Lithium-ion Battery Performance Degradation Research Articles

Targeting the low-temperature performance degradation of lithium-ion batteries: A non-destructive bidirectional pulse current heating framework

The poor low-temperature performance of lithium-ion batteries (LIBs) significantly impedes the widespread adoption of electric vehicles (EVs) and energy storage systems (ESSs) in cold regions. In this paper, a non-destructive bidirectional pulse current (BPC) heating framework considering different BPC parameters is proposed. To determine the lithium plating boundary of graphite anode, a three-electrode battery is first fabricated. Then, an electro-thermal coupled model is established and the model parameters consider not only the effects of temperature and heating frequency but also the effect of current amplitude. Subsequently, a novel lithium plating criterion is derived based on the thermodynamic theory and the graphite anode model. Finally, leveraging the electro-thermal coupled model and considering the constraints of lithium plating and terminal voltage, the effects of different BPC heating parameters on battery heat generation are discussed and a novel BPC heating strategy is designed. The results show that the heat generation power is highest when the ratio of discharge time to charge time is 0.55. Additionally, the BPC heating strategy proposed in this paper is 45 s faster than the traditional BPC heating strategy when the battery is heated from -15 °C to 10 °C at 100 Hz. The heating rate of the new BPC strategy is 3.51 °C/min and there is no observed capacity degradation after 100 heating cycles. The BPC heating framework proposed in this paper effectively improves the low-temperature performance of the battery and contributes positively to the promotion of EVs and ESSs in cold regions.

Design and experiment of a low-temperature charging preheating system for power battery packs with an integrated dissipative balancing function

The performance degradation of lithium-ion batteries (LiB) at low temperatures, as well as variability among batteries after battery grouping, limit the application range of electric vehicles (EVs). A low-temperature preheating method for power battery packs with an integrated dissipative balancing function is proposed in this research. The system builds its topology structure and sets the placement of heating units in the battery pack, as well as using heating units as preheating elements and balancing resistors. Furthermore, this paper presents an online balancing control technique to completely utilize the minimal capacity single battery module and formulates the balancing control threshold for different SOCnew states. The results showed that the temperature rise and temperature difference caused by the battery pack balancing process are within the range of 7 °C under the full operation conditions of balancing resistance at an ambient temperature of 20 °C. Under a −20 °C ambient temperature, the preheating system can raise the average temperature of the battery pack to 16 °C within 40 min and keep the temperature inside the battery pack above 0 °C for the following 4 h. This paper provides a new idea for coupling the research of efficient balancing technology and low-temperature preheating technology for power battery packs.

Internal fault diagnosis method for lithium batteries based on a failure physical model

Performance degradation of lithium-ion batteries is directly related to failure mechanisms such as loss of internal active lithium ions or active material. For in-service batteries, internal failures or degradation cannot be directly observed, and it is difficult to analyze and diagnose them through limited external characteristic measurements. To effectively diagnose lithium-ion batteries failure behaviors, an internal fault diagnosis method is proposed. Firstly, a failure physical model containing side reactions is established, aiming at studying the inherent relationship between the parameters of the failure physical model and internal faults. Then, a method of constructing parameter failure boundaries is proposed to diagnose whether aging failures including electrolyte decomposition, active particle rupture, SEI film growth, and lithium plating occur inside the battery by tracking and analyzing the evolution laws of the model parameters. Finally, abnormal batteries with faster capacity degradation in the same group are selected, and based on destructive characterization testing, the effectiveness and reliability of the proposed non-destructive internal fault diagnosis method are verified.

Analysis of Performance Degradation in Lithium-Ion Batteries Based on a Lumped Particle Diffusion Model.

