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[en] Highlights: • Integrated online model identification and SOC estimate is explored. • Noise variances are online estimated in a data-driven way. • Identification bias caused by noise corruption is attenuated. • SOC is online estimated with high accuracy and fast convergence. • Algorithm comparison shows the superiority of proposed method. - Abstract: State of charge (SOC) estimators with online identified battery model have proven to have high accuracy and better robustness due to the timely adaption of time varying model parameters. In this paper, we show that the common methods for model identification are intrinsically biased if both the current and voltage sensors are corrupted with noises. The uncertainties in battery model further degrade the accuracy and robustness of SOC estimate. To address this problem, this paper proposes a novel technique which integrates the Frisch scheme based bias compensating recursive least squares (FBCRLS) with a SOC observer for enhanced model identification and SOC estimate. The proposed method online estimates the noise statistics and compensates the noise effect so that the model parameters can be extracted without bias. The SOC is further estimated in real time with the online updated and unbiased battery model. Simulation and experimental studies show that the proposed FBCRLS based observer effectively attenuates the bias on model identification caused by noise contamination and as a consequence provides more reliable estimate on SOC. The proposed method is also compared with other existing methods to highlight its superiority in terms of accuracy and convergence speed.
[en] Highlights: • Continuous-time system identification is applied in Lithium-ion battery modeling. • Continuous-time and discrete-time identification methods are compared in detail. • The instrumental variable method is employed to further improve the estimation. • Simulations and experiments validate the advantages of continuous-time methods. - Abstract: The modeling of Lithium-ion batteries usually utilizes discrete-time system identification methods to estimate parameters of discrete models. However, in real applications, there is a fundamental limitation of the discrete-time methods in dealing with sensitivity when the system is stiff and the storage resolutions are limited. To overcome this problem, this paper adopts direct continuous-time system identification methods to estimate the parameters of equivalent circuit models for Lithium-ion batteries. Compared with discrete-time system identification methods, the continuous-time system identification methods provide more accurate estimates to both fast and slow dynamics in battery systems and are less sensitive to disturbances. A case of a 2"n"d-order equivalent circuit model is studied which shows that the continuous-time estimates are more robust to high sampling rates, measurement noises and rounding errors. In addition, the estimation by the conventional continuous-time least squares method is further improved in the case of noisy output measurement by introducing the instrumental variable method. Simulation and experiment results validate the analysis and demonstrate the advantages of the continuous-time system identification methods in battery applications.
[en] Highlights: • A simplified impedance model was established from the EIS test. • Online parameters identification method for simplified impedance model was proposed. • Differences of model parameters identified by time-frequency domain were analyzed. • SEI resistance was confirmed as the most sensitive parameter to indicate degradation. • A novel capacity estimation method with the SEI resistance has been proposed. - Abstract: Degradation is a complex and intricate process which relates strongly to the state of health (SoH) of a lithium-ion battery. Due to the ambiguous mechanism and sensitivity to the objective factors of lithium-ion batteries, it is difficult to recognize the degradation state and monitor the SoH of a battery. A recognition method for the degradation state to estimate the remaining capacity online has been presented. First, through the analysis of the results of electrochemical impedance spectroscopy (EIS) tests at different SoHs, the degradation level can be detected by the EIS measurement. Second, according to the fractional order theory, an online parameter identification approach with the fractional order impedance model has been proposed for the degradation analysis. Third, the correlation between variation of parameters and degradation level is discussed and the SEI (Solid Electrolyte Interphase) resistance is extracted to predict the remaining capacity by selecting an appropriate fitting function. Finally, the effectiveness of the presented method is validated by the test data, and the estimation error of the remaining capacity can be guaranteed within 3%.
[en] A rechargeable lithium-ion batteries cathode LiNi0.5Mn0.5O2 with hollow nano/micro hierarchical microspheres (LNMO-HS) is prepared. LNMO-HS with diameters of about 1 μm is composed of approximately 100 nm primary nanoparticles. The initial discharge capacity of LNMO-HS cathode is as high as 181.5 mAh g−1 at 1 C between 2.5 and 4.5 V. The corresponding capacity retention is 96.3% during 100 charge and discharge cycles. The reversible discharge capacities are 152.2 (10 C) and 134.6 mAh g−1 (15 C), respectively. After 1000 cycles at 15 C, the capacity retention of LNMO-HS cathode is up to 95.5%. The superior rate capability and cyclability of LNMO-HS cathode can be attributed to the distinctive hollow nano/micro hierarchical microspherical structures. It could not only effectively reduce the paths of Li ions diffusion, increase contact area between electrodes and electrolyte but also buffer the volume changes during Li ions intercalation/deintercalation processes.
