scholarly journals Novel Battery Management System with Distributed Wireless and Fiber Optic Sensors for Early Detection and Suppression of Thermal Runaway in Large Battery Packs, FY13 Q4 Report, ARPA-E Program: Advanced Management Protection of Energy Storage Devices (AMPE

2013 ◽  
Author(s):  
J. Farmer ◽  
J. Chang ◽  
J. Zumstein ◽  
J. Kovotsky ◽  
F. Puglia ◽  
...  
Keyword(s):  
Energy Storage ◽  
Early Detection ◽  
Management System ◽  
Fiber Optic ◽  
Fiber Optic Sensors ◽  
Battery Management System ◽  
Battery Management ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Battery Packs

scholarly journals Fiber Optic Sensing Technologies for Battery Management Systems and Energy Storage Applications

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1397
Author(s):  
Yang-Duan Su ◽  
Yuliya Preger ◽  
Hannah Burroughs ◽  
Chenhu Sun ◽  
Paul Ohodnicki
Keyword(s):  
Energy Storage ◽  
Fiber Optics ◽  
Fiber Optic ◽  
Gas Sensing ◽  
Fiber Optic Sensors ◽  
Management Systems ◽  
Practical Implementation ◽  
Battery Management ◽  
Battery Management Systems ◽  
Battery Systems

Applications of fiber optic sensors to battery monitoring have been increasing due to the growing need of enhanced battery management systems with accurate state estimations. The goal of this review is to discuss the advancements enabling the practical implementation of battery internal parameter measurements including local temperature, strain, pressure, and refractive index for general operation, as well as the external measurements such as temperature gradients and vent gas sensing for thermal runaway imminent detection. A reasonable matching is discussed between fiber optic sensors of different range capabilities with battery systems of three levels of scales, namely electric vehicle and heavy-duty electric truck battery packs, and grid-scale battery systems. The advantages of fiber optic sensors over electrical sensors are discussed, while electrochemical stability issues of fiber-implanted batteries are critically assessed. This review also includes the estimated sensing system costs for typical fiber optic sensors and identifies the high interrogation cost as one of the limitations in their practical deployment into batteries. Finally, future perspectives are considered in the implementation of fiber optics into high-value battery applications such as grid-scale energy storage fault detection and prediction systems.

A Design Methodology for Lithium-Ion Battery Management System and its Application to an Autonomous Underwater Vehicle

2011 ◽  
Vol 383-390 ◽  
pp. 7175-7182 ◽  
Author(s):  
Chao Shen ◽  
Lei Wan
Keyword(s):  
Management System ◽  
Autonomous Underwater Vehicle ◽  
Lithium Ion ◽  
Underwater Vehicle ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Battery Packs ◽  
Non Linear System ◽  
Estimated Error

Battery management system (BMS) in autonomous underwater vehicle (AUV) not only can measure the main parameters of battery packs such as current, voltage, and temperature, but also estimate the state of charge (SOC) of battery packs. This paper proposes a broad approach for the design of battery management system. The new design can improve the cycle life and safety capability. With the model well designed, the parameters required are obtained and the SOC estimation is completed. Extended Kalman filter (EKF) was chosen to make the last estimate with the reliable battery model which was used to the non-linear system to estimate SOC and suitable for AUV applications. The experiments results prove that the data measured by battery management system have high precision and reliability. The estimated error of SOC was also small, which was better than other approaches for estimate.

Distributed Battery Management System in Battery Electric Vehicle

2011 ◽  
Vol 201-203 ◽  
pp. 2427-2430
Author(s):  
Yuan Liao ◽  
Ju Hua Huang ◽  
Qun Zeng
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicle ◽  
Management System ◽  
Lithium Ion ◽  
Battery Management System ◽  
Battery Electric Vehicle ◽  
Battery Management ◽  
Extended Kalman Filtering ◽  
Battery Packs ◽  
Master Module

According to the features of lithium ion battery packs, a distributed battery management system (BMS) for battery electric vehicle (BEV) is designed in this article. The BMS consists of a master module with several sampling modules. The kernel of master module is TMS320C2812 digital signal processor, and the kernel of sampling module is P87C591 singlechip. The main functions of master module include estimation of state of charge (SOC) and security management of lithium ion battery packs, and the main functions of sampling module include battery information collection and CAN bus based communication. SOC estimation method based on Extended Kalman filtering (EKF) theory is adopted in this article to precisely estimate the SOC of lithium ion battery packs.

A cost effective accumulator management system for electric vehicles

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Suchitra D ◽  
Rajarajeswari R ◽  
Dhruv Singh Bhati
Keyword(s):  
Electric Vehicles ◽  
Management System ◽  
Lithium Ion ◽  
Cost Effective ◽  
Battery Management System ◽  
Battery Management ◽  
Cell Parameters ◽  
Battery Design ◽  
Battery Packs ◽  
Cost Efficient

AbstractAn accumulator or battery is an energy storage cramped in an adaptable stockade. Lithium-ion batteries are commonly used in hybrid electric vehicles (HEV) and battery operated electric vehicles (BOEV) due to its eco-friendliness and increased efficiency. To maintain lithium batteries in the safe operating region and also to perform tasks like cell balancing, preventing thermal runaway, maintain the state of health, an effective battery management system (BMS) is required. The BMS should also communicate effectively between host devices and battery packs. This paper proposes a reliable, modular and cost-efficient BMS, which will emanate an alert when a fault occurs and thus preventing the battery from damage. An efficient control strategy has been proposed for charging and discharging of the battery pack. The thermal analysis of the lithium-ion battery used in this work is simulated using battery design studio (BDS) with the inclusion of a self-discharging effect. The proposed hardware setup also provides a provision for on-board diagnosis (OBD) and logging in the accumulator management system (AMS) to constantly monitor the cell parameters like voltage, current, and temperature. The live data display of AMS working is also shown during abnormal and normal conditions. Also, an attempt is made to use the design of proposed AMS for HEV.

scholarly journals Battery Management System in Electric Vehicles

2021 ◽  
pp. 8-23
Author(s):  
Rakshitha Ravi ◽  
USHA SURENDRA
Keyword(s):  
Energy Storage ◽  
Electric Vehicles ◽  
Management System ◽  
Battery Management System ◽  
Battery Management ◽  
Time Saving ◽  
Ic Engine ◽  
Smart Charging ◽  
Failure Conditions ◽  
Battery Technology

Here this document provides the data about the batteries of electric vehicles. It consists of numerous data about various energy storage methods in EVs and how it is different from energy storage of IC-engine vehicles. How electric vehicles will take over ICEngine vehicles due to advancement in battery technology and the shrink in its prices. Various types of batteries are listed in the document with their specifications. Possible future battery technology which will have more or same energy density than current gasoline fuels and also with the significant reduction in battery weights; which will make EVs cheaper than current condition. Some examples are listed showing current battery capacities of various EVs models. Some battery parameters are shown in the document with introduction to BMS (Battery Management System). Then a brief introduction about the charging of these EV batteries and its types displaying variations in charging time in different types of EVs according to their charger type and manufacturers. How DC charging is more time saving method than AC and how smart charging will help to grid in case of peak or grid failure conditions.

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