The analysis of performance degradation in lithium-ion batteries plays a crucial role in achieving accurate and efficient fault diagnosis as well as safety management. This paper proposes a method for studying the degradation pattern of lithium-ion batteries and establishing the structure-activity relationship between internal and external parameters by employing a lumped particle diffusion model. To simulate real-world operating conditions, a cycle life test was conducted with the constant current-constant voltage (CC-CV) charge mode and the discharge mode under New European Driving Cycle (NEDC) working condition. The test aimed to analyze the variations in the external macroscopic characteristic parameters of the battery. Building upon this analysis, a lumped particle diffusion model was constructed, and the model parameters were identified using the Levenberg-Marquardt (L-M) algorithm. Subsequently, the ohmic, activation, and concentration losses of the battery under different aging conditions were determined, revealing the internal state evolution during the degradation process of lithium-ion batteries. The findings indicate that the lumped particle diffusion model provides a comprehensive explanation of the internal mechanisms contributing to the performance degradation of lithium-ion batteries. Moreover, the proposed method offers a novel perspective for the real-time quantitative analysis of lithium-ion battery performance degradation.

Effects of Zn, Mg and Cu Doping on Oxidation Reaction of Al (111) Surface

Aiming at the performance degradation of lithium-ion batteries due to shell corrosion, the doping of alloy elements Zn, Mg and Cu on Al (111) surface and the effect on oxidation reaction of Al (111) surface were studied by the first-principles calculation method. The results show that Zn, Mg and Cu atoms stably combine with Al atoms, and the surface smoothness is slightly different due to their different radii and electronegativity. The dissociative adsorption of O2 molecules is related to the surface doping atoms and O2 coverage, while the electron tunneling of underlying metal promotes O2 adsorption on the surface. As O2 coverage increases, the O atoms adsorbed on the hcp site gradually migrate to the subsurface layer. Zn, Mg, Cu and vacancy defect hinder the migration of the surrounding O atoms to subsurface layer, resulting in different structures and thicknesses of the oxide film near the doped atoms. At the same time, Zn, Mg, and Cu atoms differ in their ability to gain or lose electrons compared with Al atoms, resulting in their different positions on the surface. In addition, the surface work function rises with the increase of O2 coverage, and Zn and Cu atoms make the work function increase faster. Finally, according to the research results, it can be inferred that Zn and Mg are the unfavorable factors for the oxidation reaction of Al surface.

A polynomial scale transformation and improved Wiener process for a novel lithium-ion battery performance degradation model: remaining useful life performance

A polynomial scale transformation and improved Wiener process for a novel lithium-ion battery performance degradation model: remaining useful life performance

Experimental investigation of the influence of electrolyte loss and replenishment on the critical performances of cylindrical lithium-ion cells

The loss of electrolytes is a non-negligible aging mode that could lead to the performance degradation of lithium-ion batteries, and electrolyte replenishment may be a potential scheme for battery performance recovery. In this study, a series of cylindrical 18,650 cells with different electrolyte losses and replenishments are prepared, and then the relationship between electrolyte residuals and battery performances is experimentally investigated. The results show that the capacity reduction will be significant when the electrolyte residual is less than 70%, and the solid electrolyte interphase, ohmic impedance, and reaction impedance increase suddenly. Severe capacity fading (rollover failure) can be clearly observed when the electrolyte residual is less than 50%. Moreover, the feasibility of restoring battery performance by refilling electrolytes is investigated experimentally. The results show that the capacity and impedance of the cells with different electrolyte losses are extensively restored after electrolyte replenishment, and the rollover failure is disappeared. This study provides a valuable reference for understanding the mechanism of battery aging and rollover failure, and proposes a new idea for the secondary utilization of the aged cells.

Investigation the Degradation Mechanisms of Lithium-Ion Batteries under Low-Temperature High-Rate Cycling

Low-temperature high-rate cycling leads to accelerated performance degradation of lithium-ion batteries, which seriously hampers the large-scale popularization of electric vehicles. To clarify the battery degradation characteristics and mechanisms, this work conducts an in-depth investigation on the commercial lithium-ion batteries with 37 A h during the long-term cycling under low-temperature high-rate charging. The battery capacity displays the decelerating degradation trend during the long-term cycling, and the battery capacity recovery rate is as high as 80–90%. Furthermore, it is interesting that the constant current discharge capacity exhibits jumping behavior at around 120th cycle during the discharge process. By postmortem characterization analysis, it is found that lithium plating is the primary degradation mechanism. Lithium plating exhibits nonuniformity and displays a decelerating trend in the later stage. In view of the lower temperature, the plated lithium can better retain the reaction activity, which results in a higher capacity recovery rate. Besides, lithium plating increases the internal polarization, which makes the constant current discharge capacity jump when lithium plating reaches a certain level. The findings can provide certain references for battery optimization.

