Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data

Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estimation using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capacity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incremental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to determine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neural network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is validated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demonstrate that the proposed method exhibits better estimation performance with an error of less than 1.1%compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.

Zwitterionic ring-opening polymerization of macrocyclic ethyleneoxy-substituted carbonate:Access to cyclic PEG-like polycarbonate

The innovation in polymer design to rival conventional polyethylene glycol(PEG)is an important approach to achieving a more sustainable society.Here,cyclic PEG-like polycarbonates having high molecular weight(4.4–49.5 kg/mol)were enabled through zwitterionic ring-opening polymerization(ZROP)of macrocyclic carbonates(MCs)mediated by N-heterocyclic carbene(NHC).The thermodynamic behavior of polymerization depends on the ring size of monomers.During this process,the ZROP of 11-membered MC was driven by the change of enthalpy(ΔH_(p))which differed from the ZROP of 14-membered MC driven by the entropic change(ΔS_(p)).Cyclic polycarbonates depicted improved thermostability(T_(d5%)≥204℃)and higher glass transition temperatures(T_(g)>–40℃)in comparison to their linear analogues(T_(d5%)≤185℃,T_(g)~–50℃).In addition,the mechanism of ZROP of MC was addressed through computational study.A distinct mechanism of polymerization distinguishable from the well-known NHC-mediated ZROP of cyclic esters was revealed,where the zwitterion from nucleophilic addition to MC,i.e.tetrahedral intermediate,cannot be ring-opened probably due to the delocalization of negative charge on the carbonate group,but serves as an active center for the polymerization.In comparison to PEG,the attained polymer demonstrated comparable hydrophilic and biocompatible properties,as revealed by the results of contact angle and in vitro cytotoxicity studies,suggesting that cyclic polycarbonate hold the promise as the alternative of PEG.

Brief Introduction of TianHe Exascale Prototype System

Facing the challenges of the next generation exascale computing,National University of Defense Technology has developed a prototype system to explore opportunities,solutions,and limits toward the next generation Tianhe system.This paper briefly introduces the prototype system,which is deployed at the National Supercomputer Center in Tianjin and has a theoretical peak performance of 3.15 Pflops.A total of 512 compute nodes are found where each node has three proprietary CPUs called Matrix-2000+.The system memory is 98.3 TB,and the storage is 1.4 PB in total.