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.

Modeling the Diffusion of New Energy Vehicles in China Considering Subsidy Policies

The new energy vehicle(NEV)subsidy policy introduced in China in 2013 has significantly boosted the adoption and sales of NEVs,with sales increasing more than 40-fold.However,the mechanisms by which subsidy policies influence the diffusion of NEVs in China remain unclear,posing challenges for governments to design future strategies.Thus,the primary objective of this paper is to empirically examine the impact of subsidy policy on the diffusion of new energy vehicles and to forecast future development trends using the grey Bass model,a predictive model suited for new product adoption forecasting.Our findings suggest that while the sales of NEVs in China will continue to rise,the growth rate will slow.Key milestones include the first inflection points for new energy vehicles and battery electric vehicles,anticipated in 2025 and 2024 respectively,with peak sales expected in 2028 and 2027.These insights are crucial for manufacturers,enabling them to adjust their production strategies timely and enhance their resilience in the market.