
Temperature Estimation ◽  
Estimation Strategy ◽  
Highly Nonlinear ◽  
Fast Charging

Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, cell temperature beyond the manufacturer’s specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB such as state of charge, state of health and so on. High capacity LIB pack, used in electric vehicles and grid-tied stationary energy storage system essentially consists of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell especially at the cell core is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to the researchers and industries towards developing a highly accurate, intelligent, adaptive, easy to implement and compute efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS.

scholarly journals A Comprehensive Review of Lithium-Ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5960
Author(s):  
Akash Samanta ◽  
Sheldon S. Williamson
Keyword(s):  
Computational Cost ◽  
Lithium Ion ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Battery Management ◽  
Cell Temperature ◽  
Temperature Estimation ◽  
Estimation Strategy ◽  
Highly Nonlinear ◽  
Fast Charging

Highly nonlinear characteristics of lithium-ion batteries (LIBs) are significantly influenced by the external and internal temperature of the LIB cell. Moreover, a cell temperature beyond the manufacturer’s specified safe operating limit could lead to thermal runaway and even fire hazards and safety concerns to operating personnel. Therefore, accurate information of cell internal and surface temperature of LIB is highly crucial for effective thermal management and proper operation of a battery management system (BMS). Accurate temperature information is also essential to BMS for the accurate estimation of various important states of LIB, such as state of charge, state of health and so on. High-capacity LIB packs, used in electric vehicles and grid-tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space and weight point of view. A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to researchers and industries towards developing a highly accurate, intelligent, adaptive, easy-to-implement and computationally efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS.

scholarly journals Smart Core and Surface Temperature Estimation Techniques for Health-conscious Lithium-ion Battery Management Systems: A Model-to-Model Comparison

2021 ◽  
Author(s):  
Sumukh Surya ◽  
Akash Samanta ◽  
Sheldon Williamson
Keyword(s):  
Surface Temperature ◽  
Computational Cost ◽  
Lithium Ion ◽  
Second Order ◽  
Battery Management ◽  
Order Model ◽  
Temperature Estimation ◽  
Thermal Models ◽  
First Order ◽  
Estimation Scheme

Estimation of core and surface temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. While, it is impractical to measure core temperature using physical sensors, implementing a complex estimation strategy in on-board low-cost BMS is challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models having different levels of accuracy and complexity. Broadly, there are first-order and second-order heat capacitor-resistor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study to access suitability in online prediction and onboard BMS. The aim is to guide whether it’s worth investing towards developing a second-order model instead of a first-order model with respect to prediction accuracy considering modelling complexity, experiments required and the computational cost. Both the thermal models along with the parameter estimation scheme are modelled and simulated using MATLAB/Simulink environment. Models are validated using laboratory test data of a cylindrical 18650 LIB cell. Further, a Kalman Filter with appropriate process and measurement noise levels are used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models are analyzed for comparison purposes.

scholarly journals Effect of Sensors Sensitivity on Lithium-Ion Battery Modeled Parameters and State of Charge: A Comparative Study

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 709 ◽  
Author(s):  
Muhammad Umair Ali ◽  
Amad Zafar ◽  
Sarvar Hussain Nengroo ◽  
Sadam Hussain ◽  
Hee-Je Kim
Keyword(s):  
Lithium Ion ◽  
State Of Charge ◽  
Current Sensor ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Optimal Value ◽  
Current Transducer ◽  
Research Outcome

The accurate estimation of the state of charge (SOC) is usually acknowledged as one of the essential features in designing of battery management system (BMS) for the lithium-ion batteries (LIBs) in electric vehicles (EVs). A suitable battery model is a prerequisite for correct SOC measurement. In this work, the first and second order RC autoregressive exogenous (ARX) battery models are adopted to check the influence of voltage and current transducer measurement uncertainty. The Lagrange multiplier method is used to estimate the battery parameters. The sensitivity analysis is performed under the following conditions: Current sensor precision of ±5 mA, ±50 mA, ±100 mA, and ±500 mA and voltage sensor precision of ±1 mV, ±2.5 mV, ±5 mV, and ±10mV. The comparative analysis of both models under the perturbed environment has been carried out. The effects of the sensor’s sensitivity on the different battery structures and complexity are also analyzed. Results shows that the voltage and current sensor sensitivity has a significant influence on SOC estimation. This research outcome assists the researcher in selecting the optimal value of sensor accuracy to accurately estimate the SOC of the LIB.

New Intelligent Battery Management System for Drones

2019 ◽  
Author(s):  
Seyed Reza Hashemi ◽  
Roja Esmaeeli ◽  
Haniph Aliniagerdroudbari ◽  
Muapper Alhadri ◽  
Hammad Alshammari ◽  
...  
Keyword(s):  
Management System ◽  
Lithium Ion ◽  
High Energy ◽  
Flight Time ◽  
High Energy Density ◽  
Time Range ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Battery Management ◽  
Available Energy

