Entire Battery Research Articles

Optimizing Glass Foam Production from Recycled Sources: Influence of Variation in Cullet Color and Spent Alkaline Battery Components

Glass foams present a compelling opportunity for upcycling glass waste, offering a favorable combination of low weight, thermal insulation, and mechanical strength. Prior works demonstrated the feasibility of producing glass foams from glass waste and foaming agents sourced from synthetic textile waste, manganese oxides, and spent alkaline battery cathodes. This work explores the optimization of the process, investigating the impact of glass composition of differently colored glasses on final properties and incorporating entire alkaline batteries, encompassing both cathode and anode components. By carefully adjusting the composition, foaming agent content, and process temperature, customizable properties are achieved. Increasing foaming agent content or utilizing transparent glass improves insulation but lowers density and mechanical properties. Lowering foaming agent content or using brown/green glass enhances density and strength at the expense of insulation. Temperatures beyond 900 °C increase crystalline content, boosting mechanical performance without affecting density. This innovative approach not only offers a pathway to sustainable insulation materials but also underscores the potential for minimizing environmental impact through the efficient upcycling of glass waste.

Content validation of a test battery for assessing sleep quality in adolescents in Slovenia

Abstract Background Sleep constitutes a fundamental biological necessity, occupying approximately one-third of our lifespan. Consequently, the field of sleep research has experienced an increase in interest, prompted by the recognition of insufficient sleep as a significant public health challenge. As the majority of existing questionnaires in Slovenia have yet to undergo official translation and validation processes, the aim of our study was to conduct content validation of a questionnaire designed to assess the sleep quality among adolescents. Methods We developed a test battery for assessing the sleep quality of adolescents by combining a diverse array of questionnaires. We employed the Adolescent Sleep-Wake Scale, Pediatric Daytime Sleepiness Scale, Epworth Sleepiness Scale for Children and Adolescents, and Children’s Morningness-Eveningness Scale. Following the development of the entire test battery, a process of back-translation from English to Slovene occurred, followed by cultural adaptation. Results The designed questionnaire underwent content validation by a panel of 10 experts drawn from diverse disciplines. Evaluation of questionnaire items followed a planned methodology tailored for the content validation of measurement instruments, focusing on content relevance, clarity, and classification into subscales or factors. Our analysis revealed that the individual questionnaires demonstrate content validity, supported by notably high indices of content validity. Conclusions Translated questionnaires demonstrate decent content validity, thus rendering them valid clinical and research tools for assessing sleep quality in adolescents in Slovenia. Key messages • Our research indicates the availability of content-valid tools for sleep research. • Further investigation is needed to determine the reliability, specificity, sensitivity, and appropriateness of the factorial structure of measures for the quality of sleep.

Investigation of SDS on Corrosion Behavior and Discharge Performance of AZ31B Magnesium Alloys in Aqueous Magnesium Air Batteries

To address the issue of high self-corrosion rate of Mg anodes in seawater batteries leading to low discharge efficiency, the green inhibitor, sodium dodecyl sulfate (SDS), was utilized to enhance the discharging performance of AZ31B alloy. The addition of SDS in a 3.5% NaCl solution led to a reduction in the corrosion rate of AZ31B alloy due to the formation of S-containing compounds. During the continuous-discharging period, the addition of 0.05 M SDS increased the anodic utilization efficiency from 64% to 81% and the specific energy density of the entire battery elevated from 1280 to 1912 mAh g−1 (an increase of 49%). The enhancement of the discharge performance of AZ31B Mg alloy in a 3.5% NaCl solution by SDS can be attributed to three aspects: reducing the negative difference effect of the Mg alloy, decreasing the accumulation of corrosion products on the surface of the Mg alloy, and promoting a more uniform dissolution of the Mg alloy.

