Abstract
A method based on fusion of multiple features is proposed to assess and accurately describe the performance degradation of lithium-ion batteries in this paper. First, the discharge voltage signal of lithium-ion batteries under real-time monitoring is analyzed from the perspective of time domain and complexity to obtain the values of multiple features. Then, the multi-feature parameters undergo a spectral regression process to reduce the number of dimensions and to eliminate redundancy, and on the basis of this regression, a Gaussian mixture model is established to model the health state of batteries. Thus, the degree of lithium-ion battery performance degradation can be quantitatively assessed using the Bayesian inference-based distance metric. A case calculation experiment is carried out to verify the effectiveness of the method proposed in this paper. The experimental results demonstrate that, compared with other assessment methods, the performance degradation assessment method proposed in this paper can be used to monitor the degradation process of lithium-ion batteries more effectively and to improve the accuracy of condition monitoring of batteries, thereby providing powerful support for making maintenance decisions.
Highlights
As a kind of ideal power supply energy, which can be reused, lithium-ion battery has been widely used in many fields
Mathematical Problems in Engineering it is advantageous to accurately reflect the state of battery performance and improve the prediction accuracy of residual life by extracting characteristic parameters from multiple perspectives and fusing them into a health index, and this paper focuses on performance degradation assessment of lithium batteries
A negative logarithmic likelihood probability (NLLP) value was used to evaluate the performance of the bearing [30]; that is, after establishing the Gaussian mixture model (GMM) model in normal state, a logarithmic likelihood value is obtained for each set of test data
Summary
As a kind of ideal power supply energy, which can be reused, lithium-ion battery has been widely used in many fields. Mathematical Problems in Engineering it is advantageous to accurately reflect the state of battery performance and improve the prediction accuracy of residual life by extracting characteristic parameters from multiple perspectives and fusing them into a health index, and this paper focuses on performance degradation assessment of lithium batteries. This is because most of these methods only process a single feature of the discharge voltage, which is not very sensitive to variations in the battery state To solve this challenge, this paper proposes a method based on the fusion of multiple features for assessing the performance degradation of lithium-ion batteries. Compared with a single parameter, the health index can characterize the performance degradation degree of lithium battery more accurately
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