Battery Lifetime Research Articles

I3--Mediated Oxygen Evolution Activities to Boost Rechargeable Zinc-Air Battery Performance with Low Charging Voltage and Long Cycling Life.

An effective strategy to facilitate oxygen redox chemistry in metal-air batteries is to introduce a redox mediator into the liquid electrolyte. The rational utilization of redox mediators to accelerate the charging kinetics while ensuring the long lifetime of alkaline Zn-air batteries is challenging. Here, we apply commercial acetylene black catalysts to achieve an I3--mediated Zn-air battery by using ZnI2 additives that provide I3- to accelerate the cathodic redox chemistry and regulate the uniform deposition of Zn2+ on the anode. The Zn-air battery performs an ultra-long cycle life of over 600 h at 5 mA cm-2 with a final charge voltage of 1.87 V. We demonstrate that I- mainly generates I3- on the surface of carbon catalysts during the electrochemically charging process, which can further chemically react with OH- to generate oxygen and further revert to I-, thus obtaining a stable electrochemical system. This work offers a strategy to simultaneously improve the cycling life and reduce the charging voltage of Zn-air batteries through redox mediator methods.

Extending battery lifetime of electric-hydraulic hybrid wheel loader through system parameter optimization

Parallel electric hydraulic hybrid powertrain (PEHH) is a promising substitute for electric direct drive to extend battery lifetime. The convex programming (CP) based simultaneous optimization method has been adopted in electric hybrid system parameter optimization, but it is difficult to extend it for PEHH due to its non-convexity. In this paper, a combined exhaustive search – convex programming (ES-CP) system parameter optimization method is proposed to address the non-convexity of PEHH system parameter optimization problem. In this method, the PEHH system parameter optimization problem is modeled and reformulated in convex form and the convex part is decoupled from the optimization problem. The CP method is used for the convex part and the ES method is used for the remained non-convex part. The ES-CP method is applied to the PEHH powertrain to extend its battery lifetime. Optimization results show that the battery lifetime of the ES-CP optimized PEHH system is 6.8% longer than that of the non-optimized system. The computing time of ES-CP is 97% lower than Latin hypercube sampling and 90% lower than genetic algorithm methods.

The Complex and Spatially Heterogeneous Nature of Degradation in Heavily Cycled Li-ion Cells

As service lifetimes of electric vehicle (EV) and grid storage batteries continually improve, it has become increasingly important to understand how cells perform after extensive cycling. The multifaceted nature of degradation in Li-ion cells can lead to complex behavior that may be difficult for battery management systems or operators to model. Accurate characterization of heavily cycled cells is critical for developing accurate models, especially for cycle-intensive applications like second-life grid storage or vehicle-to-grid charging. In this study, we use operando synchrotron x-ray diffraction (SR-XRD) to characterize a commercially manufactured polycrystalline NMC622 pouch cell that was cycled for more than 2.5 years. Using spatially resolved synchrotron XRD, the complex kinetics and spatially heterogeneous behavior of such cells are mapped and characterized under both near-equilibrium and non-equilibrium conditions. The resulting data is complex and multifaceted, requiring a different approach to analysis and modelling than what has been used in the literature. To show how material selection can impact the extent of degradation, we compare the results from polycrystalline NMC622 cells to an extensively cycled single-crystal NMC532 cell with over 20,000 cycles—equivalent to a total EV traveled distance of approximately 8 million km (5 million miles) over six years.

Machine Learning for Battery Quality Classification and Lifetime Prediction Using Formation Data

Machine Learning for Battery Quality Classification and Lifetime Prediction Using Formation Data

Multipath Routing with Dynamic Load Balancing for Enhanced Energy Efficiency in Zone-Based Wireless Ad Hoc Networks