Hierarchical equalization scheme for retired lithium-ion battery packs based on inductor-flyback transformer

Some lithium-ion batteries can be reused after sorting and recombination. Equalization of these batteries during the process of step utilization is an effective way to improve the inconsistency of battery packs and extend their lifespan. This paper proposes a hierarchical equalization topology based on a combination of inductor and transformer, which is divided into intra-group and inter-group battery equalization. The intra-group equalization circuit achieves energy transfer between non-adjacent individual batteries through an improved Buck-Boost circuit. The inter-group equilibrium circuit exchanges energy between any battery subpackages and the entire battery pack using a flyback transformer. An LCD (Inductance-Capacitor-Diode) lossless absorption network is added to the transformer to absorb voltage spikes caused by leakage inductance while achieving soft switch to reduce switch losses. Considering that retired lithium-ion batteries can affect battery capacity and State of Charge (SOC) estimation accuracy due to aging issues, a modified Extended Kalman Filter (EKF) algorithm is proposed to improve battery SOC estimation accuracy. A fuzzy logic controller is introduced into the SOC-based control strategy to improve the equalization efficiency of the battery pack. Finally, to validate the effectiveness of the proposed equalization scheme, a model is built and simulated using MATLAB/Simulink software. The experimental results show that compared with the traditional double-layer inductor equalization scheme using the most value equalization algorithm and fuzzy logic control algorithm respectively, this equalization scheme has a faster equalization speed and higher equalization efficiency.

Extraordinary Corrosion Inhibition of Potassium Stannate and Riboflavin Hybrid Additive on the Al Alloy Anode of Alkaline Al‐Air Batteries

AbstractAl‐air batteries (AABs) are regarded as a promising future alternative energy source due to their supposedly high energy density. The corrosion of AABs resulting from the hydrogen evolution corrosion and self‐corrosion occurring during their use in alkaline solutions represented a significant obstacle to their development, considerably limiting their commercialization. In this study, we examined the corrosion inhibition efficiency of the hybrid anticorrosive agent composed of potassium stannate (K2SO3) and riboflavin (Rib) when used as an addition in the electrolyte of an alkaline AAB. The experimental results demonstrated that the incorporation of the hybrid corrosion inhibitor leads to an anticorrosion inhibition efficiency of 70.2%, an increase in the discharge specific capacity, and specific energy of the entire battery from 913 mAh g−1 and 1023 Wh kg−1 to 2128 mAh g−1 and 2511 Wh kg−1, respectively. The adsorption behavior of the additives on the surface of the Al alloy was studied using surface characterization techniques such as scanning electron microscopy (SEM), energy‐dispersive X‐ray spectroscopy (EDS), atomic force microscopy (AFM), and X‐ray photoelectron spectroscopy (XPS). It revealed that the incorporation of the hybrid additive leads to the formation of a dense and uniform protective layer on the surface of the Al anode during the discharge process. This film effectively prevents direct contact between the alkaline electrolyte and the Al surface, drastically reducing the formation of possible hydrogen interactions between the electrolyte and the anode. Consequently, the hydrogen evolution reaction (HER) is efficiently inhibited, and the overall performance of the AAB is improved. Thus, this electrolyte regulation strategy presents a novel approach to studying high‐efficiency alkaline AABs.

Experimental playback of urban noise does not affect cognitive performance in captive Australian magpies.

Exposure of wildlife to anthropogenic noise is associated with disruptive effects. Research on this topic has focused on behavioural and physiological responses of animals to noise, with little work investigating links to cognitive function. Neurological processes that maintain cognitive performance can be impacted by stress and sleep disturbances. While sleep loss impairs cognitive performance in Australian magpies, it is unclear whether urban noise, which disrupts sleep, can impact cognition as well. To fill this gap, we explored how environmentally relevant urban noise affected the performance of wild-caught, city-living Australian magpies (Gymnorhina tibicen tyrannica) on a cognitive task battery including associative and reversal learning, inhibitory control, and spatial memory. Birds were housed and tested in a laboratory environment; sample sizes varied across tasks (n=7-9 birds). Tests were conducted over 4weeks, during which all magpies were exposed to both an urban noise playback and a quiet control. Birds were presented with the entire test battery twice: following exposure to, and in the absence of, an anthropogenic noise playback; however, tests were always performed without noise (playback muted during testing). Magpies performed similarly in both treatments on all four tasks. We also found that prior experience with the associative learning task had a strong effect on performance, with birds performing better on their second round of trials. Like previous findings on Australian magpies tested on the same tasks in the wild under noisy conditions, we could not find any disruptive effects on cognitive performance in a controlled experimental laboratory setting.