Mathematical Models for the Performance Degradation of Lithium-Ion Batteries with Different Status of Charge (SOC) in Long-Term High Temperature Storage

Mathematical models to evaluate and predict the performance degradation of lithium-ion batteries (LIBs) with different status of charge (SOC) in long-term high-temperature storage which are also applicable for setting rational storage conditions (temperature, SOC, and time) of LIBs were established. Parameters including voltage drop (Delta V), reversible capacity (RC) loss, and internal impedance (IMP) increase of LIBs under different temperature (60 °C, 45 °C, and 25°C) are used to allow the model to clarify its function. According to the results obtained from commercial 18650 cylindrical batteries with LiNi0.33Co0.33Mn0.33O2 cathode, the mathematical relationship between Delta V and storage days (x) is fitted into a simple formula: Delta V = m.In(x)-n, and similarly, RC loss = m′.exp (n′.x) and IMP increase = m″.xn″ can also be acquired. In these formulas, m, n, m′, n′, m′′ and n′′ are constants when temperature and SOC are fixed. If only the temperature is fixed, the value of these constants can be derived into a function with SOC (y), respectively, while further plugging the function into the calculation formula of Delta V, RC loss, and IMP increase, respectively, allows the mathematical models to be set up.

Remaining capacity prediction of lithium-ion battery based on the feature transformation process neural network

In order to improve the prediction accuracy of discrete time series data, a lithium-ion battery remaining capacity prediction model based on feature transformation process neural network is proposed. According to the time series characteristics of lithium-ion battery performance degradation data, the remaining capacity prediction of lithium-ion battery is converted into a functional approximation method. The integral operation of continuous function is used to realize the time accumulation effect of network input data. In order to simplify the integral operation of continuous function, a discrete Walsh transform is performed on the input data, and the integral operation of continuous function is transformed into the inner product operation of the discrete Walsh transform pair. This method simplifies the integral operation of the continuous function and eliminates the loss of precision caused by the continuity of discrete time series data. A Levenberg-Marquardt network weight learning algorithm based on the discrete Walsh transform is developed. The model and algorithm are applied to predict the remaining capacity of lithium-ion batteries. The experimental results show that the model can reduce the average absolute error, average relative error and root mean square error of lithium-ion battery remaining capacity prediction to 0.0231AH, 1.73% and 0.0299 respectively.

Copper Zinc Tin Sulfide Anode Materials for Lithium-Ion Batteries at Low Temperature

Developing novel materials is crucial to overcoming the performance degradation of lithium-ion batteries (LIBs) for low-temperature applications. In this work, we demonstrate a novel copper zinc tin sulfide (Cu2ZnSnS4, CZTS) thin film with nanowalls structure as the anode material in thin-film LIBs for low-temperature applications. The quaternary CZTS synthesized by a simple hydrothermal method shows a higher reversible capacity of 475 mAh g–1 after 200 cycles at −10 °C with the EC/DEC/DMC-based electrolyte in comparison with the graphite electrode (110 mAh g–1 after 100 cycles at −10 °C). The effects of temperature and electrolyte systems including EC/DEC- and EC/DEC/DMC-based electrolytes on the cycling performance are studied. The faster Li-ion transport in the electrolyte–electrode interface of the CZTS anode material is obtained in the EC/DEC/DMC-based electrolyte at −10 °C. In addition, the depth-profiling XPS results of the CZTS anode reveal that a solid electrolyte interphase (SEI) layer with less carbon content is formed in the EC/DEC/DMC-based electrolyte likely associated with the interfacial stability at low temperature. The enhanced cycling performance of CZTS could be attributed to its improved interfacial stability and Li+ diffusion, along with the formation of an interconnected active material architecture at low temperature.

