# Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning
###### Abstract  
Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries. machine learning; remaining useful life; symbolic regression +
Footnote †: journal: Medicalusing early electrochemical test data will bring novel opportunities for battery research and application. In fact, _RUL_ has been an increasingly more significant parameter in battery management systems, and it could provide predictive maintenance data for end users. The exploration for lithium battery modeling and degradation mechanisms has also become a research focus in energy storage fields [8; 9; 10]. Recently, advances in computational power, optimized algorithms, and data generation have enabled the wider application of machine-learning approaches in various projects, including battery degradation prediction [11; 12; 13; 14; 15; 16; 17; 18; 19]. For instance, [PERSON] et al. established a closed-loop optimization system using elastic network regression and Bayesian optimization to predict the battery life and improve fast charging policies, and they compressed the original experimental period of over 500 days to 16 days [20]. [PERSON] et al. also concentrated on the charging voltage curve of batteries to generate highly cycle life-correlated features. Interestingly, the influence of features on the state of health (SOH) is written into the loss function as a penalty factor, and the prior knowledge is thus embedded into the neural network to reduce the scope of parameter exploration and improve the optimization efficiency. Finally, the maximum prediction error of the SOH is less than 1.7% [21]. [PERSON] et al. proposed a battery SOH assessment method based on different degradation features extracted from the voltage, electric current, and critical time during operation. These degradation features were fused and trained by the support vector regression mapping model that linked the feature space to the SOH space. The correlation between the degradation feature and the battery testing capacity was higher than 0.7, and the mean error of health estimation was less than 0.05 [22]. [PERSON] et al. constructed a temporal convolution network (TCN) using the dilation convolution technique, and they accurately captured the local regeneration of capacity with battery capacity sequence as the input. Then, empirical mode decomposition (EMD) was introduced to process the cycle data, which eliminated the error caused by local regeneration, and improved the RMSE accuracy of _RUL_ prediction by 5% [23]. In addition to charge/discharge curves, other electrochemical test data have also been used to explore battery service performance. [PERSON] et al. used Gaussian process regression to deal with the electrochemical impedance spectroscopy of commercial lithium-ion batteries, and they successfully predicted the capacity and remaining cycle life of these batteries. By calculating the importance weight corresponding to different frequencies of the impedance spectrum, the interfacial impedance evolution represented by the low-frequency region was detected to be the focal points affecting battery capacity and degradation patterns [24].