Reliability Of Storage Systems Research Articles

Optimal Cell Removal to Enhance Operation of Aged Grid-Tied Battery Storage Systems

The proper operation of aged batteries is essential to improve the reliability and performance of grid-connected battery energy storage systems (BESS). In this letter, we investigate on the phenomenon that removing certain battery cells within an aged battery pack, may lead to increasing the effective capacity of the BESS, because those cells with reduced capacity become the limiting factor for the overall BESS operation, while the remaining capacity of some other cells is not used. We optimize the process of identifying which battery cells from a grid-tied BESS should be removed in order to improve the overall BESS performance for smart grid applications. The experimental hardware-in-the-loop (HIL) testing of a grid-integrated BESS with aged cells is used to validate the developed models and show the increase in the effective capacity once the identified cells are removed. We also perform case studies to assess the impact of cell removal on the operation of an aged BESS in a peak demand shaving smart grid application.

A Framework for Underground Gas Storage System Reliability Assessment Considering Functional Failure of Repairable Components

Abstract As one of the most important means of nature gas peak shaving and energy strategic reserving, the reliability assessment of underground gas storage (UGS) system is necessary. Although many methods have been proposed for system reliability assessment, the functional heterogeneity of components and the influence of hydrothermal parameters on system reliability are neglected. To overcome these problems, we propose and apply a framework to assess UGS system reliability. Combining two-layer Monte Carlo simulation (MCS) technique with hydrothermal calculation, the framework integrates dynamic functional reliability of components into system reliability evaluation. To reflect the state transition process of repairable components and their impact on system reliability, the Markov model is introduced at system level. In order to improve the calculation speed, artificial neural network (ANN) model based on off-line MCS is established to replace the on-line MCS at components level. The proposed framework is applied to the reliability assessment and operation optimization of an UGS under different operation conditions. Compared with the traditional single-layer MCS method, the proposed method can not only reflect the variation of UGS reliability with hydrothermal parameters and operation time, but also can improve evaluation efficiency significantly.

A Management Scheme of Multi-Level Retention-Time Queues for Improving the Endurance of Flash-Memory Storage Devices

As flash memory technology has been scaled down to 1x nm and more bits can be stored in a cell, the storage density of flash memory has been significantly improved. However, these technical trends also severely hurt the programming speed and endurance of flash memory. The internal data retention time is the duration for which a flash cell can correctly hold data. By relaxing internal data retention time, both the page programming speed and the block endurance could be improved. However, the retention time of flash memory typically requires to last for several years according to the industrial standard. Thus a refreshment scheme is required to deal with the decreasing of retention time. In this article, we propose multi-level retention-time queues with a management scheme to meet the retention-time requirement for a reliable storage system. Observing that many data are overwritten in hours or days in real workloads, multiple retention-time queues could effectively separate data with different update frequencies. There are three challenge issues for a proper design: (1) Since access pattern might change from time to time, a technical issue is how to promote/demote data so that data could be maintained in the proper retention-time queue to minimize the refreshment overhead. (2) Another technical issue is how to refresh each retention-time queue in time to guarantee data integrity. (3) Since blocks resided in different retention-time queue would suffer from different level of wearing, the third technical issue is how to estimate wearing status of flash-memory blocks in an effective and efficient manner to achieve wear leveling. In our scheme, data allocator, multi-level refresh module, garbage collector, and wear leveler are introduced to deal with these technical issues. Based on our experimental results, not only endurance and performance but also energy consumption of the flash-memory storage system could be significantly improved by our scheme.

The Reliability of Modern File Systems in the face of SSD Errors

As solid state drives (SSDs) are increasingly replacing hard disk drives, the reliability of storage systems depends on the failure modes of SSDs and the ability of the file system layered on top to handle these failure modes. While the classical paper on IRON File Systems provides a thorough study of the failure policies of three file systems common at the time, we argue that 13 years later it is time to revisit file system reliability with SSDs and their reliability characteristics in mind, based on modern file systems that incorporate journaling, copy-on-write, and log-structured approaches and are optimized for flash. This article presents a detailed study, spanning ext4, Btrfs, and F2FS, and covering a number of different SSD error modes. We develop our own fault injection framework and explore over 1,000 error cases. Our results indicate that 16% of these cases result in a file system that cannot be mounted or even repaired by its system checker. We also identify the key file system metadata structures that can cause such failures, and, finally, we recommend some design guidelines for file systems that are deployed on top of SSDs.