Verification of Screening Conditions Related to Reuse of Used Lithium-Ion Batteries

（Introduction, Background/Research objectives）Lithium-ion batteries (hereinafter LIB) are becoming explosively popular around the world, and demand for electric vehicles (hereinafter EV) is particularly remarkable. However, lithium and rare metals are finite and their prices are soaring worldwide, which is hindering LIB market growth. On the other hand, technology for recycling and reusing used LIBs (EVs) is attracting attention. If safety, performance, and economic efficiency are ensured, a new market will be created and it will also contribute to the realization of a recycling-oriented society.However, there are many challenges to actually reusing LIBs, so we will consider screening methods that enable LIB reuse in an optimal, safe, and low-cost manner.This time, we will perform deterioration diagnosis and capacity estimation of reused LIBs whose usage history is unknown, and construct assembled batteries. The battery is actually installed in an EV, driven, and the battery status monitored. We will devise combinations of assembled batteries and verify the accuracy required for screening in order to achieve both performance and economy.Keywordsreused LIB, BMS, LIB screening, EVProposed approachUp until now, we have been able to obtain satisfactory results with battery packs that have taken time to ensure that battery deterioration and capacity are as consistent as possible. However, this time, we purposely created an assembled battery by adding batteries with different deterioration conditions and different capacities, and monitored what kind of behavior and performance changes occur when installed and operated in an EV. We will verify these results and provide feedback to the screening method.Materials and methodsFirst, we screened 100 LIB modules taken from an EV that had traveled 100,000 km. The screening method involves estimating capacity using a charger/discharger manufactured in-house and diagnosing deterioration by measuring internal resistance. These LIBs will form a 12-module assembled battery in three patterns and will be installed in a converted EV manufactured in-house. The status of the onboard LIB will be remotely monitored and logged using our own BMS with IoT functionality, and statistical verification will be performed.The following three patterns are used for the 12-module assembled battery configuration.condition A: Match all capacitance and internal resistance.condition B: 1 module capacity is low.condition C: High internal resistance of 1 module.The capacity of the entire battery pack was taken as the average capacity of the installed batteries. 100% SOC was defined as an average voltage of 4.2V, and 0% was defined as an average voltage of 3.2V. Further, the lower limit voltage of each cell is set to 3.2V, and if the OCV of even one cell falls below the lower limit voltage, it is assumed that the battery is used up and the running test is terminated.ResultsThe pack voltage, SOC, and standard deviation when running from SOC 100 to 90% to 5 to 0% are shown in Fig. 1 to 3. The standard deviation was used as an index for observing voltage variations among the 24 cells. Only in the case of condition A, the battery capacity could be used up until the SOC reached 0%, but in the cases of B and C, the battery capacity could not be used up because some cells had a voltage lower than the lower limit voltage when the SOC was 4%. Under all conditions, the variation in each cell voltage tends to increase during acceleration/deceleration (charging/discharging) and at low SOC (20% or less), but we observed a large difference in the magnitude of the variation and the focusing value.Discussion and conclusionIn the case of condition A, the fluctuation and dispersion of the pack voltage are small, and the focusing time and focusing value when the pack voltage varies are also short and small. In the case of condition B, although the pack voltage fluctuation was large, the variation was slightly increased compared to condition A, and the convergence time and convergence value were also slightly increased. Condition C had large fluctuations and dispersions in the pack voltage, and the convergence time when the dispersion occurred was also long and large. From these results, it can be said that the important condition to consider during screening is to match the capacity of the batteries. This result not only improves the accuracy of screening, but also has the potential to increase the reuse rate of used batteries. Figure 1

A novel hybrid electrochemical equivalent circuit model for online battery management systems

Accurate battery modeling and parameter identification play pivotal roles in ensuring safety and reliability across the entire battery life cycle. Equivalent circuit models (ECM) are convenient but do not represent physical characteristics well; in contrast, electrochemical models with strong physical meaning are hard to parameterizing in an online setting. To address these challenges, this paper introduces a novel hybrid electrochemical Equivalent Circuit Model (eECM), which integrates electrochemical processes into an ECM, representing slow-dynamic internal processes with a simplified representation of solid- and liquid-phase diffusion; fast-dynamics are represented by ECM terms. The model is supported by an Adaptive Extended Kalman Filter (AEKF) to manage battery state changes and mitigate noise. To enhance parameter identification, a Fisher information matrix-enhanced Variable Forgetting Factor Recursive Least Squares (Fisher-VFFRLS) approach is employed, guided by the Cramér–Rao bound for identifying the most sensitive data points directly from the discharge cycle. Electrochemical parameters are determined via post-charging rest via a Genetic Algorithm (GA). The proposed methodology is validated on three dynamic cycles—DST, US06, and FUDS-demonstrates the effectiveness of the proposed eECM and parameter identification strategy, with maximum Root Mean Square Error (RMSE) for terminal voltage and State of Charge (SoC) estimation below 0.0076 and 0.0122, respectively.

