Accurate state-of-health (SOH) estimation is critical to guarantee the safety, efficiency and reliability of battery-powered applications. Most SOH estimation methods focus on the 0-100\% full state-of-charge (SOC) range that has similar distributions. However, the batteries in real-world applications usually work in the partial SOC range under shallow-cycle conditions and follow different degradation profiles with no labeled data available, thus making SOH estimation challenging. To estimate shallow-cycle battery SOH, a novel unsupervised deep transfer learning method is proposed to bridge different domains using self-attention distillation module and multi-kernel maximum mean discrepancy technique. The proposed method automatically extracts domain-variant features from charge curves to transfer knowledge from the large-scale labeled full cycles to the unlabeled shallow cycles. The CALCE and SNL battery datasets are employed to verify the effectiveness of the proposed method to estimate the battery SOH for different SOC ranges, temperatures, and discharge rates. The proposed method achieves a root-mean-square error within 2\% and outperforms other transfer learning methods for different SOC ranges. When applied to batteries with different operating conditions and from different manufacturers, the proposed method still exhibits superior SOH estimation performance. The proposed method is the first attempt at accurately estimating battery SOH under shallow-cycle conditions without needing a full-cycle characteristic test.
翻译:精确的健康状态(SOH)估计对于保证电池驱动的应用的安全、效率和可靠性至关重要。大多数SOH估计方法都集中在0-100\%的充满状态(SOC)范围内,其分布相似。然而,现实世界中的电池通常在浅循环条件下的部分SOC范围内工作,并且遵循不同的退化曲线,没有标记数据可用,因此SOH估计具有挑战性。为了估计浅循环电池的SOH,提出了一种新的无监督深度转移学习方法,使用自注意力蒸馏模块和多核最大均值距离技术来连接不同领域。所提出的方法从充电曲线中自动提取领域特异性特征,将大规模标记的完整循环的知识转移至未标记的浅循环。CALCE和SNL电池数据集用于验证所提出方法在不同SOC范围、温度和放电速率下估计电池SOH的有效性。所提出的方法实现了小于2\%的均方根误差,并且在不同SOC范围内胜过其他转移学习方法。当应用于不同制造商的电池以及不同的操作条件下,所提出的方法仍然表现出优异的SOH估计性能。所提出的方法是准确估计浅循环条件下电池SOH的首个尝试,无需进行完整循环特性测试。