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Estimation of the Hot Swap Circulation Current of a Multiple Parallel Lithium Battery System with an Artificial Neural Network Model
- Source :
- Electronics, Volume 10, Issue 12, Electronics, Vol 10, Iss 1448, p 1448 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.
- Subjects :
- Battery (electricity)
TK7800-8360
Computer Networks and Communications
Computer science
02 engineering and technology
circulating current
Energy storage
Automotive engineering
equivalent circuit model (ECM)
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
Fitnet
Operating point
hot swap
020208 electrical & electronic engineering
multiple parallel
Hot swapping
artificial neural network (ANN)
021001 nanoscience & nanotechnology
Battery pack
Power (physics)
State of charge
multiple series
Hardware and Architecture
Control and Systems Engineering
Signal Processing
battery
energy storage system (ESS)
Electronics
0210 nano-technology
Voltage
Subjects
Details
- Language :
- English
- ISSN :
- 20799292
- Database :
- OpenAIRE
- Journal :
- Electronics
- Accession number :
- edsair.doi.dedup.....29793b9d35ed6da78196337ab6c6ed9e
- Full Text :
- https://doi.org/10.3390/electronics10121448