Sn substitution endows NaTi2(PO4)3/C with remarkable sodium storage performances

The further development of clean energy requires the use of more stable and reliable energy storage system. In addition to lithium ion battery power supplies, sodium ion batteries also have prospects for application and development thanks to the low cost and abundant resource. NaTi2(PO4)3 has attracted much attention due to its three-dimensional channels for sodium ion transfer. In order to meliorate sodium storage properties of NaTi2(PO4)3 electrode, a facile strategy of Sn substitution at Ti sites was employed, and a series of electrodes were successfully synthesized through sol-gel route. The electrochemical performances of Sn substituted composites are significantly improved compared with bare NaTi2(PO4)3/C. And it was found that NaSn0.2Ti1.8(PO4)3 (NTP/C-Sn-2) delivers the largest capacity, and it also demonstrates the outstanding cycling performances. NTP/C-Sn-2 has discharge capacity of 131.1 mAh g−1 at 4 A g−1 in rate test and 121.4 mAh g−1 at 1 A g−1 after 1000 cycles in cycling test. The experimental results show that NaTi2(PO4)3/C with Sn substitution with proper content exhibits the great potential in anode for sodium ion batteries, and can further provide reference for next generation electrode materials and battery systems.

Standalone Photovoltaic Direct Pumping in Urban Water Pressurized Networks with Energy Storage in Tanks or Batteries

Photovoltaic energy production is nowadays one of the hottest topics in the water industry as this green energy source is becoming more and more workable in countries like Spain, with high values of irradiance. In water pressurized systems supplying urban areas, they distribute energy consumption in pumps throughout the day, and it is not possible to supply electromechanical devices without energy storages such as batteries. Additionally, it is not possible to manage energy demand for water consumption. Researchers and practitioners have proven batteries to be reliable energy storage systems, and are undertaking many efforts to increase their performance, capacity, and useful life. Water pressurized networks incorporate tanks as devices used for accumulating water during low consumption hours while releasing it in peak hours. The compensation tanks work here as a mass and energy source in water pressurized networks supplied with photovoltaic arrays (not electricity grids). This work intends to compare which of these two energy storage systems are better and how to choose between them considering that these two systems involve running the network as a standalone pumping system without being connected to electricity grids. This work also calculates the intermediate results, considering both photovoltaic arrays and electricity grids for supplying electricity to pumping systems. We then analyzed these three cases in a synthetic network (used in earlier research) considering the effect of irradiation and water consumption, as we did not state which should be the most unfavorable month given that higher irradiance coincides with higher water consumption (i.e., during summer). Results show that there is no universal solution as energy consumption depends on the network features and that energy production depends very much on latitude. We based the portfolio of alternatives on investments for purchasing different equipment at present (batteries, pipelines, etc.) based on economic criteria so that the payback period is the indicator used for finding the best alternative, which is the one with the lowest value.

Towards Building Reliable and Cost-Efficient Distributed Storage Systems

Reliability and cost are two important targets for distributed storage systems. For many years, numerous schemes have been proposed to improve the reliability or cost of distributed storage systems, and they can be divided into three categories: (1) data redundancy schemes; (2) data placement schemes; and (3) data repair schemes. However, it is still unclear regarding how to build a reliable and cost-efficient distributed storage system, because (i) insufficient considerations on the combinations of different schemes; and (ii) insufficient considerations on failures and recoveries of different subsystems (racks, nodes, disks, and sectors). To measure the reliability and cost caused by different schemes, we design and implement CR-SIM, a Comprehensive Reliability SIMulator for distributed storage systems. It considers various affecting factors, such as the system topology, the data redundancy scheme, the data placement scheme, the data repair scheme, and the failure/recovery models of different subsystems. By using CR-SIM, we conduct various simulation-based experiments, and the experimental results reveal several important findings, which are helpful to build reliable and cost-efficient distributed storage systems. For public use, we have open-sourced our source code at https://github.com/yichuan0707/CR-SIM.