Mobile Ad hoc Networks (MANETs) consist of independent, self-organized mobile nodes capable of wirelessly communicating with each other in a reliable and secure manner. One of the major challenges in MANETs is energy consumption, as the lifetime of a node's battery is limited. This limitation can affect both network connectivity and the lifetime of individual nodes. Various protocols, proposed by the IETF and other solution providers, have aimed to optimize bandwidth, transmission quality, and power control, but none have significantly improved energy consumption. In response to these challenges, the proposed protocol is the Dynamic Load Balancing in Multipath Energy-Consuming Routing Protocol (DLB-MERP) for Wireless Ad hoc Networks. DLB-MERP can be viewed as an extension of the AOMDV protocol and is classified as a zone-based routing protocol, though it incorporates superior load balancing and energy management techniques. It introduces the concept of virtual zones to manage and distribute energy consumption in an organized way. The protocol selects communication paths based on the availability of node energy and power levels. A leader node is defined within each zone, chosen based on its energy level, load, and channel strength, to optimize energy usage. This method increases network resiliency through multipath routing, thereby enhancing overall network performance and extending network lifetime. DLB-MERP effectively addresses the limitations of existing energy-aware routing methodologies by balancing energy consumption, leading to prolonged network operation.

Mechanistic Understanding of Long Duration Fast Charge Aqueous Zinc Batteries Using Physically Adsorbed Oligomers as Interphases.

Polymers have been used as additives in the liquid electrolytes typically used for secondary batteries that utilize metals as anode. Such additives are conventionally argued to improve long-term anode performance by suppressing morphological and hydrodynamic instabilities thought to be responsible for out-of-plane and dendritic metal deposition during battery charging. More recent studies have reported that the polymer additives provide even more fundamental mechanisms for stabilizing metal electrodeposition through their ability to regulate metal electrodeposit crystallography and, thereby, morphology. Few studies explore how polymers carried in a liquid electrolyte achieve these functions, and fewer still provide rules for choosing among the various polymer types, the additive polymer molecular weight (Mw), and concentration in the electrolyte. Here, we investigate how these generally easy-to-control variables influence electrochemical interphase formation inside battery cells and their impact on the morphology and reversibility of Zn electrodes in aqueous electrolytes. We focus on aqueous Zn-iodine electrochemical cells containing linear polyethylene glycol (PEG) chains as additives and find that in electrolytes where the polymer concentration is maintained in the dilute solution regime there is an optimum polymer molecular weight (Mw ≈ 1000 Da), above which beneficial effects of polymers on Zn electrode reversibility and Zn-I2 battery lifetime are progressively lost. By means of optical ellipsometry and theoretical calculations, we show that the optimal Mw is associated with saturation of the thickness of a physiosorbed PEG coating on the Zn metal electrode. Electron microscopy and X-ray photoelectron spectroscopy analysis of Zn electrodeposits formed in such electrolytes reveal that the physiosorbed polymer coating has two primary effects─it regulates the deposit morphology and suppresses parasitic reactions between the electrode and electrolyte components. The parasitic reactions produce species like ZnO, which are known to passivate the Zn electrode and promote nonuniform deposition. Galvanostatic cycling measurements in aqueous Zn-I2 cells containing the PEG additives at the optimal Mw show that the cells maintain very high Coulombic efficiencies (≥99%) at current densities as high as 50 mA/cm2─close to the maximum values permissible across the Celgard separator membranes used in our studies.

An experimental and comparative study on passive and active PCM cooling of a battery with/out copper mesh and investigation of PCM mixtures

The carbon emission contribution to global warming accelerated both research on and transition to electric vehicles (EVs). Drivers demand high power, fast acceleration and less charging times. All these demands require high C rate charging/discharging demands from batteries. The rate of heat generation is exponentially proportional to C rates which decreases battery lifetime and may lead to thermal runaway. However, a battery thermal management system decreases thermal runaway risk and decelerates battery degradation via controlling battery temperature. In this paper, we first document the thermal conductivity enhancement via copper foam into phase change material (PCM) domain to uncover their possible use in EV thermal management applications. Maximum 15.93 times increment is achieved with a specific copper foam. Then, physical properties and behaviors of distinct PCM mixtures are documented. Homogeneity of mixtures is associated with the chemistry of PCMs and the mixture melting point is proportional to the volume weighted average of melting temperatures. The results document that the PCM with relatively lower melting point is beneficial when end of discharge temperatures considered, except for high discharge rate of 2C. Temperature uniformity across the battery increases with relatively higher melting point PCM. Experiments also document that the amount of PCM volume lost via insertion of copper foam yields higher end of discharge temperatures. Overall, both PCM and copper foam enhances temperature homogeneity and their benefit becomes more sensible during drive cycles relative to continuous charge/discharge use cases.

