One-step surface nitridation of CoO for high-energy-density lithium-ion batteries

The surface of micrometer-sized CoO particles is finely tuned by a nitride produced through one-step process with various heat treatments with solid-state urea, which has been frequently used as a surfactant for the nanosynthesis of CoO in aqueous media. After conducting simple surface nitridation processes without a solvent using an autogenic reactor, it is found that the CoO surface is clearly modified by a cobalt-rich nitride (Co_5.47N) by X-ray diffraction analyses. As the heat treatment temperature increases, the content of the cobalt nitride also grows. A temperature above 500°C for the surface modification causes an initial specific capacity loss because of the growth of the inert cobalt nitride; however, the surface-modified samples exhibit a more prolonged cycle life than the pristine CoO due to decreased kinetic barrier and enhanced surface stability. Additionally, the surface nitridation leads to a high rate capability, which can solve poor power density for conversion-type anode materials. It is found that the surface nitridation allows a high electrical conductivity and reduces the surface charge transfer resistance.     

Thermal management of lithium-ion batteries using phase change materials

良好的热管理系统是电池安全及高效使用的保证,电池的热管理需要确保电池温度在安全温度范围以及电池组内最大温差不超过5 ℃.传统的热管理方式,如空气冷却,不仅需要额外的动力输入,而且越来越不能满足高能量密度的新型锂离子电池的热管理需要.使用相变材料的电池热管理系统,利用相变材料的相变潜热吸收电池产生的热量,在不使用外界功耗的条件下,可以长时间保持电池的温度在适宜的范围内.通过与膨胀石墨,金属泡沫复合,相变材料的热导率可以大大提高,电池组内体系温度均匀性可以满足工作要求.而且,相变材料的形状不固定,可以使用在任意形状的电池上.被动式热管理是应用于电池热管理系统中最具前景的技术.     

Online state of health prediction method for lithium-ion batteries,based on gated recurrent unit neural networks

Online state of health (SOH) prediction of lithium-ion batteries remains a very important problem in assessing the safety and reliability of battery-powered systems. Deep learning techniques based on recurrent neural networks with memory, such as the long short-term memory (LSTM) and gated recurrent unit (GRU), have very promising advantages, when compared to other SOH estimation algorithms. This work addresses the battery SOH prediction based on GRU. A complete BMS is presented along with the internal structure and configuration parameters. The neural network was highly optimized by adaptive moment estimation (Adam) algorithm. Experimental data show very good estimation results for different temperature values, not only at room value. Comparisons performed against other relevant estimation methods highlight the performance of the recursive neural network algorithms such as GRU and LSTM, with the exception of the battery regeneration points. Compared to LSTM, the GRU algorithm gives slightly higher estimation errors, but within similar prediction error range, while needing significantly fewer parameters (about 25% fewer), thus making it a very suitable candidate for embedded implementations.     

Research on thermal runaway test platform design for energy-storage lithium-ion batteries

介绍了锂离子储能电池热失控研究的目的和意义,探讨了储能电池与动力电池在热失控检测实验研究关注上的异同,从理论分析和实验研究两方面归纳了影响储能电池热失控的促发条件及对应的关键阈值.在此基础上,完成了模拟热失控促发条件和满足阈值要求的检测实验平台设计及功能验证,并对此平台的应用前景进行了展望.     

A hierarchical management system for energy storage batteries

现有的储能电池管理系统大都是从电动汽车电池管理系统直接引用过来的,其管理的电池容量小,功能单一,实时性较差.兆瓦级储能系统由大容量电池串联,对电池系统管理效率提出了新要求.为解决这一问题,提出了一种3层分层式储能电池管理系统.对底层BMU,中层BCMS和顶层BAMS从硬件和软件设计两方面做了详细地介绍.分层式储能电池管理系统具有检测与计算,电池单体均衡管理,高压管理,统计存储,充放电管理,报警功能和通信.     

