The state-of-health (SOH) estimation of lithium-ion batteries (LIBs) is of great importance to the safety of systems. In this article, a novel ensemble learning method is proposed to accurately estimate the SOH of LIBs. A feature defined as the duration of the same charging voltage range (DSCVR) is extracted as the key health indicator for the LIB. The Pearson correlation analysis is performed to select four optimal indicators that are used as inputs of the prediction model. A random learning algorithm named extreme learning machine (ELM) is applied to extract the mapping knowledge relationship between the health indicators and the SOH due to its fast learning speed and efficient tuning mechanism. Moreover, an ensemble learning structure is proposed to reduce the prediction error of the single ELM models. A reliable decision-making rule is then designed to evaluate the credibility of the output of each single ELM model and remove the unreliable outputs, thereby significantly improving the accuracy and reliability of the estimation results. The testing results on two public data sets show that the proposed method can accurately estimate the SOH in 1 ms and is robust to the operating temperature and load profile. The average root-mean-square error (RMSE) is as low as 0.78%. The proposed method does not require any additional hardware or downtime of the system, which makes the method suitable for online practical applications.

中文翻译：

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基于集成学习的数据驱动的锂离子电池健康状况在线估计方法

锂离子电池（LIB）的健康状态（SOH）估算对于系统的安全性至关重要。在本文中，提出了一种新颖的集成学习方法，可以准确地估计LIB的SOH。提取定义为相同充电电压范围（DSCVR）持续时间的功能作为LIB的关键健康指标。进行Pearson相关分析，以选择用作预测模型输入的四个最佳指标。由于学习速度快，调整机制有效，因此采用一种名为极限学习机（ELM）的随机学习算法提取健康指标与SOH之间的映射知识关系。此外，提出了集成学习结构以减少单个ELM模型的预测误差。然后设计一个可靠的决策规则，以评估每个单个ELM模型的输出的可信度并消除不可靠的输出，从而显着提高估计结果的准确性和可靠性。在两个公共数据集上的测试结果表明，该方法可以在1 ms内准确估算SOH，并且对工作温度和负载曲线具有鲁棒性。平均均方根误差（RMSE）低至0.78％。所提出的方法不需要任何额外的硬件或系统的停机时间，这使得该方法适合于在线实际应用。在两个公共数据集上的测试结果表明，该方法可以在1 ms内准确估算SOH，并且对工作温度和负载曲线具有鲁棒性。平均均方根误差（RMSE）低至0.78％。所提出的方法不需要任何额外的硬件或系统的停机时间，这使得该方法适合于在线实际应用。在两个公共数据集上的测试结果表明，该方法可以在1 ms内准确估算SOH，并且对工作温度和负载曲线具有鲁棒性。平均均方根误差（RMSE）低至0.78％。所提出的方法不需要任何额外的硬件或系统的停机时间，这使得该方法适合于在线实际应用。