Characterization and identification towards dynamic-based electrical modeling of lithium-ion batteries

Lithium-ion batteries are widely recognized as a crucial enabling technology for the advancement of electric vehicles and energy storage systems in the grid.The design of battery state estimation and control algorithms in battery management systems is usually based on battery models,which interpret crucial battery dynamics through the utilization of mathematical functions.Therefore,the investigation of battery dynamics with the purpose of battery system identification has garnered considerable attention in the realm of battery research.Characterization methods in terms of linear and nonlinear response of lithium-ion batteries have emerged as a prominent area of study in this field.This review has undertaken an analysis and discussion of characterization methods,with a particular focus on the motivation of battery system identification.Specifically,this work encompasses the incorporation of frequency domain nonlinear characterization methods and dynamics-based battery electrical models.The aim of this study is to establish a connection between the characterization and identification of battery systems for researchers and engineers specialized in the field of batteries,with the intention of promoting the advancement of efficient battery technology for real-world applications.

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.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

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.

Regulating the Solvation Structure of Li^(+) Enables Chemical Prelithiation of Silicon-Based Anodes Toward High-Energy Lithium-Ion Batteries

The solvation structure of Li^(+) in chemical prelithiation reagent plays a key role in improving the low initial Coulombic efficiency(ICE) and poor cycle performance of silicon-based materials. Never theless, the chemical prelithiation agent is difficult to dope active Li^(+) in silicon-based anodes because of their low working voltage and sluggish Li^(+) diffusion rate. By selecting the lithium–arene complex reagent with 4-methylbiphenyl as an anion ligand and 2-methyltetrahydrofuran as a solvent, the as-prepared micro-sized Si O/C anode can achieve an ICE of nearly 100%. Interestingly, the best prelithium efficiency does not correspond to the lowest redox half-potential(E_(1/2)), and the prelithiation efficiency is determined by the specific influencing factors(E_(1/2), Li^(+) concentration, desolvation energy, and ion diffusion path). In addition, molecular dynamics simulations demonstrate that the ideal prelithiation efficiency can be achieved by choosing appropriate anion ligand and solvent to regulate the solvation structure of Li^(+). Furthermore, the positive effect of prelithiation on cycle performance has been verified by using an in-situ electrochemical dilatometry and solid electrolyte interphase film characterizations.

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.

Mechanical behavior analysis of high power commercial lithium-ion batteries

In application,lithium-ion cells undergo expansion during cycling.The mechanical behavior and the impact of external stress on lithium-ion battery are important in vehicle application.In this work,18 Ah high power commercial cell with Li Ni_(0.5)Co_(0.2)Mn_(0.3)O_(2)/graphite electrode were adopted.A commercial compress machine was applied to monitor the mechanical characteristics under different stage of charge(SOC),lifetime and initial external force.The dynamic and steady force was obtained and the results show that the dynamic force increases as the SOC increasing,obviously.During the lifetime with high power driving mode,different external force is shown to have a great impact on the long-term cell performance,with higher stresses result in higher capacity decay rates and faster impedance increases.A proper initial external force(900 N)provides lower impedance increasing.Postmortem analysis of the cells with2000 N initial force suggests a close correlation between electrochemistry and mechanics in which higher initial force leads to higher direct current internal resistance(DCIR)increase rate.In addition,for the cell with higher external force,deformation of the cathode and thicker solid electrolyte interface(SEI)film on the surface of anode and separator are observed.Porosity reduction and closure was also verified after cycles which is an obstacle to the lithium ion transferring.The largest cause of the observed capacity decline was the loss of active lithium through autopsy analysis.In addition,for the cell with higher external force,deformation of the cathode and thicker SEI film on the surface of anode and separator are observed.Porosity reduction and closure was also verified after cycles which is an obstacle to the lithium ion transferring.The largest cause of the observed capacity decline was the loss of active lithium through autopsy analysis.