Heterogeneous aging of large-scale flexible lithium-ion batteries based on micro-heterostructures and simulation

To meet application demands of electric vehicles, cathode materials of batteries have to overcome the life limitation and performance attenuation caused by crack propagation on the surface of electrode particles. With the increase of size and power of batteries, the voltage gradient generated by metal foil current collectors with high conductivity cannot be neglected. This study reconstructed a porous microstructure based on images of surface morphologies of lithium manganese oxide particles collected by a field emission-scanning electron microscope. Based on this, a multi-scale and multi-physics simulation model coupling electro-chemo-thermo-mechanical behaviours was developed to predict heterogeneous mechanical stress and capacity loss of a large-scale flexible lithium-ion battery. The results arising from use of the model show that: (1) Lithium in electrode particles cannot be diffused in time under a high-charge and discharge rate, and the capacity loss of the battery is directly proportional to the stress generated on the electrode particles. Capacity loss at discharge rate of 10C is 46% higher than that at the rate of 1C and corresponding stress in the microstructure increases by 16%. (2) In the design of the battery layout adopted in this study, utilization rates of electrodes and temperature fields are highly heterogeneous at the higher rate. Mechanical stress near the tab is 8% higher than that at the bottom edge, and it is speculated that the rate of aging of the tab is 35% higher. (3) Mechanical stress during lithium extraction in the cathode during charge is less than half of that during discharge. Attributed to small influences of material activity and excellent performance of lithium titanate oxide in the anode, capacity loss during charge is only 2%. (4) During discharge, stress in the contact region of between particles is the largest, resulting in the decrease of the activity and the low lithium-ion concentration. This leads to cracks during cyclic charging and discharging, which further decreases the activity of the materials. (5) Heterogeneity in the distribution of lithium-ion concentration with different sizes of particles significantly rises with the rate. The model built in this research couples the analysis of temperature field of a battery cell and stress field of the microstructure, which is conducive to understanding mechanisms underlying performance attenuation of the large-scale flexible lithium-ion battery under high-rate use.     

Electromobility concept for racing cars based on lithium-ion batteries and supercapacitors

For the construction of an all-electric race car, all aspects from engineering design over cost estimation up to the road capability are illuminated. From the most promising batteries for electric vehicle propulsion, the state-of-the art and commercial availability of lithium-ion secondary batteries is critically discussed with respect to cycle-life and unfavorable charge-discharge conditions. A market-overview is given with respect to a small electric car. Different combinations of electric motors and a recuperation system have been investigated. Weight aspects of central drive systems were considered and compared with decentralized wheel-hub drives. As a result, a centralized high-speed drive train based on a permanent-magnet synchronous engine with high-energy magnets seems to be superior due to limited space for assembly.     

Design and investigation on portable energy storage device based on sodium-ion batteries

报道了一种新型移动式钠离子电池储能系统,其核心储能器件为钠离子电池,采用自制的NaNi_1/3Fe_1/3Mn_1/3O₂为正极材料,负极材料为硬碳。采用XRD、DSC等对正极材料的结构和热稳定性进行分析表征。设计制作了1 A·h软包型钠离子电池,对其电化学性能与安全性进行测试。在此基础上设计了钠离子电池包以及基于钠离子电池的0.1 kW·h新型移动式储能系统。该系统在家用储能、军事电源、低速电动车上有良好的应用前景。     

Numerical study on thermal behavior and a liquid cooling strategy for lithium-ion battery

Investigation on the thermal behavior of the lithium-ion battery which includes the temperature response, heat contribution and generation, is of vital importance for their performance and safety. In this study, an electrochemical-thermal cycling model is presented for a 4 Ah 21700 type cylindrical single cell and 3× 3 battery pack and the model is validated by experiment on a single cell. Thermal behavior on a single cell is first analyzed, the results show that the heat generated in the charge is smaller than the discharge, and the polarization heat contributes the most to total heat, especially under higher rate. It can also be concluded from the battery pack that the temperature of the cell inside the battery pack is significantly greater than the external battery, while the temperature difference exists the opposite regular due to the worst heat dissipation of the central cell. Finally, after taking the enhanced liquid cooling strategy, the maximum temperature is 320.6 K that is reduced by 9.38%, and the maximum temperature difference is 4.9 K which is reduced by 69.6% at 2C, meeting the requirements of battery thermal management system.     

Thermal safety study of Li-ion batteries under limited overcharge abuse based on coupled electrochemical-thermal model

Many fire accidents of electric vehicles were reported that happened during the charging process. In order to investigate the reasons that lead to this problem, this paper studies the thermal safety of Li-ion batteries under limited overcharge abuse. A 3D electrochemical-thermal coupled model is developed for modeling thermal and electrochemical characteristics from normal charge to early overcharge state. This model is validated by experiment at charge rates of 0.5C, 1C, and 2C. The simulation results indicate that irreversible heat contributes most to temperature rise during the normal charge process, but the heat induced by Mn dissolution and Li deposition gradually dominates heat generation in the early overcharge period. Based on this, a threshold selection method for multistage warning of batteries overcharge is proposed. Among them, level 1 should be considered as a critical stage during the early overcharge process due to the deposited lithium starts to react with electrolyte at the end of level 1, where temperature rate increases to 0.5°C min⁻¹ for 1C charge. While the thresholds of levels depend on charge rate and composition of battery. Furthermore, several critical parameters are analyzed to figure out their effects on thermal safety. It is found that the temperature at the end of overcharge is significantly influenced by the change of positive electrode thickness and solid electrolyte interface (SEI) film resistance. The final temperature increases by 17.5°C and 7.9°C, respectively, with positive electrode thickness ranging from 50 to 80 μm and SEI film resistance increasing from 0.002 to 0.03 Ω.     