Inherent Water Competition Effect-Enabled Colloidal Electrode for Ultra-stable Aqueous Zn-I Batteries.

Electrode material stability is crucial for the development of next-generation ultralong-lifetime batteries. However, current solid- and liquid-state electrode materials face challenges such as rigid atomic structure collapse and uncontrolled species migration, respectively, which contradict the theoretical requirements for ultralong operation lifetimes. Herein, we present a design concept for a soft colloid polyvinylpyrrolidone iodine (PVP-I) electrode, leveraging the inherent water molecule competition effect between (SO4)2- from the electrolyte and PVP-I from the cathode in an aqueous Zn||PVP-I battery. Electrochemical demonstrations measured under various simulated and practical (integrated with photovoltaic solar panel) conditions highlight the potential for an ultralong battery lifetime. The PVP-I colloid exhibits a dynamic response to the electric field during battery operation. More importantly, the water competition effect between (SO4)2- from the electrolyte and water-soluble polymer cathode materials establishes a new electrolyte/cathode interfacial design platform for advancing ultralong-lifetime aqueous batteries.

Controllable assembly of polysulfides mediator induced by host-guest chemistry for upgrading energy density and longevity of lithium-sulfur batteries

Rechargeable lithium-sulfur (Li-S) batteries are deemed to one potential candidate for next-generation energy storage systems due to high theoretical energy density and environmental friendliness of sulfur. However, the sluggish redox kinetics and the inevitable shuttle effect of soluble polysulfides impede their commercialization process. Herein, the host–guest chemistry strategy has been proposed to fabricate ultrafine Co9S8/CoO nanoparticles embedded in porous carbon nanosheets (Co9S8/CoO@PC) for improving reversible conversion kinetics and adsorption ability toward polysulfides. Benefiting from bidirectional catalysis of Co9S8/CoO heterostructure and the fully exposed active sites on porous carbon nanosheets, the resultant Co9S8/CoO@PC-S electrode exhibits high specific capacity of 1549 mA h/g at 0.1C as well as outstanding cyclic stability with low average capacity decay of 0.021 % in 1000 cycles at high rate of 3C. Remarkably, the electrode delivers high specific capacities of 1209 and 756 mA h/g at 0.1C after 100 cycles with high sulfur loadings of 4.0 and 7.5 mg cm−2, respectively. This work provides new methodology for the controllable assembly of multiple dimensional composites to realize high energy density and long lifetime of Li-S batteries.

Elucidating Heterogeneous Li Insertion Using Single-Crystalline and Freestanding Layered Oxide Thin Film.

The kinetics of interfacial ion insertion govern the uniformity of electrochemical reactions, playing a crucial role in lithium-ion battery performance. In two-dimensional lithium-conducting layered-oxide battery particles, variation in insertion rates across insertion channels remains unclear due to poorly defined crystal orientation at the solid-liquid interface and solid-state-lithium-diffusion length. This ambiguity complicates understanding inhomogeneous lithium-insertion channels activation. A systematic study requires crystallographically predefined interfaces and in situ lithium-concentration mapping. Here, we fabricated a freestanding, (104)-oriented-LiNi1/3Mn1/3Co1/3O2 single-crystal thin film using dissolution-induced release and performed in situ scanning-transmission-X-ray-microscopy to spatially resolve lithium-insertion at well-defined-interfaces. We observed heterogeneous lithium-concentration evolution due to channel-by-channel insertion rate variation, despite the potential for homogeneous lithium distribution via a solid-solution-phase at equilibrium in NMC111. Increasing current density exacerbates this heterogeneity, highlighting channel-by-channel variation. Our findings provide critical insights into battery electrode utilization and lifetime management, potentially guiding the design of more efficient and durable lithium-ion batteries.