Carbon emission assessment of lithium iron phosphate batteries throughout lifecycle under communication base station in China

The demand for lithium-ion batteries has been rapidly increasing with the development of new energy vehicles. The cascaded utilization of lithium iron phosphate (LFP) batteries in communication base stations can help avoid the severe safety and environmental risks associated with battery retirement. This study conducts a comparative assessment of the environmental impact of new and cascaded LFP batteries applied in communication base stations using a life cycle assessment method. It analyzes the influence of battery costs and power structure on carbon emissions reduction. Results indicate: When consuming the same amount of electricity in a cascaded battery system (CBS), LFP batteries with a retirement state of health (SOH) range between 76.5 % and 90.0 % can reduce 30.3 % of the global warming potential (GWP) compared to new batteries. From the perspective of battery costs, when the price ratio of new to old batteries is greater than 31.0 %, the GWP of batteries retired at 70.0 % SOH is higher than that of new batteries. As the proportion of renewable energy sources in the power structure increases, the GWP of new batteries in 2035 is 15.0 % lower than in 2020. For batteries retired at 80.0 % SOH, their GWP decreases by 12.3 % compared to 2020. This study offers a new approach to determining the retirement point for LFP batteries from an environmental perspective, promoting carbon emission reduction throughout the entire battery life cycle and the sustainable development of the transportation sector.

Multi-objective optimization of efficient liquid cooling-based battery thermal management system using hybrid manifold channels

Maintaining a battery cell at an optimal temperature improves both its performance and lifespan. This study proposes a cold plate equipped with hybrid manifold channels, positioned at the bottom of a high-capacity 280 Ah LiFeO4 battery pack. Based on the developed whole battery pack model, the response surface method elucidates the functional relationship between design parameters (i.e., the width of parallel channels, the width of manifold channels, the height of parallel channels, and the inlet velocity) and responses (i.e., the flow pressure drop, the temperature difference of the entire battery modules, and the temperature difference of the cold plate). Multi-objective optimization of design parameters is performed to search the Pareto front to maximize thermal performance and minimize flow pressure drop, employing the NSGA-II algorithm. Results reveal that the maximum battery temperature can be limited to 30.73–33.78 °C with a coolant pressure drop ranging from 7.66 kPa to 1.76 kPa, at a heating power of 10 kW/m3 for the battery cell. The optimal design configuration, identified through TOPSIS, limits the maximum battery temperature to an acceptable temperature of 45 °C at a discharging rate of 3C, with a pressure drop below 4.2 kPa. Compared to the 280 Ah LiFeO4 battery with natural air cooling and forced flow immersion cooling systems, the maximum battery temperature with a discharging rate of 1C is reduced by 17.6 °C and 11.7 °C, respectively.

A Fe(III)-driven strategy for efficient closed-loop recovery of critical metals from spent LiNixCoyMnzO2 powder

With the concepts of carbon peaking and carbon neutrality taking hold, the demand for recycling of spent lithium-ion batteries (LIBs) is increasing rapidly. However, current recycling methods are mostly faced with the dilemma of high cost and low efficiency and unable to meet the demand for energy conservation and consumption reduction. Here, inspired by the formation of acid mine drainage (AMD), we propose a Fe(III)-driven recovery approach, in which the leaching of valuable metals is enhanced by the addition of solid reagents Fe2(SO4)3 and pyrite (FeS2) to create an acidic reducing atmosphere. Under optimal conditions, the leaching efficiency of Ni/Co/Mn/Li reached 99.9%. In addition, NH3·H2O-NaOH was employed to adjust the pH of the leachate for stepwise precipitation of Fe and Ni/Co/Mn. Li was finally collected as Li2CO3 and Fe(III) could be recycled. We have also made attempts on Ni/Co/Mn co-precipitation and Li2CO3 resynthesis of regenerated LiNixCoyMnzO2 (NCM). It is predicted that this process can provide a green, efficient and economical closed-loop approach for cathode recovery of LIBs, with far-reaching implications for the entire battery recycling industry.