Review on heat generation theory model of lithium-ion battery

本文根据近年来锂离子电池产热特性方面的研究,详细阐述了锂离子电池产热的基本原理,并总结了国内外锂离子电池产热模型的研究现状。重点针对电化学-热耦合模型、电-热耦合模型以及热滥用模型进行了详细综述,并在此基础上对锂离子电池热效应的研究和产热模型的建立进行了展望。     

A review on thermal management techniques for lithium-ion battery

随着新能源汽车的广泛使用,动力锂离子电池的热安全性问题日益突出。本文以Bernardi生热机理为基础,耦合不同物理量,分别从电化学-热耦合模型、电-热耦合模型和热滥用模型来介绍单体电池的热特性。由于电池能量密度的增加与行驶工况复杂程度的提高,动力锂离子电池容易发生热量堆积,甚至造成热失控,对此,文中梳理了商用动力电池包的常用冷却方式。最后,根据对影响电池模组安全性的热失控蔓延机理及实测结果,介绍了阻断单体及基本模块热失控传播的有效方法。     

The review of thermal management technology for large-scale lithium-ion battery energy storage system

大容量锂离子电池储能系统对完善传统电网和高效利用新能源都具有非常重要的作用。为了实现大容量锂离子电池储能系统的高倍率化、长寿命化以及高安全性,高性能电池热管理系统的研发刻不容缓。本文总结了温度对锂离子电池性能的影响规律,综述了空冷、液冷、热管冷却、相变冷却这4种典型热管理技术的研究概况,分析了热管理技术在锂离子电池储能系统中的应用与研究状况。随着锂离子电池储能系统工作倍率的提高,产热量随之增大,对热管理系统的要求也越来越高。下一步的研究工作应围绕空冷系统优化、基于新型冷却介质的液冷系统、经济型热管及多目标优化设计这4方面展开。     

Cobalt-doped layered lithium nickel oxide as a three-in-one electrode for lithium-ion and sodium-ion batteries and supercapacitor applications

Layered LiNi_0.94Co_0.06O₂ (LNCO) was prepared and explored as an energy-storage material for Li-ion (LIBs), Na-ion (SIBs) batteries as well as supercapacitor application for the first time. All the physical and morphological characterizations were studied for the sample LNCO. The result displays good thermal stability, phase purity in the crystal structure, appreciable Brunauer-Emmett-Teller (BET) surface area (5.53 m² g⁻¹) and possesses cubic morphology. The cobalt was identified in lithium nickel oxide with binding energies at 794.02, 779.04 and 784.30 eV, respectively. In the case of LIBs, LNCO exists with a minimal difference of 5 mAh g⁻¹, even when cycled from 2C to 0.1C. After 200 cycles, the specific capacity, 247 mAh g⁻¹, is obtained for the cell with retention of 97.8% (efficiency 99.8%) at 0.1C. In SIBs, at 0.1C, the discharge capacity of 182 mAh g⁻¹ was restored even when cycled after 2C. After 200 cycles, a discharge capacity of 204 mAh g⁻¹ is ensured with retention of 96.6% (efficiency of 99.4% at 0.1C). In supercapacitor, the electrode, LNCO, delivered a specific capacity of 300 F g⁻¹ at 0.5 A g⁻¹. Therefore, LNCO is highly recommended as a suitable electrode material for fulfilling the requirement of energy-storage applications.     