A Self-Healing and Nonflammable Cross-Linked Network Polymer Electrolyte with the Combination of Hydrogen Bonds and Dynamic Disulfide Bonds for Lithium Metal Batteries

The self-healing solid polymer electrolytes(SHSPEs)can spontaneously eliminate mechanical damages or micro-cracks generated during the assembly or operation of lithium-ion batteries(LIBs),significantly improving cycling performance and extending service life of LIBs.Here,we report a novel cross-linked network SHSPE(PDDP)containing hydrogen bonds and dynamic disulfide bonds with excellent self-healing properties and nonflammability.The combination of hydrogen bonding between urea groups and the metathesis reaction of dynamic disulfide bonds endows PDDP with rapid self-healing capacity at 28°C without external stimulation.Furthermore,the addition of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide(EMIMTFSI)improves the ionic conductivity(1.13×10^(−4)S cm^(−1)at 28°C)and non-flammability of PDDP.The assembled Li/PDDP/LiFePO_(4)cell exhibits excellent cycling performance with a discharge capacity of 137 mA h g^(−1)after 300 cycles at 0.2 C.More importantly,the self-healed PDDP can recover almost the same ionic conductivity and cycling performance as the original PDDP.

Analysis of Capacity Decay, Impedance, and Heat Generation of Lithium-ionBatteries Experiencing Multiple Simultaneous Abuse Conditions

Abuse of Lithium-ion batteries,both physical and electrochemical,can lead to significantly reduced operational capabilities.In some instances,abuse can cause catastrophic failure,including thermal runaway,combustion,and explosion.Many different test standards that include abuse conditions have been developed,but these generally consider only one condition at a time and only provide go/no-go criteria.In this work,different types of cell abuse are implemented concurrently to determine the extent to which simultaneous abuse conditions aggravate cell degradation and failure.Vibrational loading is chosen to be the consistent type of physical abuse,and the first group of cells is cycled at different vibrational frequencies.The next group of cells is cycled at the same frequencies,with multiple charge pulses occurring during each discharge.The final group of cells is cycled at the same frequencies,with a partial nail puncture occurring near the beginning of cycling.The results show that abusing cells with vibrational loading or vibrational loading with current pulses does not cause a significant decrease in operational capabilities while abusing cells with vibrational loading and a nail puncture drastically reduces operational capabilities.The cells with vibration only experience an increase in internal resistance by a factor of 1.09–1.26,the cells with vibration and current pulses experience an increase in internal resistance by a factor of 1.16–1.23,and all cells from each group reach their rated lifetime of 500 cycles without reaching their end-of-life capacity.However,the cells with vibration and nail puncture experience an increase in internal resistance by a factor of 6.83–22.1,and each cell reaches its end-of-life capacity within 50 cycles.Overall,the results show that testing multiple abuse conditions simultaneously provides a better representation of the extreme limitations of cell operation and should be considered for inclusion in reference test standards.

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.

Modeling and Optimization of Heat Dissipation Structure of EV Battery Pack

In order to solve the problems of high temperature and inconsistency in the operation of electric vehicle（ EV） battery pack,computational fluid dynamics（ CFD） simulation method is used to simulate and optimize the heat dissipation of battery pack. The heat generation rate at different discharge magnifications is identified by establishing the heat generation model of the battery. In the forced air cooling mode,the Fluent software is used to compare the effects of different inlet and outlet directions,inlet angles,outlet angles,outlet sizes and inlet air speeds on heat dissipation. The simulation results show that the heat dissipation effect of the structure with the inlet and outlet on the same side is better than that on the different sides; the appropriate inlet angle and outlet width can improve the uniformity of temperature field; the increase of the inlet speed can improve the heat dissipation effect significantly. Compared with the steady temperature field of the initial structure,the average temperature after structure optimization is reduced by 4. 8℃ and the temperature difference is reduced by 15. 8℃,so that the battery can work under reasonable temperature and temperature difference.