Improving heat transfer in indirect liquid-based battery thermal management systems through turbulator-equipped conical flow paths

To guarantee the safe operation of Li-Ion Cells (LICs) in various applications, such as electric vehicles, an efficient Battery Thermal Management System (BTMS) is necessary. The design of BTMSs, as documented in the literature, typically involves the use of cooling channels with uniform and smooth configurations. However, these configurations encounter the challenge of temperature non-uniformity in the battery module, which stems from elevated coolant temperatures along the flow direction. In this study, a 3D numerical analysis is conducted to develop a novel indirect liquid-based cooling system for the BTMS of a module with cylindrical LICs. The study explores the effects of conical cooling channels, both with and without twisted turbulators, across various coolant mass flows during a 2C discharge process of LICs. Within the studied ranges, diverging cooling channels improve the overall thermal and hydraulic performance of the BTMS by an average of 40%, increasing the heat transfer coefficient by a maximum of 32.6%, and decreasing pressure drop by up to 35.1%. The application of turbulators proves effective in reducing the maximum temperature difference within the LICs, demonstrating a decrease of approximately 34.2% at the minimum mass flow rate and 74.4% at the maximum mass flow rate. This emphasizes the more substantial impact of turbulator utilization at higher coolant mass flow rates. An increased coolant flow rate leads to a significant enhancement of the heat transfer coefficient, ranging from 70.5% to 79.0% for the BTMS incorporating turbulators. Moreover, the study highlights a more pronounced impact of increasing mass flow rates in the BTMS designed with converging cooling channels compared to those designed with diverging channels. Finally, the results of this analysis demonstrate that the proposed system is capable of efficiently regulating the temperature of the entire battery module, ensuring that it consistently maintains a normal and safe operational range suitable for vehicular applications (below 40 °C).

Multi-objective optimization analysis of air-cooled heat dissipation coupled with thermoelectric cooling of battery pack based on orthogonal design

As the energy supply core of electric vehicles (EVs), the battery's performance is closely related to its temperature. Therefore, the battery thermal management system (BTMS) plays a crucial role in ensuring the vehicle's driving safety and power performance. This paper proposes the air cooling as the primary heat dissipation method, combined with a semiconductor refrigeration sheet (SRS) to improve heat transfer and reduce local high temperature. Firstly, determine the optimum air volume with the U-type air-cooled structure. Additionally, orthogonal analysis is used to investigate the inlet and outlet locations, the front deflector position and length of the shunt chamber, and the rear deflector position on the air-cooling effect. Then, through multi-objective optimization, the optimal air-cooled structure is selected based on the air supply pressure drop. The chosen structure is the same-side dual-exit ventilation with a front deflector located 150 mm from the near-wind end wall and a length of 30 mm, and a rear deflector located 100 mm from the far-wind end wall. Finally, SRS coupled air cooling is used to dissipate the heat. Four SRSs are used to analyze the effect of mounting position and different currents on the battery pack. The findings suggest that the optimal placement for SRSs is parallel to the lower end of the battery module at the far-wind end. When SRSs pass a current of up to 0.4A at 37 ℃, the entire battery module can complete 7200 s discharge with 0.5C. This leads to a decrease in the maximum temperature (Tmax) to 46.09 ℃ and a drop in the temperature uniformity index (δT) to 1.36.

A low-cost all-iron hybrid redox flow batteries enabled by deep eutectic solvents

Redox flow batteries (RFBs) emerge as highly promising candidates for grid-scale energy storage, demonstrating exceptional scalability and effectively decoupling energy and power attributes. Nevertheless, the high cost of vanadium metal hinders the continued commercialization of vanadium redox flow batteries (VRFBs), prompting the exploration of low-cost all-iron RFBs as a viable alternative. In this context, we propose an innovative deep eutectic-based all-iron hybrid RFBs. By synthesizing a deep eutectic solvent through the mixture of choline chloride, ethylene glycol, and an appropriate amount of deionized water, the deficiencies in mass transport performance of the deep eutectic electrolyte have been successfully addressed, while enhancing its conductivity. The deep eutectic solvents facilitate the restructuring of the solvent shell surrounding the negative electrolyte Fe2+, effectively suppressing its hydrolysis. Additionally, the synergistic interaction between the restructured solvation structure and the chelating ring structure formed between Fe2+ and glycine improves the deposition morphology of iron. Simultaneously, utilizing a eutectic-based positive electrolyte improves the solubility of active substances while maintaining excellent cycling stability and mass transport performance. The entire battery system exhibits an average coulombic efficiency exceeding 98 % in a 360-hour charge–discharge cycle at 10 mA/cm2. In the first 66 cycles, the coulombic efficiency remains at 100 %, and the energy efficiency approaches 54 %. This indicates that the battery system developed using deep eutectic solvents demonstrates promising applications within the same category of eutectic-based batteries.

