【摘要】 阀控式铅酸蓄电池作为配电终端的后备电源，为电力系统的安全保驾护航。在实际应用中，由于电池质量差异及后期维护问题，蓄电池在寿命上存在较大的差异，为终端设备在电网故障下的后备工作埋下了隐患。针对配电终端设备中的蓄电池寿命评估，分析了铅酸蓄电池的工作原理以及影响铅酸蓄电池寿命的关键因素，建立了 LS-SVM 蓄电池寿命评估模型。为了保证评估模型的精准度，引入并改进粒子群算法来对蓄电池评估模型参数进行寻优。通过测试数据对评估模型的训练，以及样本数据对评估模型的测验表明，该电池寿命评估模型的准确度较高，能够较好地反应阀控式铅酸蓄电池组的寿命和状态。更多还原

【Abstract】 Valve-regulated lead-acid batteries are used as the backup power supply for the power distribution terminal to escort the safety of the power system.In practice,due to battery quality differences and later maintenance issues,there are large differences in the lifespan of terminal batteries,which creates hidden dangers for the backup work of terminal equipment under grid failures.Aiming at battery life evaluation in power distribution terminal equipment,this paper proposes an improved least square support vector machine (LS-SVM) valve-regulated lead-acid battery life evaluation model based on particle swarm optimization.On this basis,the working principle of lead-acid batteries and the key factors affecting the life of lead-acid batteries are analyzed,and the LS-SVM battery life evaluation model is established.In order to ensure the accuracy of the evaluation model,particle swarm optimization is introduced and improved to optimize the parameters of the battery evaluation model.The training of the evaluation model by the test data and the test of the evaluation model by the sample data show that the battery life evaluation model has high accuracy and can better reflect the life and status of the valve-regulated lead-acid battery pack.更多还原