Battery energy storage system expands the flexibility of the electricity grid, which facilitates the extensive usage of renewable energies in industrial applications. In order to ensure the techno-economical reliability of the battery energy storage system, managing the lifespan of each battery is critical. In this paper, a novel evolutionary framework is proposed to estimate the Lithium-ion battery state of health, which uniformly optimizes the two key processes of establishing a data driven estimator. The features in the degradation process of a battery are conveniently measured by a group of current pulses, which last only few seconds. The proposed evolutionary framework selects the most efficient combination of the short-term features from the current pulse test, and guarantees an optimal training process simultaneously. A hybrid encoding technology is applied to mix the feature extraction and the parameters of support vector regression in one chromosome. With the benefit of the proposed evolutionary framework, the battery state of health is estimated by using support vector regression and genetic algorithm in a more efficient way. A mission profile corresponding to batteries providing the primary frequency regulation service to the power system is used to cycle two Lithium-ion batteries for the validation of the proposed method.

电池储能系统扩展了电网的灵活性，从而促进了可再生能源在工业应用中的广泛使用。为了确保电池储能系统的技术经济可靠性，管理每个电池的寿命至关重要。在本文中，提出了一种新颖的进化框架来估计锂离子电池的健康状态，该框架统一地优化了建立数据驱动估计器的两个关键过程。电池退化过程中的特征可以通过仅持续几秒钟的一组电流脉冲方便地进行测量。提出的进化框架从当前的脉冲测试中选择短期特征的最有效组合，并同时保证最佳的训练过程。一种混合编码技术被应用于在一个染色体中混合特征提取和支持向量回归的参数。利用提出的进化框架的好处，可以通过使用支持向量回归和遗传算法以更有效的方式来估计电池的健康状态。对应于向电力系统提供主要频率调节服务的电池的任务配置文件用于循环两个锂离子电池，以验证所提出的方法。