Conceptual model for extending electric vehicle battery lifetime

To maximise the resource efficiency of electric vehicle lithium-ion batteries (LIBs), their lifetimes can be extended through cascading second- and third-life applications. Using expert input, this study establishes a conceptual model for understanding these applications’ state of health (SOH) thresholds and user requirements. Using a qualitative methodology, including focus group discussions with multistakeholder experts and policy analysis of the European battery regulatory landscape, we propose extending LIB use. Our model outlines potential second- and third-life applications aiming to maximise battery value retention. The findings highlight gaps in current European Union regulations that inadequately support battery-repurposing strategies. The conceptual model with an SOH threshold and key performance indicators serves as a foundation for researchers and industries to explore cascading battery applications, foster long-term resource efficiency and contribute to the circular economy by extending LIB lifespans through repurposing initiatives.

Areal capacity balance to maximize the lifetime of layered oxide/hard carbon sodium-ion batteries

Optimizing the areal capacity balance between the negative and positive electrodes is crucial for enhancing the performance of sodium-ion batteries (SIBs). This study investigates NaNi1/3Fe1/3Mn1/3O₂ || hard carbon full cells with varying N/P ratios, employing three-electrode measurements to evaluate cycling performance and identify failure mechanisms in situ. A novel full cell design principle is proposed to determine the threshold N/P ratio for sodium plating. Electrochemical tests demonstrate that an N/P ratio slightly above the threshold value, particularly around 0.9, provides the best cycling performance within 1.5–4.1 V. The cycling degradation mechanism with different N/P ratios was also elucidated. It is due to either the PE or NE exceeding its critical window, leading to active material loss on the positive side and loss of sodium inventory on the negative side. The research further emphasizes that reasonably adjusting the working voltage window can significantly extend the battery's lifetime by suppressing degradation processes. The optimized cell, with an N/P ratio of 0.9 and operating within a 1.0–4.0 V window, demonstrates an 81 % capacity retention over 500 cycles. These findings provide a practical framework for designing high-performance SIBs that balance energy density and longevity.

Regulating Zn2+ Migration-Diffusion Behavior by Spontaneous Cascade Optimization Strategy for Long-Life and Low N/P Ratio Zinc Ion Batteries.

Parasitic side reactions and dendrite growth on zinc anodes are formidable issues causing limited lifetime of aqueous zinc ion batteries (ZIBs). Herein, a spontaneous cascade optimization strategy is first proposed to regulate Zn2+ migration-diffusion behavior. Specifically, PAPE@Zn layer with separation-reconstruction properties is constructed in situ on Zn anode. In this layer, well-soluble poly(ethylene oxide) (PEO) can spontaneously separation to bulk electrolyte and weaken the preferential coordination between H2O and Zn2+ to achieve primary optimization. Meanwhile, poor-soluble polymerized-4-acryloylmorpholine (PACMO) is reconstructed on Zn anode as hydrophobic flower-like arrays with abundant zincophilic sites, further guiding the de-solvation and homogeneous diffusion of Zn2+ to achieve the secondary optimization. Cascade optimization effectively regulates Zn2+ migration-diffusion behavior, dendrite growth and side reactions of Zn anode are negligible, and the stability is significantly improved. Consequently, symmetrical cells exhibit stability over 4000 h (1 mA cm-2). PAPE@Zn//NH4 +-V2O5 full cells with a high current density of 15 A g-1 maintains 72.2 % capacity retention for 12000 cycles. Even better, the full cell demonstrates excellent performance of cumulative capacity of 2.33 Ah cm-2 at ultra-low negative/positive (N/P) ratio of 0.6 and a high mass-loading (~17 mg cm-2). The spontaneous cascade optimization strategy provides novel path to achieve high-performance and practical ZIBs.