Understanding the failure mechanism towards developing high-voltage single-crystal Ni-rich Co-free cathodes

Benefited from its high process feasibility and controllable costs,binary-metal layered structured LiNi_(0.8)Mn_(0.2)O_(2)(NM)can effectively alleviate the cobalt supply crisis under the surge of global electric vehicles(EVs)sales,which is considered as the most promising nextgeneration cathode material for lithium-ion batteries(LIBs).However,the lack of deep understanding on the failure mechanism of NM has seriously hindered its application,especially under the harsh condition of high-voltage without sacrifices of reversible capacity.Herein,singlecrystal LiNi_(0.8)Mn_(0.2)O_(2) is selected and compared with traditional LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)(NCM),mainly focusing on the failure mechanism of Cofree cathode and illuminating the significant effect of Co element on the Li/Ni antisite defect and dynamic characteristic.Specifically,the presence of high Li/Ni antisite defect in NM cathode easily results in the extremely dramatic H2/H3 phase transition,which exacerbates the distortion of the lattice,mechanical strain changes and exhibits poor electrochemical performance,especially under the high cutoff voltage.Furthermore,the reaction kinetic of NM is impaired due to the absence of Co element,especially at the single-crystal architecture.Whereas,the negative influence of Li/Ni antisite defect is controllable at low current densities,owing to the attenuated polarization.Notably,Co-free NM can exhibit better safety performance than that of NCM cathode.These findings are beneficial for understanding the fundamental reaction mechanism of single-crystal Ni-rich Co-free cathode materials,providing new insights and great encouragements to design and develop the next generation of LIBs with low-cost and high-safety performances.

基于电池组动态串联结构实现交流输出及电池均衡

电池是电动汽车的主要储能系统,如何提高输出波形的可靠性及改善电池的不一致性已成为汽车电池研究的关键问题。提出了一种改进型电池单元结构,该拓扑结构可以实现任意电池单元间的串联,且可以调整串联电池的数量,不仅可以输出稳定的正弦波,还可以有效实现电池均衡。与其他多电平结构相比,其结构简单、器件成本低,控制方式更简单且易于实现,具有较高的效率与较低的波形畸变率。逆变器输出的控制策略采用面积等效原理,使输出波形最大限度地等效于正弦波,有效降低谐波含量,而在均衡策略中,设计基于该结构动态均衡策略,实现各电池单元间的快速平衡。结果表明,该电路结构能够稳定地提供220 V/50 Hz的交流电压,输出波形谐波含量仅为2.37%。与传统逆变电路相比,该拓扑结构在逆变工作时兼具电池均衡功能,且均衡效果良好。最后通过物理样机的测试证明了该电路的有效性和可靠性,为电动汽车的逆变输出和均衡控制提供了一种新的方案。

Explosion behavior investigation and safety assessment of large-format lithium-ion pouch cells

Large-format lithium-ion(Li-ion) batteries with high energy density for electric vehicles are prone to thermal runaway(or even explosion) under abusive conditions. In this study, overcharge induced explosion behaviors of large-format Li-ion pouch cells with Li[NiCoMn]Ocathode at different current rates(C-rates)(0.5C, 1C, 2C) were investigated. The explosion characteristics of the cells were elucidated by discussing the evolution of the cell voltage, the surface temperature and the shock wave pressure.Generally, the whole overcharge process could be divided into four stages according to the evolution of several key parameters and the overcharge behaviors;the overcharge C-rate has a great influence on cells’ thermal behaviors. The experimental results showed that the thermal runaway process of Liion cells caused by overcharging consisted of two kinds of explosions, physical explosion and chemical explosion. The existence of observable negative pressure zone in the pressure curves indicated that the Li-ion cells are not a self-supplying oxygen system during the explosion. Further, the explosion dynamics parameters were matched. An explosion TNT-equivalent conversion strategy that depended on the pressure of the shock wave was utilized to evaluate the released energy and its hazards. In addition, with respect to the overcharge of Li-ion pouch cells, a safety assessment method and a safety management method were proposed based on the explosion behaviors. From the perspective of battery safety, this study is of great significance for the safety design of Li-ion cells and can provide guidance for engineers to optimize the safety function of battery packs.

动态工况下锂电池组多物理场仿真与退化分析

目的提高锂电池组SOH评估的准确性,提出面向实际复杂动态工况的锂电池组退化仿真分析方法。方法通过耦合多个电池单体P2D电化学–热模型和电池组串并联等效电路–热–流体模型,建立锂电池组多物理场耦合仿真模型,分析电池系统实际使用过程中电流、温度等工况的动态特性,构建锂电池组广义动态工作载荷谱。开展模型验证和典型3并5串锂电池组多物理场仿真分析,并耦合基于SEI膜生成机理的容量退化模型,分析在动态工况下内部各电池单体的容量及SOH退化情况,并给出该型电池组寿命的薄弱环节。结果动态工况下,锂电池退化轨迹呈高度非线性,环境温度为25~60℃时,随着温度的升高,电池组退化较快,但电池组内部最大温差反而减小。结论提出的方法能够很好地量化实际复杂动态工况对锂电池组退化的影响,为其可靠性设计和运行管理提供了技术支撑。

