Batteries, Volume 8, Issue 4 (April 2022) – 10 articles

Cyclic Voltammetry is an analysis method for characterizing the behaviors of electrochemically active materials by measuring current through defined potential sweeps. The current–potential relationship depends on key variables such concentration of electrolyte, electron-transfer rate, and the distance and time of species in relation to the electroactive surface of the material. A MATLAB^® simulation was developed on a diffusion and kinetics basis, simulating the equations of Fick’s second law and Butler–Volmer, respectively, towards understanding the energy-storage mechanisms of cobalt hydroxide electrodes. The simulation was compared to a real cobalt hydroxide system, showing an accurate approximation to the experimentally obtained response and deviations possibly related to other physical/chemical processes influencing the involved species. Full article

The temperature in each cell of a battery system should be monitored to correctly track aging behavior and ensure safety requirements. To eliminate the need for additional hardware components, a software based prediction model is needed to track the temperature behavior. This study looks at machine learning algorithms that learn physical behavior of non-linear systems based on sample data. Here, it is shown how to improve the prediction accuracy using a new method called “artificial feature extraction” compared to classical time series approaches. We show its effectiveness on tracking the temperature behavior of a Li-ion cell with limited training data at one defined ambient temperature. A custom measuring system was created capable of tracking the cell temperature, by installing a temperature sensor into the cell wrap instead of attaching it to the cell housing. Additionally, a custom early stopping algorithm was developed to eliminate the need for further hyperparameters. This study manifests that artificially training sub models that extract features with high accuracy aids models in predicting more complex physical behavior. On average, the prediction accuracy has been improved by $\Delta T_{\mathrm{cell}}=0.01 °\mathrm{C}$ for the training data and by $\Delta T_{\mathrm{cell}}=0.007 °\mathrm{C}$ for the validation data compared to the base model. In the field of electrical energy storage systems, this could reduce costs, increase safety and improve knowledge about the aging progress in an individual cell to sort out for second life applications. Full article

The safety of lithium-ion batteries within electrified vehicles plays an important role. Hazards can arise from contaminated batteries resulting from non-obvious damages or insufficient production processes. A systematic examination requires experimental methods to provoke a defined contamination. Two prerequisites were required: First, the extent and type of contamination should be determinable to exclude randomness. Second, specimens should work properly before the contamination, enabling realistic behavior. In this study, two experimental methods were developed to allow for the first time a controlled and reproducible application of water or oxygen into 11 single-layer full cells (Li₄Ti₅O₁₂/LiCoO₂) used as specimens during electrical cycling. Electrochemical impedance spectroscopy was used to continuously monitor the specimens and to fit the parameters of an equivalent circuit model (ECM). For the first time, these parameters were used to calibrate a machine-learning algorithm which was able to predict the contamination state. A decision tree was calibrated with the ECM parameters of eight specimens (training data) and was validated by predicting the contamination state of the three remaining specimens (test data). The prediction quality proved the usability of classification algorithms to monitor for contaminations or non-obvious battery damage after manufacturing and during use. It can be an integral part of battery management systems that increases vehicle safety. Full article

In recent years, research on lithium–ion (Li-ion) battery safety and fault detection has become an important topic, providing a broad range of methods for evaluating the cell state based on voltage and temperature measurements. However, other measurement quantities and close-to-application test setups have only been sparsely considered, and there has been no comparison in between methods. In this work, the feasibility of a multi-sensor setup for the detection of Thermal Runaway failure of automotive-size Li-ion battery modules have been investigated in comparison to a model-based approach. For experimental validation, Thermal Runaway tests were conducted in a close-to-application configuration of module and battery case—triggered by external heating with two different heating rates. By two repetitions of each experiment, a high accordance of characteristics and results has been achieved and the signal feasibility for fault detection has been discussed. The model-based method, that had previously been published, recognised the thermal fault in the fastest way—significantly prior to the required 5 min pre-warning time. This requirement was also achieved with smoke and gas sensors in most test runs. Additional criteria for evaluating detection approaches besides detection time have been discussed to provide a good starting point for choosing a suitable approach that is dependent on application defined requirements, e.g., acceptable complexity. Full article

For intelligent battery systems that are able to control the current flow for each individual cell, the multilevel inverter is an interesting approach to replace the bidirectional AC/DC-converter and improve flexibility of charging system and signal quality in both directions. Therefore, the cells are modulated by switching varying the duty cycle, the current and the frequency up to the kHz-range. This is only beneficial if the switching does not lead to a significant additional aging. The scientific gap to assess and understand the impact of switching is investigated in this paper by testing 22 high-power 18650 lithium-ion cells (Samsung 25R). The cells are tested at 50 Hz and 10 kHz switching frequency during charge, discharge and charge/discharge at 50% duty cycle. The tests are compared to eight reference tests with continuous current flow performed at the average and the maximum current for charge and discharge, respectively. The results are obtained by evaluating the remaining capacity, resistance, electrochemical impedance spectroscopy and dV/dQ analysis. Before reaching rollover, the investigated cells lose homogeneity and cathode capacity but no significant difference for the aging parameters are found. After rollover, the cell-to-cell variation is greater than the aging induced by the different cycling parameters. Full article

