Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China.
Department of Aeronautical and Automotive Engineering, Loughborough University, LE11 3TU Leicestershire, UK.
STAR Protoc. 2022 Apr 4;3(2):101272. doi: 10.1016/j.xpro.2022.101272. eCollection 2022 Jun 17.
Accurate estimates of State of Health (SoH) are critical for characterizing the aging of lithium-ion batteries. This protocol combines feature extraction and a representative machine learning algorithm (i.e., least-squares support vector machine) for SoH prediction of lithium-ion batteries. We detail the step-by-step estimation process, followed by validation of the constructed model with a maximum absolute error of 1.62%. Overall, the proposed approach can efficiently track the aging trajectory and ensure precise SoH prediction. For complete details on the use and execution of this protocol, please refer to Shu et al. (2021b).
准确估计电池健康状态（State of Health，SoH）对于描述锂离子电池的老化至关重要。本方案结合特征提取和具有代表性的机器学习算法（即最小二乘支持向量机），对锂离子电池的 SoH 进行预测。我们详细介绍了逐步估计过程，随后用最大绝对误差为 1.62%验证了构建模型的有效性。总体而言，该方法可以有效跟踪老化轨迹并确保精确的 SoH 预测。如需了解本方案的详细使用方法和执行步骤，请参考 Shu 等人（2021b）。