Exploiting wind-solar resource complementarity to reduce energy storage need

Resource complementarity carries significant benefit to the power grid due to its smoothing effect on variable renewable resource output. In this paper, we analyse literature data to understand the role of wind-solar complementarity in future energy systems by evaluating its impact on variable renewable energy penetration, corresponding curtailment, energy storage requirement and system reliability. Results show that wind-solar complementarity significantly increases grid penetration compared to stand-alone wind/solar systems without the need of energy storage. However, as capacity increases, the capability of complementarity to increase grid penetration approaches its limit due to the reduced matching of output to the load profile and pursuant increase in excess generation. Thus, achieving very high penetration requires appropriately designed energy storage and curtailment. Yet, even at higher grid penetration, complementarity carries significant multidimensional benefits to the power system. The most important observation was the achievement of very high grid penetration at reduced energy storage and balancing requirements compared to stand-alone systems. Researchers reported that using the same energy storage capacity, wind-solar complementarity led to significantly higher penetration of up to 20% of annual demand compared to stand-alone systems. In addition, by coupling to curtailment as an enabler, and related dispatch flexibility that comes with storage application, lower balancing capacity need was reported at higher penetration. Wind-solar complementarity was also found to reduce ramping need while contributing to improved system adequacy. Complementarity from other dispatchable renewable resources further reduces storage need and curtailment and improve system reliability, whereas power grid integration and relative cost changes allow for further optimisation while transitioning to 100% renewable energy.

ColdStore: A Storage System for Archival Data

With the astounding growth of the information era, the amount of data increases at high speed. However, a significant fraction of the data residing in storage is rarely accessed, which is referred to as cold data. It has become a looming problem of how to optimally manage and maintain this data at a low cost. In this paper, we present a large-scale, reliable, energy- and cost-efficient storage system called ColdStore. Different from traditional file systems, ColdStore aims to store cold data efficiently in the aspects of energy and cost. It is designed as a high-available distributed system, which is a cluster composed of a number of nodes with different roles: Metadata Nodes maintain the metadata of all files; Transfer Nodes play a role in encoding, decoding and caching; and Storage Nodes store encoded data for durability. Dedicated hardware is designed for Storage Nodes to reduce the power consumption. Each Storage Node is equipped with a low-power CPU and has a power supply control unit of hard disk drives. Due to the architecture we proposed, the vast majority, as many as 93.75%, of the hard disk drives can be powered off under the archival workload, while the system still provides reasonable performance and high reliability.

Delay Reduction Through Optimal Controller Placement to Boost Scalability in an SDDC

Software-defined storage (SDS) is an abrupt technology that aims to address the explosive data proliferation and storage management complexity through the separation of control and data path, which are tightly bounded in the traditional storage area network (SAN) model. In SDS, the centralized controller manages and controls the overall storage services and operations. However, the centralized controller approach raises the scalability and single point of failure flag. In such a paradigm, the controller placement is an important activity. A random placement of controllers suffers from unpredictable delay in a large enterprise. Multiple SDS controllers mitigate the single point of failure and provide redundancy. We propose a method called controller placement based on the center of gravity (CPCG) to solve the optimal placement controllers’ locations to reduce the delay. CPCG incorporates different coordinates and analyzes their density (the number of attached devices to a switch) to suggest new optimal sites. For the least number of SDS controllers, we propose an areawise optimal placement location strategy; the areawise approach mitigates workload processing from the central controller and also provides a scalable and reliable storage system. Experiments show that the optimal placement drastically reduces delay and, hence, boosts scalability.

Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm

In photovoltaic and wind power storage systems, the reliability of the battery directly affects the overall reliability of the energy storage system. Failed batteries can seriously affect the stable operation of energy storage systems. This paper aims to improve the reliability of the storage systems by accurately predicting battery life and identifying failing batteries in time. The current prediction models mainly use artificial neural networks, Gaussian process regression and hybrid models. Although these models can achieve high prediction accuracy, the computational cost is high due to model complexity. Least squares support vector machine (LSSVM) is a computationally efficient alternative. Hence, this study combines the improved chicken swarm optimization algorithm (ICSO) and LSSVM into a hybrid ICSO-LSSVM model for the reliability of photovoltaic and wind power storage systems. The following are the contributions of this work. First, the optimal penalty parameter and kernel width are determined. Second, the chicken swarm optimization algorithm (CSO) is improved by introducing chaotic search behavior in the hen and an adaptive learning factor in the chicks. The performance of the ICSO algorithm is shown to be better than CSO using standard test problems. Third, the prediction accuracy of the three models is compared. For NMC1 battery, the predicted relative error of ICSO-LSSVM is 0.94%; for NMC2 battery, the relative error of ICSO-LSSVM is 1%. These findings show that the proposed model is suitable for predicting the failure of batteries in energy storage systems, which can improve preventive and predictive maintenance of such systems.