Temperature, Ageing and Thermal Management of Lithium-Ion Batteries

Heat generation and therefore thermal transport plays a critical role in ensuring performance, ageing and safety for lithium-ion batteries (LIB). Increased battery temperature is the most important ageing accelerator. Understanding and managing temperature and ageing for batteries in operation is thus a multiscale challenge, ranging from the micro/nanoscale within the single material layers to large, integrated LIB packs. This paper includes an extended literature survey of experimental studies on commercial cells investigating the capacity and performance degradation of LIB. It compares the degradation behavior in terms of the influence of operating conditions for different chemistries and cell sizes. A simple thermal model for linking some of these parameters together is presented as well. While the temperature appears to have a large impact on ageing acceleration above room temperature during cycling for all studied cells, the effect of SOC and C rate appear to be rather cell dependent.Through the application of new simulations, it is shown that during cell testing, the actual cell temperature can deviate severely from the reported temperature depending on the thermal management during testing and C rate. It is shown, that the battery lifetime reduction at high C rates can be for large parts due to an increase in temperature especially for high energy cells and poor cooling during cycling studies. Measuring and reporting the actual battery (surface) temperature allow for a proper interpretation of results and transferring results from laboratory experiments to real applications.

Lithium-ion battery performance degradation evaluation in dynamic operating conditions based on a digital twin model

The performance of lithium-ion batteries degrades over time. Evaluating the performance degradation for lithium-ion batteries is essential to ensure the operational reliability and reduces the risk of host-system downtime. The battery capacity that is obtained by completely charging and discharging a battery cell, directly reflects the performance of a lithium-ion battery. But in practical applications, the battery is dynamically charged and discharged. This makes it difficult to measure the actual capacity and further evaluate battery performance degradation to ensure the battery operating safety. To address this challenging issue, this paper proposes a performance degradation evaluation model by estimating the battery actual capacity in dynamic operating conditions. A health indicator (HI) is extracted from the measurable parameters to reflect the battery performance degradation. A battery digital twin model that describes the relationship between the cell voltage and the cell state-of-charge (SOC) are modelled by the long short-term memory (LSTM) algorithm, which takes the HI as a temporal measurement. The battery actual capacity can be obtained by virtually completely discharging this digital twin model. The experimental results illustrate the potential of the proposed method applying in dynamic operating conditions.

Suppressing transition metal dissolution and deposition in lithium-ion batteries using oxide solid electrolyte coated polymer separator**Project supported by the National Key R&D Program of China (Grant No. 2016YFB0100100) and the National Natural Science Foundation of China (Grant Nos. 51822211, U1932220, U1964205, and U19A2018).

The dissolution of transition metal (TM) cations from oxide cathodes and the subsequent migration and deposition on the anode lead to the deconstruction of cathode materials and uncontrollable growth of solid electrode interphase (SEI). The above issues have been considered as main causes for the performance degradation of lithium-ion batteries (LIBs). In this work, we reported that the solid oxide electrolyte Li1.5Al0.5Ti1.5(PO4)3 (LATP) coating on polyethylene (PE) polymer separator can largely block the TM dissolution and deposition in LIBs. Scanning electron microscopy (SEM), second ion mass spectroscopy (SIMS), and Raman spectroscopy characterizations reveal that the granular surface of the LATP coating layer is converted to a dense morphology due to the reduction of LATP at discharge process. The as-formed dense surface layer can effectively hinder the TM deposition on the anode electrode and inhibit the TM dissolution from the cathode electrode. As a result, both the LiCoO2/SiO-graphite and LiMn2O4/SiO-graphite cells using LATP coated PE separator show substantially enhanced cycle performances compared with those cells with Al2O3 coated PE separator.