Beyond Performance Metrics: The Critical Role of Resource-Based Evaluation in Assessing IoT Attack Detectors

The proliferation of threats within the Internet of Things (IoT) environment is intensifying, largely due to the inherent limitations of this technology. The panoply of anti-threats based on artificial intelligence suffer from the complete embedment of models in limited resources. Tiny Machine Learning (TinyML) is presented as an opportunity in optimizing and selecting machine learning algorithms specifically tailored for intrusion detection systems (IDS) on limited-resource devices. This article addresses the challenges that must be overcome to enable the deployment of machine learning models on devices with constrained resources. In particular, it introduces additional indicators that could influence the algorithmic design of IoT models. Utilizing the PyCaret tool on the TON_IoT dataset, which encompasses nine distinct attacks, we developed and evaluated our approach for selecting the optimal algorithm from fourteen supervised learning models. The proposed tool, beyond the traditional six performance metrics, emphasizes resource consumption metrics, including memory, processor usage, battery life, and execution time – key considerations for TinyML in model refinement and selection. This study has identified less resource-intensive models suitable for developers in the design of IDS for IoT systems. We believe this research offers a foundational framework for the development of lightweight and efficient IoT vulnerability detection solutions.

Sustainable full-cell NCM||Graphite system with superior stability: The hybrid impact of sulfone-containing and flame-retardant additives on interface formation and cyclability

S-containing additives improve the performance, lifetime, and sustainability of lithium-ion batteries (LIBs) through the stabilization of electrode-electrolyte interfaces, modification of electrolyte properties, and facilitating the efficient resource utilization and rapid Li+ migration. Non-etheless, fire safety, extended shelf life, and capacity maintenance are critical concerns for commercial LIBs. Herein, a S-containing film-forming additive, including 4,4-diaminodiphenylsolfon (DADP) by incorporating flame retardancy of dimethyl methyl phosphonate (DMMP) was considered toward taking the barriers of above-mentioned challenges. Based on the calculation, the additives decompose sequentially to produce a distinct SEI via a narrower energy gap under both cathodic and anodic conditions. The NCM532||Graphite with DADP/DMMP showed the enhanced preservation and stability of structure, defined by XRD, Rietveld refinement pattern, SEM, and FT-IR. While the DMMP decreased the fire risk and lowered the capacity due to side reactions, the DADP improved the capacity loss, where the retention rate was 94.91%, 92.01%, 81.29%, and 76.22% within 100, 200, 300, and 400 cycles, respectively. The additive also maintained a capacity of 1123.115 mAhg−1 for 650 cycles, demonstrating excellent cyclability. Therefore, DADP/DMMP facilitate the formation of stable SEI layer, reduce fire risk, improve cycle stability, and offer an achievable path to develop the safe and long-lasting commercial batteries.

A Comparison of Standard SEI Growth Models in the Context of Battery Formation

Growth of the Solid Electrolyte Interphase (SEI) layer on negative electrode particles during the formation cycle is one of the most complex and least understood steps of lithium-ion battery manufacturing. This initial SEI formation significantly impacts battery performance, lifetime, and degradation. Zero-dimensional models, which reduce the complexity of SEI’s morphology, material, and structure, are commonly used to study long-term SEI growth rates and capacity fade. These models are derived based on limiting mechanisms. We aim to compare the most common SEI growth models, focusing on the first few cycles at low C-rates representing formation protocols. Using consistent parameters across models, we seek to understand if they can capture the dynamics of SEI formation. We conducted qualitative comparisons with experimental measurements of Coulombic efficiency in 2032-type coin cells at low C-rate. Our analysis shows that the models predict SEI growth in the first cycle to be higher than in subsequent cycles. However, the difference between cycles in these models is insufficient to explain the experimental results, which indicate a capacity fade during the first cycle that is two orders of magnitude higher than in later cycles. This suggests new models are needed to accurately describe the physics of the formation cycle.