PFP: Improving the Reliability of Deduplication-based Storage Systems with Per-File Parity

Data deduplication weakens the reliability of storage systems since by design it removes duplicate data chunks common to different files and forces these files to share a single physical date chunk, or critical chunk, after deduplication. Thus, the loss of a single such critical data chunk can potentially render all referencing (sharing) files unavailable. However, the reliability issue in deduplication-based storage systems has not received adequate attention. Existing approaches introduce data redundancy after files have been deduplicated, either by replication on critical data chunks, i.e., chunks with high reference count, or RAID schemes on unique data chunks, which means that these schemes are based on individual unique data chunks rather than individual files. This can leave individual files vulnerable to losses, particularly in the presence of transient and unrecoverable data chunk errors such as latent sector errors. To address this file reliability issue, this paper proposes a Per-File Parity (short for PFP) scheme to improve the reliability of deduplication-based storage systems. PFP computes the XOR parity within parity groups of data chunks of each file after the chunking process but before the data chunks are deduplicated. Therefore, PFP can provide parity redundancy protection for all files by intra-file recovery and a higher-level protection for data chunks with high reference counts by inter-file recovery. Our reliability analysis and extensive data-driven, failure-injection based experiments conducted on a prototype implementation of PFP show that PFP significantly outperforms the existing redundancy solutions, DTR and RCR, in system reliability, tolerating multiple data chunk failures and guaranteeing file availability upon multiple data chunk failures. Moreover, a performance evaluation shows that PFP only incurs an average of 5.7 percent performance degradation to the deduplication-based storage system.

Lasagna-Inspired Zn Anode Design for High-Energy Rechargeable Aqueous Batteries

A key aspect of any future battery technology development is safety. Although lithium-based batteries are ubiquitous, there are still challenges related to their energy density, cycle life, cost and safety. In regard to safety, compared with organic electrolyte, aqueous rechargeable batteries may provide a safer alternative for reliable, low-cost and large-scale energy storage systems. As seen from the penetration test in Fig. 1a-1b, the battery with organic electrolyte catches fire, yet the battery with aqueous electrolyte is relatively safe. Moreover, aqueous batteries have high ion conductivity and cost effectiveness. Generally, the cell voltage and energy density of aqueous batteries are lower than those of organic-based batteries (e.g. Li-ion) because of the relatively smaller electrochemical stability window of water. Among all the metals that are stable in water, zinc is the most active and has the lowest possible operating potential. This means using Zn anode can increase overall cell voltage of aqueous batteries. Moreover, zinc is globally available, inexpensive (3.19 USD kg-1), and has high capacity (820 Ah kg-1 and 5854 Ah L-1). Zinc-based aqueous batteries also possess the stability to be operated in ambient air. Accordingly, Zn aqueous rechargeable batteries are promising to become a safer energy storage system. Among zinc-based aqueous batteries, Zn-air batteries have high theoretical volumetric energy density, which is around three times that of conventional Li-ion batteries (LIB). Zn anodes have been investigated in neutral/mild acidic aqueous electrolytes. Yet in order to pair them with oxygen cathode to reach the highest energy density, alkaline aqueous electrolyte is ideal, in which the oxygen electrode has low polarization. In alkaline aqueous electrolyte, Zn anode undergoes a Zn (s) ↔ Zn(OH)4 2- (aq) ↔ ZnO (s) conversion. This solid-solute-solid transformation and insulating discharge product ZnO lead to three vital challenges: 1) ZnO passivates Zn surface and prevents further discharging, leading to low Zn utilization; 2) ZnO is insulating and can hardly be charged back to Zn; 3) diffusion of Zn(OH)4 2- causes the loss of active material and change of electrode morphology. Thus, anode modification and protection are needed to alleviate the passivation and dissolution. We firstly designed a Zn mesh@GO anode (Fig 1c). Graphene oxide (GO) layers on the Zn mesh surface deliver electrons across insulating ZnO and can slow down the Zn dissolution. However, the utilization of zinc is still low because passivation problem is not completely solved. Through SEM investigation, critical passivation size was found to be ~ 2 µm. Thus, we further designed a lasagna-inspired ZnO@GO anode (Fig 1d). ZnO nanoparticles are encapsulated by GO. ZnO lasagna structure has three features: 1) the size of ZnO nanoparticles is smaller than the critical size of passivation; 2) the fabrication of ZnO lasagna anode starts with commercially available ZnO nanoparticles (~100 nm), and is compatible with the roll-to-roll process, which is ideal for large-scale manufacturing; 3) GO allows permeation of OH- and water, and prevents loss of Zn active material through blocking bigger Zn(OH)4 2-. As a result, such lasagna anode achieves a high volumetric capacity of 2308 Ah/L and a remarkable capacity retention of 86% after 150 cycles. In contrast, the open-structured ZnO nanoparticle anode, without the protection of GO, completely died after 90 cycles. Figure 1