Electric vehicle battery chemistry affects supply chain disruption vulnerabilities

We examine the relationship between electric vehicle battery chemistry and supply chain disruption vulnerability for four critical minerals: lithium, cobalt, nickel, and manganese. We compare the nickel manganese cobalt (NMC) and lithium iron phosphate (LFP) cathode chemistries by (1) mapping the supply chains for these four materials, (2) calculating a vulnerability index for each cathode chemistry for various focal countries and (3) using network flow optimization to bound uncertainties. World supply is currently vulnerable to disruptions in China for both chemistries: 80% [71% to 100%] of NMC cathodes and 92% [90% to 93%] of LFP cathodes include minerals that pass through China. NMC has additional risks due to concentrations of nickel, cobalt, and manganese in other countries. The combined vulnerability of multiple supply chain stages is substantially larger than at individual steps alone. Our results suggest that reducing risk requires addressing vulnerabilities across the entire battery supply chain.

Separator pore size induced oriented Zn deposition

Aqueous zinc batteries (AZBs) are promising alternatives to lead-acid batteries for large-scale energy storage. In this work, we developed a TiO2-grafted polyethylene (TiO2-PE) separator for AZBs with excellent hydrophilicity. The TiO2-PE separator demonstrates the thinnest thickness of 9 μm, significantly decreasing the inactive mass of the entire battery comparing with the commercial glass fiber separator (260 μm). We further prove that the separator pore size can modulate the Zn deposition morphology. The (002)-orientated Zn deposition is induced when the pore size is smaller than the crystal critical length. Therefore, the 0.2 μm pores in TiO2-PE induce a (002)-orientated Zn deposition, while the 2 μm pores in glass fiber lead to a randomly oriented growth of Zn metal. This work highlights the opportunities in the pore size-modulated deposition/dissolution process in Zn and other metal batteries.

Investigation of Heat Transfer Enhancement Techniques on a Scalable Novel Hybrid Thermal Management Strategy for Lithium-Ion Battery Packs

This paper introduces a novel hybrid thermal management strategy, which uses secondary coolants (air and fluid) to extract heat from a phase change material (paraffin), resulting in an increase in the phase change material’s heat extraction capability and the battery module’s overall thermal performance. A novel cold plate design is developed and placed between the rows and columns of the cells. The cold plate contains a single fluid body to improve the thermal performance of the battery module. Experimental studies were conducted to obtain the temperature and heat flux profiles of the battery module. Moreover, a numerical model is developed and validated using the experimental data obtained. The numerical data stayed within ±2% of the experimental data. In addition, the ability of nanoparticles to increase the thermal conductivity of water is examined and it is found that the cooling from the liquid cooling component is not sensitive enough to capture the 0.32 W/m K increase in the thermal conductivity of the fluid. Furthermore, in order to enhance the air cooling, fins were added within the air duct to the cold plate. However, this is not feasible, as the pressure drop through the addition of the fins increased by ~245%, whereas the maximum temperature of the battery module reduced by only 0.6 K. Finally, when scaled up to an entire battery pack at a high discharge rate of 7 C, the numerical results showed that the overall temperature uniformity across the pack was 1.14 K, with a maximum temperature of 302.6 K, which was within the optimal operating temperature and uniformity ranges. Therefore, the developed thermal management strategy eliminates the requirement of a pump and reservoir and can be scaled up or down according to the energy and power requirements.