An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation

Capacity estimation is of great significance to help assess the performance degradation of lithium-ion batteries, so as to take actions to extend their lifetime. Traditional capacity estimation methods for Lithium-ion batteries are usually based on individual model-based or data-driven prognostic approaches. However, no single prognostic method performs appropriately for all possible situations as each individual method presents particular assumptions and application limitations. Therefore, this paper presents an ensemble prognostic framework that combines multiple individual prognostic algorithms to improve the accuracy and robustness of battery capacity estimation. In the proposed ensemble prognostic framework, the degraded capacity data of the full battery life cycles are divided into three parts: a training dataset, a validation dataset, and a test dataset, among which the training and validation datasets are employed for member prognostic model training, the validation dataset is utilized for weight calculation, and the test dataset is used for prognostic performance assessment. A validation-data based induced ordered weighted averaging (IOWA) operator, i.e. V-IOWA operator, is proposed to realize time-varying weight assignment. By summing the weighted prognostic results of each member prognostic algorithm, the ensemble prognostic results are finally obtained. Effectiveness of the proposed approach was validated based on datasets provided by NASA Ames Prognostics Center of Excellence. The experiment results indicated that the proposed ensemble prognostic approach outperforms individual prognostic algorithms with a higher accuracy.

Modelling Lithium-Ion Battery Ageing in Electric Vehicle Applications—Calendar and Cycling Ageing Combination Effects

Battery ageing is an important issue in e-mobility applications. The performance degradation of lithium-ion batteries has a strong influence on electric vehicles’ range and cost. Modelling capacity fade of lithium-ion batteries is not simple: many ageing mechanisms can exist and interact. Because calendar and cycling ageings are not additive, a major challenge is to model battery ageing in applications where the combination of cycling and rest periods are variable as, for example, in the electric vehicle application. In this work, an original approach to capacity fade modelling based on the formulation of reaction rate of a two-step reaction is proposed. A simple but effective model is obtained: based on only two differential equations and seven parameters, it can reproduce the capacity evolution of lithium-ion cells subjected to cycling profiles similar to those found in electric vehicle applications.

Novel Lithium-Ion Battery State-of-Health Estimation Method Using a Genetic Programming Model

State-of-health (SOH) is a health index (HI) that directly reflects the performance degradation of lithium-ion batteries in engineering, but the SOH of Li-ion batteries is difficult to measure directly. In this paper, a novel data-driven method is proposed to estimate the SOH of Li-ion batteries accurately and explore the relationship-like mechanism. First, the features of the battery should be extracted from the performance data. Next, by using the evolution of genetic programming to reflect the change in SOH, a mathematical model describing the relationship between the features and the SOH is constructed based on the data. Additionally, it has strong randomness in the formula model, which can cover most of the structural space of SOH and features. An illustrative example is presented to evaluate the SOH of the two batches of Li-ion batteries from the NASA database using the proposed method. One batch of batteries was used for testing and comparison, and another was chosen to verify the test results. Through experimental comparison and verification, it is demonstrated that the proposed method is rather useful and accurate.

Lithium-ion Battery Performance Degradation Recognition Method based on SOC Estimation

Lithium-ion Battery Performance Degradation Recognition Method based on SOC Estimation

Assessing the Performance Degradation of Lithium-Ion Batteries Using an Approach Based on Fusion of Multiple Feature Parameters

A method based on fusion of multiple features is proposed to assess and accurately describe the performance degradation of lithium-ion batteries in this paper. First, the discharge voltage signal of lithium-ion batteries under real-time monitoring is analyzed from the perspective of time domain and complexity to obtain the values of multiple features. Then, the multi-feature parameters undergo a spectral regression process to reduce the number of dimensions and to eliminate redundancy, and on the basis of this regression, a Gaussian mixture model is established to model the health state of batteries. Thus, the degree of lithium-ion battery performance degradation can be quantitatively assessed using the Bayesian inference-based distance metric. A case calculation experiment is carried out to verify the effectiveness of the method proposed in this paper. The experimental results demonstrate that, compared with other assessment methods, the performance degradation assessment method proposed in this paper can be used to monitor the degradation process of lithium-ion batteries more effectively and to improve the accuracy of condition monitoring of batteries, thereby providing powerful support for making maintenance decisions.