Nail penetration is one of the most critical scenarios for a lithium-ion cell: it involves the superposition of electrical, thermal and mechanical abusive loads. When an electrically conductive nail is introduced into the active layers of a lithium-ion cell, an electric short circuit takes place between the conductive components (electrodes and current collectors). Hence, for this load case, electro-thermal modeling must be performed considering each and every layer of the cell in order to predict the electric quantities and the cell temperature (with numerical models). When standard conic nails are used, as is typical for this class of tests, the electrical contact between conductive components and the nail itself suffers of poor reproducibility mainly due to the separator that interposes between the electrically conductive components. This phenomenon makes it difficult to validate electro-thermal models, since the electrical contact between nail and lithium-ion cell parts cannot be safely determined. In this work, an alternative nail with an optimized ratio between the external surface and volume is presented to overcome this issue. To demonstrate the effectiveness of the designed nail, five tests (with the same conditions) were conducted on five commercial lithium-ion pouch cells, monitoring the tabs voltage and surface temperature. In all tests, thermal runaway was reached within 30 s and the tabs voltage showed comparable behavior, indicating that the short circuit values for all five repetitions were similar. The investigation included the implementation of a detailed layers model to demonstrate how the validation of such model would be possible with the novel data. Full article

Electric vehicles (EVs) and hybrid vehicles (HEVs) are being increasingly utilized for various reasons. The main reasons for their implementation are that they consume less or do not consume fossil fuel (no carbon dioxide pollution) and do not cause sound pollution. However, this technology has some challenges, including complex and troublesome accurate state of health estimation, which is affected by different factors. According to the increase in electric and hybrid vehicles’ application, it is crucial to have a more accurate and reliable estimation of state of charge (SOC) and state of health (SOH) in different environmental conditions. This allows improving battery management system operation for optimal utilization of a battery pack in various operating conditions. This article proposes an approach to estimate battery capacity based on two parameters. First, a practical and straightforward method is introduced to assess the battery’s internal resistance, which is directly related to the battery’s remaining useful life. Second, the different least square algorithm is explored. Finally, a promising, practical, simple, accurate, and reliable technique is proposed to estimate battery capacity appropriately. The root mean square percentage error and the mean absolute percentage error of the proposed methods were calculated and were less than 0.02%. It was concluded the geometry method has all the advantages of a recursive manner, including a fading memory, a close form of a solution, and being applicable in embedded systems. Full article

Fast charging of lithium-ion batteries is often related to accelerated cell degradation due to lithium-plating on the negative electrode. In this contribution, an advanced electrode equivalent circuit model is used in order to simulate fast-charging strategies without lithium-plating. A novel parameterization approach based on 3-electrode cell measurements is developed, which enables precise simulation fidelity. An optimized fast-charging strategy without evoking lithium-plating was simulated that lasted about 29 min for a 0–80% state of charge. This variable current strategy was compared in experiments to a conventional constant-current–constant-voltage fast-charging strategy that lasted 20 min. The experiments showed that the optimized strategy prevented lithium-plating and led to a 2% capacity fade every 100 fast-charging cycles. In contrast, the conventional strategy led to lithium-plating, about 20% capacity fade after 100 fast-charging cycles and the fast-charging duration extended from 20 min to over 30 min due to increased cell resistances. The duration of the optimized fast charging was constant at 29 min, even after 300 cycles. The developed methods are suitable to be applied for any given lithium-ion battery configuration in order to determine the maximum fast-charging capability while ensuring safe and durable cycling conditions. Full article

Lithium-ion batteries (LIBs) have been widely used in various fields. In order to ensure the safety of LIBs, it is necessary to accurately estimate of the state of health (SOH) of the LIBs. This paper proposes a SOH hybrid estimation method based on incremental capacity (IC) curve and back-propagation neural network (BPNN). The voltage and current data of the LIB during the constant current (CC) charging process are used to convert into IC curves. Taking into account the incompleteness of the actual charging process, this paper divides the IC curve into multiple voltage segments for SOH prediction. Corresponding BP neural network is established in multiple voltage segments. The experiment divides the LIBs into five groups to carry out the aging experiment under different discharge conditions. Aging experiment data are used to establish the non-linear relationship between the decline of SOH and the change of IC curve by BP neural network. Experimental results show that in all voltage segments, the maximum mean absolute error does not exceed 2%. The SOH estimation method proposed in this research makes it possible to embed the SOH estimation function in battery management system (BMS), and can realize high-precision SOH online estimation. Full article

Hard carbon is one of the most promising anode materials for sodium-ion batteries. In this work, new types of biomass-derived hard carbons were obtained through pyrolysis of different kinds of agro-industrial biowaste (corncob, apple pomace, olive mill solid waste, defatted grape seed and dried grape skin). Furthermore, the influence of pretreating the biowaste samples by hydrothermal carbonization and acid hydrolysis was also studied. Except for the olive mill solid waste, discharge capacities typical of biowaste-derived hard carbons were obtained in every case (≈300 mAh·g⁻¹ at C/15). Furthermore, it seems that hydrothermal carbonization could improve the discharge capacity of biowaste samples derived from different nature at high cycling rates, which are the closest conditions to real applications. Full article