The Design Of Energy Monitoring System About Lead-acid Battery

The energy monitoring on lead-acid batteries is an important part in batterymanagement system. It mainly including the real-time collection of parameters such asworking voltage, charging and discharging current, temperature and the SOC predictionof batteries. By the monitoring of lead-acid battery energy, it can avoid the excessivecharging and discharging effectively and improve the life of lead-acid batteries to a largedegree which is of great social significance and economic value no matter for the energyconservation or the environmental protection.Based on the internal structure and working mechanism of lead-acid batteries, thisstudy analyzes the working characteristics of lead-acid battery and the external factorsthat affect its normal work. It also demonstrates that the SOC forecast level is theimportant influence to the remaining power of the lead-acid battery so as to know thecharging and discharging degree of it. Meanwhile, this study analyzes the commonlyused SOC prediction method at home and abroad and compares the effectiveness and theadvantages and disadvantages of each.Since it is difficult to establish a mathematical model of lead-acid battery and topredict the accuracy of SOC, this study discussed the nonlinear of the battery workingsituation and puts forward a SOC prediction scheme which is based on the fuzzyC-Means clustering algorithm. Traditional fuzzy control rules are mainly from theexperts’ and operators’ experience and can’t fully react the essential characteristics of theprediction system. With the introducing data classification of fuzzy C-Means clusteringalgorithm, this study analyzes how to generate fuzzy control rules targetly, reduce thegeneration of redundant control rules and measured data sample to verify the fuzzyC-Means clustering algorithm, can get good results. Besides, through the Matlabsimulation experiments, it verifies the accuracy of the fuzzy prediction program.The study finally introduces an energy monitoring system of the lead-acid batteries.designed the hardware circuit and writed the software. Combining the real-time datacollection and the prediction program, it estimates the SOC of lead-acid batteries andcompares the prediction with the discharging experimental results. We can get theconclusion that accuracy to the testing result of the working voltage reaches0.01V, theerror of SOC estimation is less than5%, which meets the description Lead-acid batteryenergy monitoring system mentioned in this article has good practicality and some promotional value.