In-Situ Stress Evolution in Thin Film Electrodes during Electrochemical Cycling in Li-Batteries

Electrochemical cycling induced mechanical damage of electrode materials actively contributes towards the performance degradation of lithium-ion batteries. The correlation between mechanical damage and performance degradation in anode materials that show large volume changes, such as silicon, graphite and tin, has been amply demonstrated. On the hand, typical oxide electrodes undergo only a few % volume changes, and the non-reversible nature of the crystal structure evolution as a function of lithium concentration in such electrodes is, in general, believed to be the limiting factor for performance degradation. For example, cycling in LixCoO2 is generally limited to 0≤x≤0.5, mainly, due to the irreversibility associated with the crystal structure changes beyond further Li extraction/re-insertion. However, due to their brittle nature only a few % volume changes can have significant implication on the mechanical damage leading to performance degradation for such ceramic oxide electrodes. Thus, the issue of electrochemical cycling induced mechanical degradation in oxide electrodes is being actively explored in recent years [1-2]. In this work, we present in-situ stress evolution of i) two canonical cathode systems, namely layered LiCoO2 and spinel LiMn2O4 and ii) one conversion electrode system, Co3O4 in thin film configuration to quantify the driving force leading to the mechanical degradation. In-situ stress evolution in thin film electrodes was measured by monitoring the change in the elastic substrate curvature during electrochemical cycling in a suitably designed beaker cell using multiple-beam optical sensing (MOS) method. Thin films of the electrodes were prepared using solution deposition technique. Structural characterizations using XRD and Raman spectroscopy showed predominant presence of desired (poly)crystalline phases in the as prepared samples. In addition, SEM images also revealed the presence of dense microstructural features in the as prepared films. During Li-extraction from layered LixCoO2, there was almost linear increase in compressive stress up to ~50% Li removal, which is consistent with its lattice parameter evolution during Li removal [3], and a maximum compressive stress of ~0.35 GPa was observed for x~0.5. Upon lithiation there was almost reversible stress evolution in LixCoO2. Similar behavior was also observed for subsequent cycles as well, while limiting the upper charging cut-off voltage to 4.3V. On the other hand, initial delithiation from spinel LixMn2O4 induces tensile stress up to ~4.1V, beyond which the induced stress reverses direction (termed as “compressive drop”) with further delithiation (up to 4.3 V). This reversal of stress evolution in the later stages of delithiation from spinel LixMn2O4 is in apparent contradiction with the lattice parameter evolution of spinel LixMn2O4 during lithium extraction [4]. Upon lithium re-insertion (up to 3.5V), induced compressive stress increases linearly. The subsequent cycles (in the 4V region), however, did not show any “compressive drop” during later stages of delithiation and the induced stress evolved reversibly during delithiation-lithiation. The origin of this first cycle “compressive drop” in spinel LiMn2O4 is not known at present. In an attempt to establish the origin of the observed first cycle “compressive drop” in spinel LiMn2O4 thin films, stress measurement data varying multiple parameters including cathode film thickness, reannealing a cycled electrode will be presented. The effect of stress evolution in these thin film electrodes during cycling as a function of cycling voltage window and current density will also be presented and discussed in the light of their crystal structural changes. References D. J. Miller, C. Proff, J. G. Wen, D. P. Abraham and J. Bareno, Adv. Energy Mater., 3, 1098 (2013).W. H. Woodford, W. C. Carter and Y. M. Chiang, Energy Environ. Sci., 5, 8014 (2012).J. N. Reimers and J. R. Dahn, J. Electrochem. Soc., 139, 2091 (1992).Y. Xia and M. Yoshio, J. Electrochem. Soc., 143, 825 (1996).