Transfer State of Health Estimation Based on Cross-Manifold Embedding

The data-driven approach is currently a research hotspot of battery state of health (SOH) estimation. Such methods have advantages in nonlinear fitting; no human intervention is required in their implementation process. In existing research, general data-driven models are developed for specific battery objects. However, different battery objects need to be dealt with in practical applications, and the batteries may have different characteristics. To make SOH estimates for various battery types, electric vehicle maintainers usually require a model to have portability. However, the general machine learning methods are based on the data consistency assumption. The differences in the battery characteristics make the model migration difficult. To address this issue, we propose a novel cross-manifold transfer learning method. This method obtains a small amount of data from the target battery, and at the same time, brings relevant information from related tasks through cross manifold embedding, thereby achieving small sample SOH estimation. Experimental results show that traditional machine learning methods may suffer serious over-fitting problems when the training and target objects are very different. With the cross manifold embedding method, the knowledge learned by data-driven models can be well generalized to unseen battery objects. In this way, a data-driven model can perform a practical SOH estimation with a small amount of target data.