Mechanisms for the evolution of cell-to-cell variations and their impacts on fast-charging performance within a lithium-ion battery pack

Cell-to-cell variations(CtCV) compromise the electrochemical performance of battery packs, yet the evolutional mechanism and quantitative impacts of CtCV on the pack's fast-charging performance remain unexplored. This knowledge gap is vital for the proliferation of electric vehicles. This study underlies the relationship between CtCV and charging performance by assessing the pack's charge speed, final electric quantity, and temperature consistency. Cell variations and pack status are depicted using 2D parameter diagrams, and an m PnS configured pack model is built upon a decomposed electrode cell model.Variations in three single electric parameters, i.e., capacity(Q), electric quantity(E), and internal resistance(R), and their dual interactions, i.e., E-Q and R-Q, are analyzed carefully. The results indicate that Q variations predominantly affect the final electric quantity of the pack, while R variations impact the charge speed most. With incremental variances in cell parameters, the pack's fast-charging capability first declines linearly and then deteriorates sharply as variations intensify. This research elucidates the correlations between pack charging capabilities and cell variations, providing essential insights for optimizing cell sorting and assembly, battery management design, and charging protocol development for battery packs.

Electric-controlled pressure relief valve for enhanced safety in liquid-cooled lithium-ion battery packs

The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.

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.

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.

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.

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.

Research on Fuzzy PID Charging Control of Battery Pack

Battery groups are widely used in production and life. Optimal charging can not only shorten the charge time, but also improve the performance and life of the battery pack. A constant current or constant voltage charging method is commonly used. This type of method cannot adjust the charge capacity in time according to the change of charging capacity of storage battery, and the charge performance is not high. This paper designs a fuzzy PID controller. In the case of variable load and interference, the battery group can still be charged by the optimal charging current. Through the simulation results, the fuzzy PID controller works well and verifies the feasibility of the charging controller.

Comparative study of the thermal insulation performance of steel and aluminum battery packs in high-and low-temperature environments

As the only power source of pure electric vehicles,the performance of battery packs is easily affected by the temperature,and too high or too low temperature will make the performance of battery packs decline.In this study,the thermal analysis finite element modeling of a cast aluminum battery pack and steel battery pack of a pure electric vehicle is established to compare the thermal insulation performance of two kinds of battery packs under high-and low-temperature conditions.The simulation results show that the thermal insulation performance of the two kinds of battery packs meets the design requirements under high-and low-temperature conditions.The external environment of the cell and battery pack mainly transmits heat through heat conduction.Aiming at the problem that the uniform temperature performance of the steel battery pack is lower than that of the cast aluminum battery pack,several optimization solutions are put forward for the insulation design of the steel battery pack,and the optimal solution is obtained by comparing the simulation results.

New Composite Equalization Strategy for Lithium Battery Packs

In order to improve the working efficiency of the power battery pack and prolong the service life, there is a problem of inconsistency among the individual cells. Based on the centralized equalization structure of the multi-output winding transformer, a three-stage hybrid equalization control strategy is designed for equalization. The equalization scheme realizes that the high voltage single battery transfers the energy to the low voltage battery cell during the charging of the battery pack, improving not only charging efficiency and energy use loss, but also the high voltage battery transferring the power to the low voltage battery cell when the pressure difference is greater than 10 mv during the discharge. Between 5 mv and 10 mv, it performs passive equalization, reducing the output fluctuation of the power battery pack and achieving the balance purpose. During the standing time, the maximum active balancing operation within the battery pack is performed in order to achieve intra-group optimum consistency. It is proved by experiments that the equalization control method can realize the quick and effective equalization in the battery pack, and the energy balance of each single battery.

基于机器视觉的电池PACK装配线装配工艺研究

为了提高动力电池PACK自动化行业的产品质量和生产效率,通过机器视觉识别模块和壳体组件来组装螺栓孔的位置和状态,机器人执行模块抓取和螺栓拧紧,并在指定的姿势下实现模块壳体组装和螺栓紧固工作。

故障树分析在锂电池PACK生产监理中的应用

本文从电池PACK生产过程中的实际监理出发,针对电池PACK生产过程中容易出现问题的环节,以故障树分析方法分析生产过程中故障产生的原因,并通过监理过程实际应用,取得了良好效果,为今后电池PACK的生产提供了新的监理思路和宝贵经验。

动力电池PACK智能制造复合型人才定向培养研究

分析了新能源汽车核心能量源——动力电池PACK制造的现状,根据当前动力电池PACK智能制造人才培养的现状,对动力电池PACK智能制造复合型人才定向培养路径进行了研究,为其他跨专业领域的复合型人才培养提供启示。

Development and Verification of the Equilibrium Strategy for Batteries in Electric Vehicles

The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the operating performance. A hybrid equilibrium strategy based on decision combing battery state-of-charge（ SOC） and voltage has been proposed. The battery SOC is estimated through an improved least squares method. An equalization hardware in loop（ HIL） platform has been constructed. Based on this HIL platform,equilibrium strategy has been verified under the constant-current-constant-voltage（ CCCV） and dynamicstresstest（ DST） conditions. Experimental results indicate that the proposed hybrid equalization strategy can achieve good balance effect and avoid the overcharge and over-discharge of the battery pack at the same time.

磷酸铁锂电池PACK的不同种类充电方法研究

采用了三种不同的充电策略对磷酸铁锂动力电池PACK进行充放电测试研究,主要对锂离子电池的充放电容量、温度变化量、时间、能量往返效率等参数进行测试分析,通过实验得出:在电压平台区采用斜坡电流充电能够将电池充至满电状态,具有较高的能量往返效率,并且能够有效控制电池充电过程中的温升。

一种新型纯电动城市客车锂电池组的PACK及管理

简述目前纯电动客车运行的问题,通过电池PACK的串并分析,提出先串后并的PACK方式,并对电池PACK管理系统提出建议。

阻燃隔热涂层在锂离子动力电池PACK中的应用

轻量化是电动汽车的发展趋势,电动汽车蓄电池PACK壳体往轻质金属和高分子复合材料方向发展,但这些材料无法通过外部火烧的实验规定,安全性能无法得到保证,本文采用了高效阻燃隔热涂层,使电动汽车蓄电池包系统具备了短时间内抵抗外部火焰的能力,给车上人员以救险或者逃生时间,具有重要的现实意义和广泛的应用前景。

一种电池PACK复合材料箱盖设计研究

对动力电池PACK的箱盖采用玻璃纤维和环氧树脂复合材料进行设计研究,并进行试件的力学性能测试,最后进行强度和模态分析。结果表明,该材料箱盖不仅能够满足刚强度要求,还能实现轻量化。

动力电池模组和PACK装配线的工艺技术应用研究

随着电动自行车共享换电的市场占有率越来越大,各大新能源电池生产企业纷纷建设了动力电池模组线和PACK装配线。为了提高共享换电的动力电池模组和PACK装配组装的工艺水平和生产效率,笔者以某新能源电池包模组和PACK装配线项目为例,介绍了模组和装配线的结构组成,阐述了模组和装配线工艺技术的应用,并对各模块应用做了简要的概述,为相似项目的解决方案提供了参考和依据。