| 引用本文: | 毛 玲,温佳林,赵晋斌,董 浩.基于集成ELM的锂离子电池充电截止电压下的SOC和SOH联合估计[J].电力系统保护与控制,2023,51(11):86-95.[点击复制] |
| MAO Ling,WEN Jialin,ZHAO Jinbin,DONG Hao.Joint estimation of SOC and SOH at lithium-ion battery charging cut-off voltagebased on an ensemble extreme learning machine[J].Power System Protection and Control,2023,51(11):86-95[点击复制] |
|
| 摘要: |
| 充电截止电压是大多数电动汽车用户充电都会经历的电压点。针对传统安时积分法忽略初始容量误差和电池老化等一系列待优化的问题，提出了双层集成极限学习机(extreme learning machine, ELM)算法，实现锂离子电池充电截止电压下的荷电状态(state of charge, SOC)和健康状态(state of health, SOH)联合估计。首先，提取易测的电池健康特征(health indicator, HI)，采用集成极限学习机映射HI及充电所需时间与SOH之间的关系。其次，用测得的HI估计难以在线测量的充电所需时间，对充电截止电压下安时积分法的SOC进行在线修正。该方法充分考虑了电动汽车用户初始充电状态的不确定性，指导电动汽车用户合理充电。此外，通过选择合适的集成ELM模型集成度，解决了单个ELM模型输出不稳定的问题。最后，选用NASA和CALCE数据集进行实验验证。验证结果表明，锂离子电池充电截止电压下SOC的估计均方根误差均小于1.5%，集成ELM相比于其他常见算法具有较高的训练、测试精度和较短的预测时间。 |
| 关键词: 锂离子电池 荷电状态 健康状态 健康特征 集成极限学习机 |
| DOI：10.19783/j.cnki.pspc.236010 |
| 投稿时间：2022-08-15修订日期：2023-01-21 |
| 基金项目:国家自然科学基金项目资助(52177184) |
|
| Joint estimation of SOC and SOH at lithium-ion battery charging cut-off voltagebased on an ensemble extreme learning machine |
| MAO Ling,WEN Jialin,ZHAO Jinbin,DONG Hao |
| (College of Electrical Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China) |
| Abstract: |
| The charging cut-off voltage is the voltage point that most electric vehicles (EVs) will experience during charging. There is a series of problems to be optimized such as ignoring the initial capacity error and battery aging by the traditional ampere-hour integration method. A two-layer ensemble extreme learning machine (Ensemble ELM) algorithm is proposed to realize the joint estimation of SOC and SOH under the charging cut-off voltage of lithium-ion batteries. First, this study extracts the health indicator (HI), which is easily measured, and establishes the model between HI, charging time and SOH based on an Ensemble ELM. Second, the easily measured HI is used to estimate the charging time, something that is difficult to measure online. The online SOC correction based on the ampere-hour integration method is realized at the charging cut-off voltage. This method fully considers the uncertainty of the initial charging state of EVs, and can guide EV users to charge reasonably. In addition, the problem of output instability of a single ELM model is solved by selecting appropriate integration degree of the ensemble ELM model. Finally, the proposed method is tested on NASA and CALCE datasets. The results show that the root-mean-square-error (RMSE) of SOC estimation is less than 1.5% for a lithium-ion battery at charging cut-off voltage. Compared with other common algorithms, the ensemble ELM shows a higher training and test accuracy with short estimation time. |
| Key words: lithium-ion battery state of charge state of health health indicator ensemble extreme learning machine |