State of charge estimation method based on the extended Kalman filter algorithm with consideration of time-varying battery parameters

In developing battery management systems, estimating state-of-charge (SOC) is important yet challenging. Compared with traditional SOC estimation methods (eg, the ampere-hour integration method), extended Kalman filter (EKF) algorithm does not depend on the initial value of SOC and has no accumulated error, which is suitable for the actual working condition of electric vehicles. EKF is a model-based algorithm; the accuracy of SOC estimated by this algorithm was greatly influenced by the accuracy of battery model and model parameters. The parameters of battery change with many factors and exhibit strong nonlinearity and time variance. Typical EKF algorithm approximates battery as a linear, time-invariant system; however, this approach introduces estimation errors. To minimize such errors, previous studies have focused on improving the accuracy of identifying battery parameters. Although studies on battery model with time-varying parameters have been carried out, few have studied the combination of time-varying battery parameters and EKF algorithm. A SOC estimation method that combines time-varying battery parameters with EKF algorithm is proposed to improve the accuracy of SOC estimation. Battery parameter data were obtained experimentally under different temperatures, SOC levels, and discharge rates. The results of parameter identification are made into a data table, and the battery parameters in the EKF system matrix are updated by looking up the data in the table. Simulation and experimental results shown that, average error of SOC estimated by the proposed algorithm is 2.39% under 0.9 C constant current discharge and 2.4% under 1.3 C, which is 1.91% and 2.35% lower than that of EKF algorithm with fixed battery parameters. Under intermittent discharge with constant current (1.1 C) and capacity (10%), the average error of SOC estimated by the proposed algorithm is 1.4%, which is 0.3% lower than that of EKF algorithm with fixed battery parameters. The average error of SOC estimated by the proposed algorithm under the New European Driving Cycle (NEDC) is 1.6%, which is 0.2% lower than that of EKF algorithm with fixed battery parameters. Relative to the EKF algorithm with fixed battery parameters, the proposed EFK algorithm with time-varying battery parameters yields higher accuracy.     

A novel interval-based approach for quantifying practical parameter identifiability of a lithium-ion battery model

Practical identifiability of battery model parameters, on which both modeling accuracy and robustness rely, is considered as a very important prerequisite for advanced onboard monitoring and control of Lithium-ion batteries. In this paper, a novel confidence-interval-based approach is proposed for the quantification and assessment of the practical identifiability of a widely used second order battery equivalent circuit model (ECM). This method utilizes profile likelihood and likelihood ratio subset statistic to calculate each parameter's confidence interval, based on which a normalized index is further derived for facilitating quantification and fast comparison of the identifiability degree among different parameters. Using this approach, the practical identifiability of the second order ECM under lab-collected experimental data is successfully evaluated, and the influences of several real-world factors are systematically examined through extensive simulations. The results show that the open circuit voltage and ohmic internal resistance have a much larger degree of identifiability in all the investigated conditions. Some practically useful insights on performing battery parameter identification are also provided.     

Design of robust battery capacity model for electric vehicle by incorporation of uncertainties

The improvement in the operating range of electric vehicles can be accomplished by robust modelling of the design and optimization of the energy storage capacity of the battery pack system. In this work, the authors have conducted a comprehensive survey on battery modelling methods and identified critical areas of improvement vital for estimating the battery capacity. This work proposes the artificial intelligence approach of automated neural networks search (ANS) in development of the robust battery capacity models for the lithium ion batteries based on the inputs (temperature and discharge rates). The robustness in the models is introduced by incorporating uncertainties in the inputs (the temperature and discharge rates, the architecture of algorithm and the models). The statistical analysis and validation of the models reveal that the models formulated using an ANS approach outperform the response surface regression models with correlation coefficient achieved as high as 0.97. The uncertainty analysis based on normal distribution of the inputs suggests that the models formulated from ANS are least sensitive to change in the input conditions when compared to response surface regression models. The global sensitivity analysis reveals that the temperature is a dominant factor for accurate battery capacity estimation. Copyright © 2017 John Wiley & Sons, Ltd.     

Simultaneous effect of particle size and location on stress development in the electrodes of lithium-ion batteries

In this study, stress generation at the electrode in Li-ion batteries was studied using a two-dimensional cell-scale model that includes multiple active particles during galvanostatic discharge. Numerical simulations were performed using an electrochemical–mechanical coupled model to elucidate the simultaneous effects of particle size and location, lithium intercalation kinetics and binder constraints on the stress. The simulation results showed that when different sizes of particle are considered in the electrode, the small particles were discharged more than the large particles, resulting in higher level of stress in the smaller particles. In addition, the closer the particles were located to the separator, the larger the stresses that were developed in those particles. Therefore, a layered structure, where the particle size gradually increases as the distance from the particles to the separator decreases, can alleviate stress on the electrode. When binder constraints were considered for the electrode particles, the stress was increased at the anode and alleviated at the cathode upon discharge. This indicates that the effect of mechanical constraints on stress generation in the particles differs in the lithiation and delithiation process.