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State of health (SOH) estimation for lithium-ion battery modules with cellsconnected in parallel is a challenging problem, especially with cell-to-cellvariations. Incremental capacity analysis (ICA) and differential voltageanalysis (DVA) are effective at the cell level, but they cannot be directlyapplied to module-level SOH estimation, when only module-level measurements areavailable. This paper proposes a new method and demonstrates that, withmultiple features systematically selected from the module-level ICA and DVA,the module-level SOH can be estimated with high accuracy and confidence in thepresence of cell-to-cell variations. First, an information theory-based featureselection algorithm is proposed to find an optimal set of features formodule-level SOH estimation. Second, a relevance vector regression (RVR)-basedmodule-level SOH estimation model is proposed to provide both point estimatesand three-sigma credible intervals while maintaining model sparsity.Experimental datasets are used to illustrate and evaluate the proposed method.With more selected features incorporated, the proposed method achieves betterestimation accuracy and higher confidence at the expense of higher modelcomplexity. This trade-off is explored through a case study. When applied to alarge experimental dataset, the proposed method and the resulting sparse modellead to module-level SOH estimates with 0.5% root-mean-square errors and 1.5%average three-sigma values. With all the optimization and training processescompleted offboard, the proposed method has low computational complexity foronboard implementations.

State of Health Estimation for Battery Modules with Parallel-Connected Cells Under Cell-to-Cell Variations