An Adaptive Controller of a Hybrid Storage System for Power Smoothing With Enlarged Battery Lifetime

An Adaptive Controller of a Hybrid Storage System for Power Smoothing With Enlarged Battery Lifetime

Construction of iodine-rich solid electrolyte interphase to achieve highly stable aqueous zinc-ion batteries

Aqueous zinc ion batteries (AZIBs) are considered highly potential secondary energy storage equipment for their higher energy density, lower risk, and affordability. However, parasitic reactions within the anode affect the Zn2+ deposition behavior with uncontrollable dendrite formation significantly shortening the battery lifetime. For improvement of the Zn anode cycling stability, tetraethylammonium iodide (TEAI), a multifunctional additive was introduced into ZnSO4, which constructs an iodine-rich solid electrolyte interphase (SEI) on the zinc anode surface and significantly inhibits the parasitic reactions and nucleation of zinc dendrites. Furthermore, the TEAI also reshapes the solvation sheath of Zn2+, contributing to better transportation kinetics of zinc ions. Attributing to these functions, highly stable AZIBs are realized with the addition of TEAI, in which a stable cycling life of over 3500 h was achieved for symmetric Zn//Zn batteries under 1 mA cm-2. With higher current densities, the battery also exhibited excellent cycling stability over 3500 h. Full cells with the V2O5 cathode were fabricated to examine the commercial availability of TEAI additives, achieving a high reversible discharge capacity of 381 mAh g-1, and the capacity of 102 mAh g-1 was still maintained after 800 cycles at 1 A g-1.

Combined fuzzy-metaheuristic framework for bridge health monitoring using UAV-enabled rechargeable wireless sensor networks

It is essential to monitor the health of important infrastructure (e.g., bridges) to maintain their functions. Visual inspections have been conventionally dominant in this regard, although they are susceptible to human errors. Wireless sensor networks (WSNs) provide an automated, convenient, and low-cost option for developing bridge health monitoring (BHM) networks. However, the constant use of WSNs for monitoring the structural and environmental health of bridges can pose a serious challenge due to the limited lifetimes of these networks that depend on the battery lifetimes of sensor nodes. This paper proposes a combined fuzzy-metaheuristic framework to maintain the BHM stability by using rechargeable sensors. This framework benefits from metaheuristic methods and the fuzzy logic to match the sensor network configuration management to the specific conditions of each bridge, recognizing that different bridges share very few common characteristics. Every new bridge is unique; hence, it is difficult to design a BHM paradigm that fits the conditions of all bridges. The proposed framework manages the current network configuration concerning the conditions of each bridge. This framework manages the network topology formation, information relay, and recharge by using a multipurpose objective, tuning control parameters, and controlling network activities in an optimization process. Moreover, unmanned aerial vehicles (UAVs) are employed to recharge sensor nodes under the proposed framework strategies to overcome the energy limitation of sensor nodes. The proposed framework is evaluated on three bridge scenarios: Hardanger Bridge, Bergsøysund Bridge, and New Carquinez Suspension Bridge. Compared to common WSN methods, it demonstrated superior performance under various conditions, including the rate of active and inactive nodes, energy efficiency, survival rate, stability, recharge delay, average node energy, recharge requests, and total packets received. The evaluation results demonstrate that the proposed framework significantly surpasses existing methods in terms of WSN performance metrics. The results show that the proposed framework outperforms existing methods by an average of 32.8% for the Hardanger Bridge, 53.2% for the Bergsøysund Bridge, and 31.2% for the New Carquinez Suspension Bridge.

Estimation of state of charge for polymer solid-state batteries: Ensemble learning models and temperature impact study

In addition to the research and development of solid electrolytes to improve battery performance, an efficient battery management system (BMS) is a must to ensure safe use and extend battery life, and state of charge (SOC) estimation is critical in the BMS. This paper presents a novel temperature-influenced method for estimating the SOC of polymer-based solid-state batteries in real-time, using machine learning to capture the relationship between SOC and battery characteristics at different temperatures. The results show that accurate SOC estimation results can be achieved at different temperatures, with an average root mean square error of 1.42 %. Furthermore, the method uses Shapley Additive explanation (SHAP) to reduce the degree of black box nature of the model and analyzes the electrochemical processes inside the battery at different temperatures through relaxation time distribution. Accurate estimates of SOC and guidelines for appropriate operating temperatures can serve as a basis for BMS decision-making and improve the lifetime of polymer-based solid-state batteries.