Rapid and flexible battery capacity estimation using random short-time charging segments based on residual convolutional networks

Abstract

Monitoring the capacity of lithium-ion battery is a crucial task to ensure its safety and reliability during long-term use. However, conventional capacity estimation methods heavily rely on the specially designed operating conditions or durations of charge/discharge cycles, limiting their applications in real-world operations. To address such challenges, in this paper, a fast and flexible method is proposed to accurately estimate battery capacity based on a residual convolutional neural network using only small pieces of raw measurement data. And Bayesian optimization as well as network slimming are introduced to optimize and prune the network structure. Then, the proposed model is validated in two public battery degradation data sets, containing two types of batteries and six types of charging strategies in total. It's shown that the model is flexible enough to cope with the application scenarios of different charging strategies, different sampling frequencies and different voltage ranges starting at arbitrary initial SOCs, while still achieving fast and accurate capacity estimation. The mean absolute errors on 38 LFP batteries and 8 LCO batteries with a four-fold cross validation approach are all below 0.5% throughout the whole life of the batteries. Comprehensive case studies are also carried out to investigate the influence of capacity increment size, compression ratio of input data and the depth of network on the trade-offs between the model accuracy and its practicability for wide applications.