Abstract Drones or Unmanned Aerial Vehicles (UAV) are the aircraft controlled remotely by radio waves or autonomously and can fly without a pilot and passengers. Demands for drones with long flight times are increasing significantly in the personal and commercial applications. One of the main issues about drones is their power management. However, these devices are powered by a high energy density lithium battery, but a flight time range could be about 20–40 min. Increasing the battery energy storage capacity to achieve more flight time is not usually a good idea due to the additional weight in drones. In order to solve this issue, an Intelligent Battery Management System (IBMS) is proposed to predict the maximum available energy of the battery pack to make the best decision for finding the closest charging station depending on different weather conditions. In this study, lithium-ion battery with lithium titanite oxide (LTO) anode, as a fast charging and fast discharging battery, is used as the drone power supply. The proposed IBMS can not only increase the performance and life of the battery system but also it can estimate the battery cells state of charge (SOC) based on a system identification method. Results show that the proposed system has an accurate estimation of the maximum available energy, and therefore accurate flight time prediction to find the best recharging node for the drone.

scholarly journals System protection for lithium-ion batteries management system: a review

2019 ◽  
Vol 13 (3) ◽  
pp. 1184 ◽  
Author(s):  
L. Rimon ◽  
Khairul Safuan Muhammad ◽  
S.I. Sulaiman ◽  
AM Omar
Keyword(s):  
Management System ◽  
Short Circuit ◽  
Lithium Ion ◽  
Cell Voltage ◽  
Battery Management System ◽  
Battery Management ◽  
Battery Cell ◽  
Safety Issues ◽  
System Protection ◽  
Faulty Cell

<span>Robustness of a battery management system (BMS) is a crucial issue especially in critical application such as medical or military. Failure of BMS will lead to more serious safety issues such as overheating, overcharging, over discharging, cell unbalance or even fire and explosion. BMS consists of plenty sensitive electronic components and connected directly to battery cell terminal. Consequently, BMS exposed to high voltage potential across the BMS terminal if a faulty cell occurs in a pack of Li-ion battery. Thus, many protection techniques have been proposed since last three decades to protect the BMS from fault such as open cell voltage fault, faulty cell, internal short circuit etc. This paper presents a review of a BMS focuses on the protection technique proposed by previous researcher. The comparison has been carried out based on circuit topology and fault detection technique</span>

scholarly journals Development of Life-cycle Detection and Extending Function for Reusing Automotive Lithium-ion Battery – Verified in 52V LiMnNiCoO2 Platform

2020 ◽  
Author(s):  
Wu-Yang Sean ◽  
Ana Pacheco
Keyword(s):  
Life Cycle ◽  
Lithium Ion Battery ◽  
Management System ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
Battery Pack ◽  
Battery Management System ◽  
Battery Management ◽  
Cycle Detection

Abstract For reusing automotive lithium-ion battery, an in-house battery management system is developed. To overcome the issues of life cycle and capacity of reused battery, an online function of estimating battery’s internal resistance and open-circuit voltage based on adaptive control theory are applied for monitoring life cycle and remained capacity of battery pack simultaneously. Furthermore, ultracapacitor is integrated in management system for sharing peak current to prolong life span of reused battery pack. The discharging ratio of ultracapacitor is adjusted manually under Pulse-Width-Modulation signal in battery management system. In case study in 52V LiMnNiCoO2 platform, results of estimated open-circuit voltage and internal resistances converge into stable values within 600(s). These two parameters provide precise estimation for electrical capacity and life cycle. It also shows constrained voltage drop both in the cases of 25% to 75% of ultracapacitors discharging ratio compared with single battery. Consequently, the Life-cycle detection and extending functions integrated in battery management system as a total solution for reused battery are established and verified.

scholarly journals Designing of Lithium - Ion Battery Pack Rechargeable on a Hybrid System with Battery Management System (BMS) for DC Loads of Low Power Applications – A Prototype Model

2021 ◽  
Vol 2089 (1) ◽  
pp. 012017
Author(s):  
Ramu Bhukya ◽  
Praveen Kumar Nalli ◽  
Kalyan Sagar Kadali ◽  
Mahendra Chand Bade
Keyword(s):  
Lithium Ion Battery ◽  
Management System ◽  
Short Circuit ◽  
Lithium Ion ◽  
Battery Pack ◽  
Prototype Model ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Batteries ◽  
Li Ion

Abstract Now a days, Li-ion batteries are quite possibly the most exceptional battery-powered batteries; these are drawing in much consideration from recent many years. M Whittingham first proposed lithium-ion battery technology in the 1970s, using titanium sulphide for the cathode and lithium metal for the anode. Li-ion batteries are the force to be reckoned with for the advanced electronic upset in this cutting-edge versatile society, solely utilized in cell phones and PC computers. A battery is a Pack of cells organized in an arrangement/equal association so the voltage can be raised to the craving levels. Lithium-ion batteries, which are completely utilised in portable gadgets & electric vehicles, are the driving force behind the digital technological revolution in today’s mobile societies. In order to protect and maintain voltage and current of the battery with in safe limit Battery Management System (BMS) should be used. BMS provides thermal management to the battery, safeguarding it against over and under temperature and also during short circuit conditions. The battery pack is designed with series and parallel connected cells of 3.7v to produce 12v. The charging and releasing levels of the battery pack is indicated by interfacing the Arduino microcontroller. The entire equipment is placed in a fiber glass case (looks like aquarium) in order to protect the battery from external hazards to design an efficient Lithium-ion battery by using Battery Management System (BMS). We give the supply to the battery from solar panel and in the absence of this, from a regular AC supply.

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