锂电池参数采集与综合管理

发布时间：2018-06-20 05:12

本文选题：锂电池 + 参数采集　；参考：《河北工业大学》2015年硕士论文

【摘要】：由于能源危机与环境污染的影响,各国政府越来越重视新能源的开发与利用。锂电池作为一种储能装置被研究人员广泛应用于机器人与电动汽车上。针对锂电池参数采集与综合管理的要求,需要实时监测锂电池的状态。本文以PIC18F45K80主控芯片,完成了以下主要任务:首先搭建了锂电池参数采集平台,其中分别以LTC6802电压采集芯片、霍尔元件ACS712、热敏电阻完成锂电池电压、电流、温度的采集,实时采集锂电池在工作时的电压、电流、温度以及剩余电量(SOC)的变化,并且通过串口通信将其在上位机上显示。通过对电池参数的监控,能根据系统的过压、过流、过温等故障进行合理管理,并及时提醒用户切断用电设备,延长了电池的寿命,提高系统的安全性。其次,将采集到的锂电池参数信息进行保存,根据数据综合分析锂电池的电压、内阻、容量以及库伦效率等基本性能,研究不同放电倍率对其的影响。最后采用BP神经网络对锂电池SOC进行估算。以BP网络的理论为基础,通过大量的实验,确定了BP网络的各层结构、神经元数、训练函数等。同时为了提高网络训练的效果,对训练集进行大量的实验,逐步完善训练集的结构,最终以MATLAB为平台,进行网络训练实验,并用训练好的网络对测试样本进行SOC估算,实验取得了较好的效果。通过基于PIC18F45K80的锂电池参数采集与综合管理系统的研究表明:本系统能够准确的实现锂电池电压、电流、温度等信息的采集,硬件采集模块与LABVIEW上位机具有良好的通信效果,并且利用采集到的数据结合MATLAB实现了锂电池剩余电量的估算,对锂电池的管理较为完善,达到了设计要求。
[Abstract]:Due to the impact of energy crisis and environmental pollution, governments in various countries pay more and more attention to the development and utilization of new energy. As a kind of energy storage device, lithium battery is widely used in robots and electric vehicles. It is necessary to monitor the status of lithium battery in real time according to the requirement of parameter acquisition and integrated management. The main tasks of this paper are as follows: firstly, a lithium battery parameter acquisition platform is built, in which LTC6802 voltage acquisition chip, Hall element ACS712, thermistor completes the acquisition of lithium battery voltage, current and temperature. The changes of voltage, current, temperature and residual power of the lithium battery are collected in real time and displayed on the host computer by serial communication. By monitoring the battery parameters, the system can be reasonably managed according to the overvoltage, overcurrent and over-temperature faults, and the users should be reminded to cut off the electric equipment in time, thus prolonging the battery life and improving the safety of the system. Secondly, the collected lithium battery parameter information is saved, and the basic performance of lithium battery such as voltage, internal resistance, capacity and Coulomb efficiency are analyzed synthetically according to the data, and the influence of different discharge rate on lithium battery is studied. Finally, BP neural network is used to estimate the SOC of lithium battery. Based on the theory of BP network, the structure of each layer, the number of neurons and the training function of BP network are determined by a large number of experiments. At the same time, in order to improve the effect of network training, a large number of experiments are carried out on the training set, and the structure of the training set is improved step by step. Finally, the network training experiment is carried out on the platform of MATLAB, and the SOC of the test sample is estimated by the trained network. The experiment has achieved good results. The research of Lithium battery parameter acquisition and integrated management system based on PIC18F45K80 shows that the system can accurately realize the acquisition of lithium battery voltage, current, temperature and so on. The hardware acquisition module has good communication effect with LabVIEW host computer. The residual power of lithium battery is estimated by using the collected data and MATLAB. The management of lithium battery is perfect and the design requirement is met.
【学位授予单位】：河北工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM912

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