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Design Of Batteries Capacity Detection System Based On Neural Network

Posted on:2016-07-12Degree:MasterType:Thesis
Country:ChinaCandidate:G C PanFull Text:PDF
GTID:2272330461476497Subject:Communication and Information System
Abstract/Summary:PDF Full Text Request
The batteries are facility of military portable communications equipment, and are basic component to ensure communication equipment work normally. The state of charge (SOC) of batteries plays a direct impact on the performance of communication equipment, and relates to ensure the communication unimpeded and combat mission complete successfully.In order to meet the requirement of military during peacetime and wartime, military communication batteries are prone to appear the following problems when they get a lot of use in the armed forces:maintain the batteries with extra electric charge or unachieved the maintenance cycle, or continue to use the batteries with poor performance of electric charge or failure of electric charge. These issues will affect the life cycle of batteries and the normal use of equipment. The existing equipments for detecting SOC of batteries are unsatisfactory because of its low accuracy and low efficiency. Therefore, we need a device to detect the SOC of batteries quickly and accurately, and this method will play an important impact on the performance and optimization of batteries. Due to the variety of military communication batteries and updating quickly, the detection device should have an easy upgrading and updating feature. Based on the characteristics of military communication batteries, we design a batteries capacity detection system to meet the above requirement.In this paper, we design a batteries capacity detection system, do deep research on the design scheme and implementation methods of system, and propose a rapid detection method for the SOC of normal military communication batteries. Mainly include the following aspects:(1) This paper designs a combined neural network for detecting communication equipment batteries capacity which is much more accurate. The combined neural network is composed of back propagation (BP) and extreme learning machine (ELM) neural network. Adopt the method of multi-stage detection detect rapidly the SOC and remaining capacity of batteries.(2) Design and implement a batteries capacity detection system. The system is composed of the batteries charge testing equipment and data processing subsystem. Data are transmitted between the batteries charge testing equipment and data processing subsystem through serial communication. The SOC detection module in system can achieve updating according to the data samples of batteries, and achieve upgrading of system.(3) Test the batteries capacity detection system. The testing result is accurate and has tiny difference, satisfying usage requirement.The batteries capacity detection system designed in this paper satisfies the design requires, solves the specific problems in detecting the SOC of batteries and realizes the quick detection of military communication batteries capacity. Therefore, this system has a pretty practical application value.
Keywords/Search Tags:Batteries, Capacity Detection System, SOC, Combined Neural Network, Multi-stage Detection, Batteries Charge Testing Equipment
PDF Full Text Request
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