电池包箱体跌落挤压动力学分析与结构优化

为改善电池包箱体的耐冲击性能,以某款物流车动力电池包为研究对象,综合考虑电池包的跌落和挤压问题,分析了不同跌落高度、跌落角度以及不同挤压方向工况下电池包箱体的动力学响应。根据分析结果,以电池包箱体安全性能作为优化目的,以电池包箱体尺寸参数作为设计变量,对电池包箱体进行了多目标尺寸优化,并通过跌落试验验证了优化效果。结果表明,优化后电池包箱体的强度得到了提高,动力学响应满足跌落和挤压工况的要求,验证了本文电池包动力学分析与结构优化方法的正确性。

A cellular automata framework for porous electrode reconstruction and reaction-diffusion simulation

Due to the high charge transfer efficiency compared to that of non-porous materials,porous electrodes with larger surface area and thinner solid pore walls have been widely applied in the lithium-ion battery field.Since the capacity and charge-discharge efficiency of batteries are closely related to the microstructure of porous materials,a conceptually simple and computationally efficient cellular automata(CA)framework is proposed to reconstruct the porous electrode structure and simulate the reactiondiffusion process under the irregular solid-liquid boundary in this work.This framework is consisted of an electrode generating model and a reaction-diffusion model.Electrode structures with specific geometric properties,i.e.,porosity,surface area,size distribution,and eccentricity distribution can be constructed by the electrode generating model.The reaction-diffusion model is exemplified by solving the Fick's diffusion problem and simulating the cyclic voltammetry(CV)process.The discharging process in the lithium-ion battery are simulated through combining the above two CA models,and the simulation results are consistent with the well-known pseudo-two-dimensional(P2D)model.In addition,a set of electrodes with different microstructures are constructed and their reaction efficiencies are evaluated.The results indicate that there is an optimum combination of porosity and particle size for discharge efficiency.This framework is a promising one for studying the effect of electrode microstructure on battery performance due to its fully synchronous computation way,easy handled boundary conditions,and free of convergence concerns.

电动汽车电池包底部球击影响因素分析

为分析电池系统底部球击工况下的安全状态,对某款电池包进行底部球击试验,并分析其壳体变形情况和受力情况。采用一种刚性球头对电池包底部进行球击的方法来模拟电池包受到道路障碍物冲击的过程,并通过仿真进行准静态底部球击分析。对比静态球击状态下电池包试验结果和仿真分析结果,可见仿真模型较好地模拟了电池包底部的受力情况,仿真结果和试验结果高度一致,充分说明了该方法和仿真模型的有效性。分别采用位移加载和速度加载方式进行底部球击仿真分析,对比两种仿真模型电池包在底部球击过程中的差异。结果表明:两种仿真模型参考单元处应力峰值仅相差0.7%,表明加载方式对底部球击点位处应力影响较小;两种加载方式下的峰值力相差近400 N,表明其致损过程是有区别的,且均表现为恒定速度加载情况下损伤更严重。分析了电池包在底部球击下的安全性能,并且揭示了仿真分析中不同加载方式对测试结果的影响,通过有限元分析的方式为电池包底部球击测试提供了数据支撑。

一种集散式动力电池组动态均衡管理系统

提出一种集散式动力电池组动态均衡管理系统。该系统由电池监测子系统、电池均衡子系统、电池管理子系统和上位机组成。各子系统之间通过单总线通信,管理子系统与上位机之间通过CAN总线进行通信。该系统可以进行动力电池组的充、放电均衡管理。充放电均衡子系统采用的小型充电机的数目少于或等于单体电池的个数,这样电池的均衡控制更为灵活,避免了一对一的配备方式,简化了系统结构,降低了成本,提高了系统的可靠性。详细阐述系统的构成和工作原理,并以停车外接均衡充电模式和行车均衡模式为例,进行了系统的均衡实验。