[en] Highlights: • A new framework based on ICA is used to monitor SOH on-board for battery packs. • The applicability of the framework is validated through simulation and experiment. • The method can monitor SOH for pack consisting of cells with various aging paths. • On-board incremental capacity analysis is realized by support vector regression. - Abstract: Incremental capacity analysis (ICA) is a widely used technique for lithium-ion battery state-of-health (SOH) evaluation. The effectiveness and robustness of ICA for single cell diagnostics have been reported in many published work. In this study, we extend the ICA based SOH monitoring approach from single cells to battery modules, which consist of battery cells with various aging conditions. In order to achieve on-board implementation, an IC peak tracking approach based on the ICA principles is proposed. Analytical, numerical and experimental results are presented to demonstrate the utility of the IC peak tracking framework on multi-cell battery SOH monitoring and the effects of cell non-uniformity on the proposed method. Results show that the methods developed for single cell capacity estimation can also be used for a module or pack that has parallel-connected cells.
[en] Silicon microwires as anodes for Li-ion batteries may show good cycling performance over 100 cycles as anodes for Lithium ion batteries without significant capacity losses. The life time of the anode, however, strongly depends on the way the battery is charged. Overcharging or undercharging may have severe negative consequences. This paper studies with the study of the operational voltage range of Si microwire anodes and its dependence on the dimensions of the wires. Cyclic voltammetry is used to identify the potentials for the different lithiation/delithiation events, while a modified cyclic voltammetry technique is used to study the dynamics of those processes. Specially prepared anodes with Si wires of different lengths and widths were used for the study.
[en] The ageing phenomena occurring in various diethyl carbonate/LiPF6 solutions are studied using gamma and pulse radiolysis as a tool to generate similar species as the ones occurring in electrolysis of Li-ion batteries (LIBs). According to picosecond pulse radiolysis experiments, the reaction of the electron with (Li+, PF6-) is ultrafast, leading to the formation of fluoride anions that can then precipitate into LiF(s). Moreover, direct radiation-matter interaction with the salt produces reactive fluorine atoms forming HF(g) and C2H5F(g). The strong Lewis acid PF5 is also formed. This species then forms various R1R2R3P=O molecules, where R is mainly -F, -OH, and -OC2H5. Substitution reactions take place and oligomers are slowly formed. Similar results were obtained in the ageing of an electrochemical cell filled with the same model solution. This study demonstrates that radiolysis enables a description of the reactivity in LIBs from the picosecond timescale until a few days. (authors)
[en] The methods and techniques commonly used in investigating the change of entropy and heat generation in Li cells/batteries are introduced, as are the measurements, calculations and purposes. The changes of entropy and heat generation are concomitant with the use of Li cells/batteries. In order to improve the management and the application of Li cells/batteries, especially for large scale power batteries, the quantitative investigations of the change of entropy and heat generating are necessary. (topical review)
[en] Organic composite electrode materials based on aromatic polyimide (PI) and electron conductive polythiophene (PT) have been prepared by a facilein situchemical oxidation polymerization method. The optimized composite electrode PI30PT delivers a remarkable high-rate cyclability, achieving a high capacity of 89.6 mA h g-1at 20 C with capacity retention of 94% after 1000 cycles.
[en] Highlights: • Embedding boehmite in the cathode improves the flame retardancy of Li-ion batteries. • Encapsulation by poly(urea-formaldehyde) remains boehmite a good fire retardancy. • Encapsulated boehmite shows fire retardancy over 40% without electrochemical sacrifice. - Abstract: The fire extinguishment of a mixture of lithium-ion cathode and electrolyte is investigated based on the use of flame retardants. Four boehmite-based flame-retardants are embedded in the LiFePO4 cathode including 0.95 μm AlOOH-L, 0.35 μm AlOOH-S, and their individual microcapsules. The microcapsules are denoted as en-AlOOH-L and en-AlOOH-S. Here, the shell material is the chemically stable poly(urea-formaldehyde). The rheology of the cathode slurries improves as flame-retardants are added. This is especially significant for cases using microencapsulated boehmites, en-AlOOH-L and en-AlOOH-S. At 5–15 wt%, the smaller AlOOH-S and en-AlOOH-S exhibit much better fire-extinguishing efficiency values—40-50% versus 30-40% for the larger AlOOH-L and en-AlOOH-L. This is because the smaller powder can be easier to form a dense barrier against further decomposition of the combustible materials. Among the four retardants, en-AlOOH-S is the best. It shows good fire retardancy in the cathode/electrolyte mixture without significant electrochemical sacrifice of the cathode.