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.

A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management,contributing to the reliability and longevity of batterypowered systems.However,complex operating conditions,coupling cell-to-cell inconsistency,and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation.To address these issues,this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data.Unlike traditional data-driven methods that lack interpretability,the hierarchical data-driven framework unveils the“mechanism”of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets.A generalized feature matrix is devised without requiring all cell voltages,significantly reducing the computational cost and memory resources.The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models,effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation.Using only 10%of degradation data,the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods,achieving mean absolute percentage errors of 0.608%,0.601%,and 1.128%for three battery packs whose degradation load profiles represent real-world operating conditions.Its high accuracy,adaptability,and robustness indicate the potential in different application scenarios,which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.

Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region.This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression.General health indicators are extracted from the partial discharge process.The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction.The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic.Besides,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction.Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging experiment.Thus,it largely reduces the time and labor for battery pack investigation.The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.

Analysis on the capacity degradation mechanism of a series lithium-ion power battery pack based on inconsistency of capacity

The lithium-ion battery has been widely used as an energy source. Charge rate, discharge rate, and operating tem- perature are very important factors for the capacity degradations of power batteries and battery packs. Firstly, in this paper we make use of an accelerated life test and a statistical analysis method to establish the capacity accelerated degradation model under three constant stress parameters according to the degradation data, which are charge rate, discharge rate, and operating temperature, and then we propose a capacity degradation model according to the current residual capacity of a Li-ion cell under dynamic stress parameters. Secondly, we analyze the charge and discharge process of a series power battery pack and interpret the correlation between the capacity degradations of the battery pack and its charge/discharge rate. According to this cycling condition, we establish a capacity degradation model of a series power battery pack under inconsistent capacity of cells, and analyze the degradation mechanism with capacity variance and operating temperature difference. The comparative analysis of test results shows that the inconsistent operating temperatures of cells in the series power battery pack are the main cause of its degradation; when the difference between inconsistent temperatures is narrowed by 5 ℃, the cycle life can be improved by more than 50%. Therefore, it effectively improves the cycle life of the series battery pack to reasonably assemble the batteries according to their capacities and to narrow the differences in operating temperature among cells.

Thermal Management of Air-Cooling Lithium-Ion Battery Pack

Lithium-ion battery packs are made by many batteries, and the difficulty in heat transfer can cause many safety issues. It is important to evaluate thermal performance of a battery pack in designing process. Here, a multiscale method combining a pseudo-two-dimensional model of individual battery and three-dimensional computational fluid dynamics is employed to describe heat generation and transfer in a battery pack. The effect of battery arrangement on the thermal performance of battery packs is investigated. We discuss the air-cooling effect of the pack with four battery arrangements which include one square arrangement, one stagger arrangement and two trapezoid arrangements. In addition, the air-cooling strategy is studied by observing temperature distribution of the battery pack. It is found that the square arrangement is the structure with the best air-cooling effect, and the cooling effect is best when the cold air inlet is at the top of the battery pack. We hope that this work can provide theoretical guidance for thermal management of lithium-ion battery packs.

基于航空锂离子蓄电池组热失控特性的排气设计及应用分析

该文简要分析航空锂离子蓄电池组热失控的排气机理及产气成分,从外部因素和内部因素两方面对锂离子蓄电池组出现热失控的诱因及影响进行分析,并结合实际装机环境,针对航空锂离子蓄电池组发生热失控排气的燃爆气体浓度和发生燃爆的失效概率进行研究,从系统设计角度对锂离子蓄电池组的热安全性设计及控制策略提供数据参考及技术支撑。

航空蓄电池组端子熔融故障的分析与改进

某型航空蓄电池组在地面起动辅助动力装置期间出现端子熔融的严重故障。根据故障现象,使用维护状况和基本电路原理列举故障树,并且采取理论分析和试验验证结合的方式对底事件逐一排查。排查结果表明,蓄电池组存在缺乏防松措施,没有紧固力矩要求,标准件选用不严格的问题,无法应对长期振动环境。根据排查结果对使用维护和安装形式进行了优化改进。振动试验和放电试验结果表明,改进措施合理有效。

航空蓄电池温度控制器的设计和实现

针对航空蓄电池正常工作时对环境温度较为敏感的问题,设计出基于PIC单片机和CPLD为核心的航空蓄电池温度控制器设计方案,以满足航空蓄电池工作时的稳定性和高效性要求;该航空蓄电池温度控制器的基本功能是采样航空蓄电池箱内实时温度,通过PIC单片机实现蓄电池温度的功率调节算法,同时通过ARINC 429总线实现与航空电子设备之间的通讯,达到对蓄电池环境温度的远程监测;实验证明,该控制器能有效地解决航空蓄电池工作时对环境温度的敏感性问题;同时该控制器稳定、高效、可靠性高、便于操作,为后续扩展也提供了一个较好的平台。

A branch current estimation and correction method for a parallel connected battery system based on dual BP neural networks

In the actual use of a parallel battery pack in electric vehicles(EVs),current distribution in each branch will be different due to inconsistence characteristics of each battery cell.If the branch current is approximately calculated by the total current of the battery pack divided by the number of the parallel branches,there will be a large error between the calculated branch current and the real branch current.Adding current sensors to measure each branch current is not practical because of the high cost.Accurate estimation of branch currents can give a safety warning in time when the parallel batteries of EVs are seriously inconsistent.This paper puts forward a method to estimate and correct branch currents based on dual back propagation(BP)neural networks.In the proposed method,one BP neural network is used to estimate branch currents,the other BP neural network is used to reduce the estimation error cause by current pulse excitations.Furthermore,this paper makes discussions on the selection of the best inputs for the dual BP neural networks and the adaptability of the method for different battery capacity and resistence differences.The effectiveness of the proposed method is verified by multiple dynamic conditions of two cells connected in parallel.

Design a Hybrid Energy-Supply for the Electrically Driven Heavy-Duty Hexapod Vehicle

Increasing the power density and overload capability of the energy-supply units(ESUs)is always one of the most challenging tasks in developing and deploying legged vehicles,especially for heavy-duty legged vehicles,in which significant power fluctuations in energy supply exist with peak power several times surpassing the average value.Applying ESUs with high power density and high overload can compactly ensure fluctuating power source supply on demand.It can avoid the ultra-high configuration issue,which usually exists in the conventional lithium-ion battery-based or engine-generator-based ESUs.Moreover,it dramatically reduces weight and significantly increases the loading and endurance capabilities of the legged vehicles.In this paper,we present a hybrid energy-supply unit for a heavy-duty legged vehicle combining the discharge characteristics of lithium-ion batteries and peak energy release/absorption characteristics of supercapacitors to adapt the ESU to high overload power fluctuations.The parameters of the lithium-ion battery pack and supercapacitor pack inside the ESU are optimally matched using the genetic algorithm based on the energy consumption model of the heavy-duty legged vehicle.The experiment results exhibit that the legged vehicle with a weight of 4.2 tons can walk at the speed of 5 km/h in a tripod gait under a reduction of 35.39%in weight of the ESU compared to the conventional lithium-ion battery-based solution.

航空用动力锂离子蓄电池组单体不一致性研究

针对锂电池成组后,普遍存在组内单体的不一致性现象,分析了造成电池组不一致的内部原因和外部原因,对7ICP45航空锂离子蓄电池组进行充放电等实验,从容量、充放电倍率、电压均衡等角度,分析、总结电池组不一致的表现规律。实验结果表明:锂电池组单体不一致性外在表现明显。本研究能够进一步推进锂电池成组的应用,为航空用动力锂电池的检测维护和健康管理提供了研究依据。