Aging-Sensitive Fast-Charging for Heavy-Duty Electric Vehicles

To use lithium-ion cells on-board electric vehicles (EVs) safely and efficiently, a battery management system (BMS) is essential. It monitors the battery’s states and controls utilization by limiting e.g., current and temperature. Advanced BMS increasingly use electrochemical models to assess internal states of battery cells and apply usage constraints based on these [1]. The performance of an electrochemical BMS is closely tied to the performance of the underlying model and the accuracy of its parameters [2]. We employ a novel re-parametrization method to update electrochemically constrained optimal fast-charging procedure to minimize battery degradation. The parameters of electrochemical models are typically identified through a combination of physico-chemical characterization techniques and curve fitting [3]. Since model parameters change with aging [4], there is a need to re-parametrize electrochemical models on-board to conserve model accuracy. As basis for this re-parametrization, we propose a novel reference performance test that can be incorporated into a conventional over-night charge and performed intermittently.Optimal control has previously been demonstrated for fast-charging in EV applications [1]. However, electrochemical, and mechanical degradation need to be considered to allow optimal fast charging throughout batteries’ lifetimes. Khalik et al. [5] demonstrated how a charging procedure could be designed a-priori for the entire cycle life, considering a direct implementation of the solid-electrolyte-interphase growth and capacity fade due to the associated lithium consumption. Such approaches can extend battery lifetime, but neither yield optimal charging-procedures at every state-of-health nor handle unknown aging modes on the fly. Our proposed approach involves intermittent identification of electrochemical model parameters and subsequent update of the fast-charging protocol such that it adheres to electrochemical constraints throughout the entire battery lifetime.In this work, summarized in Figure 1, we present this methodology and show results from an experimental study testing the proposed approach vs. conventional charging procedures. The experimental study is performed on state-of-the art automotive cells used in heavy-duty electric vehicles.Figure caption: Comparison of a conventional electrochemically constrained fast-charging procedure with the aging-sensitive approach proposed in the present work.

Cell to Pack Modelling of Li-Ion Batteries for Electric Vehicle Applications: Development and Potential Challenges

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) which are powered by Li-ion batteries (LIBs) have the potential to address the challenges of reducing the dependence on fossil fuels and environmental pollution caused by the transportation sector. However, for the reliable and safe operation of the EVs and HEVs, an accurate estimation of the state of charge (SOC) and state-of-health (SOH) of the battery pack is required. The battery capacity degrades with cycling and calendar ageing over time and usage of the vehicles, which reduces the power capability and driving range of the vehicle. An accurate prediction of the battery SOC and SOH can assist the vehicle user in planning to reach the next charging station or the destination with enough range left before the battery gets fully discharged, hence avoiding annoying circumstances.Battery models have been widely employed in battery health and SOC prediction and can successfully predict the SOC and SOH of individual cells. However, the translation of these models from cell to pack levels adds several unknown critical factors which affect the model prediction capability significantly.Our proposed ECM model is based on the electrical and thermal behavior of individual cells, and it accounts for critical factors such as cell-to-cell variations, temperature gradients, and aging effects. We also incorporate a series-parallel configuration to simulate the behavior of multiple cells at the module and pack levels. By upscaling the model in this way, we can accurately predict the SOC and SOH of the entire battery pack, and provide reliable estimates of the remaining driving range and charging requirements.We anticipate that our proposed ECM model will significantly improve the accuracy of SOC and SOH predictions, which will enable EV/HEV users to better plan their trips, avoid unexpected battery failures, and improve the safety of EV/HEV batteries. Furthermore, our study has the potential to inform the design of future battery technologies and contribute to the transition toward sustainable transportation powered by clean energy sources.

Identification Method and Quantification Analysis of the Critical Aging Speed Interval for Battery Knee Points

The identification of knee points in lithium-ion (Li-ion) batteries is crucial for predicting the battery life, designing battery products, and managing battery health. Knee points (KPs) refer to the transition points in the aging speed and aging trajectory of Li-ion batteries. KPs can be identified using a wealth of aging data and various regression-based methods. However, KP identification relies on a large amount of aging data, which is exceedingly time-consuming and resource-intensive. To overcome this issue, we propose a novel method based on KP characteristics to identify the KPs and critical aging speed. Firstly, we extract the main aging trajectory using curve-fitting techniques. Secondly, we calculate the aging speed at each cycle to identify the KPs. We then explore the relationship between the KPs and cycle life and develop a knee point identification algorithm. The correlation coefficient between the KPs and cycle life provides a valuable indicator of the critical aging speed, enabling accurate identification of KPs. To validate our approach, we apply it to the Li(NiCoMn)O2, LiFePO4, and LiCoO2 cell datasets. Our results demonstrate a strong correlation between the KPs and cycle life for these battery types. By employing our proposed method, KPs can be identified for battery life prediction, product design, and health management. Moreover, we summarize a critical degradation speed of −0.03%/cycle can serve as an empirical threshold for warning against capacity diving and KPs. The statistical transition speed threshold can eliminate the dependence on extensive aging data throughout the entire battery’s lifecycle for identifying capacity knee points.