Diagnostic and Prognosis of Li-Ion Battery State-of-Health Based on Electrodes Potentials Shifts

The degradation mechanisms affecting each electrode of a Li-ion cell are physical and chemical processes widely depicted in the literature [1-4] They lead to the battery loss of performance in terms of energy availability (capacity decay) and power capability (resistance increase). To diagnose the electrode degradation and cell available capacity, many models have been designed [5-8]. These models provide battery state-of-heath (SOH) indicators by simulating various scenarios of degradation modes. In this work, we want to go further and present a new solution not only able to estimate the battery SOH, but also to predict the evolution of the battery available capacity over the next couple of months. This diagnosis/prognosis solution is a key-enabling technology to ensure the storage system reliability of future electric cars. In our approach, each electrode open circuit potential (OCP) is used to rebuild the cell voltage at low rate. Only three parameters (fig.1) are used to define the cell voltage curve: the positive electrode capacity (Cpos), the negative electrode capacity (Cneg), and the offset (OFS) which represents the shift between both electrode OCPs. The cell capacity is directly related to these three parameters and depends on the voltage range applied during cycling. To diagnose the cell degradations, a two-step method was proposed (fig.2). First, we fitted the experimental voltage of the cell measured at low rate of discharge (C/10) with the open circuit potential (OCP) of the negative and positive electrodes to identify the values of each of the three parameters (Cneg, Cpos, OFS). Then, we finely tune the identified parameter values from differential voltage curves by ensuring proper peak alignment. The capacity is therefore derived capacity from the final values of the three parameters. This method has been applied on a graphite/LMO-NMC cell for various calendar and cycling ageing conditions, with temperatures ranging from 0 to 60 °C, under low and high discharge rates (C/3, 1C), and different state-of-charge (0 to 100 %). As expected, calendar and cycling conditions have different impacts on the cell ageing and therefore on the electrodes ageing. Figure 3 shows a good agreement between experimental and simulated capacities when identifying the three parameters while Figure 4 shows the evolution of the three parameters (Cneg, Cpos, OFS) during calendar and cycling ageing. These three parameters evolution are path dependent of the ageing test conditions. For the cell SOH prognosis, we propose a novel empirical ageing model predicting the electrode parameter evolution over the time. It is worth noticing that the Palmgren-Miner rule [9] allows us to cumulate calendar and cycling ageing effects for the total loss of three electrodes parameters in our aging model.

Thermoelectric energy storage with geothermal heat integration – Exergy and exergo-economic analysis

The proposed Thermo-Electric Energy Storage (TEES) system addresses the need for peak-load support (1–2 daily hours of operation) for small-distributed users who are often owners of small/medium size PV systems (4 to 50 kWe) and wish to introduce a reliable storage system able to compensate the productivity/load mismatch. The proposed thermoelectric system relies on sensible heat storage: a warm resource at 120/160 °C (a hot water reservoir system), and a cold resource at −10 /−20 °C (a cold reservoir system containing water and ethylene glycol). The power cycle operates through a trans-critical CO2 scheme including recuperation; in the storage mode, a supercritical heat pump restores heat to the hot reservoir, while a cooling cycle (using a suitable refrigerant) cools the cold reservoir. The power cycle and the heat pump benefit from geothermal heat integration at low-medium temperatures (80–120 °C), thereby allowing to achieve a marginal round-trip efficiency (electric-to-electric) in the range from 50 to 75% (not considering geothermal heat integration).The three systems are analyzed with different resource conditions and parameters setting (hot storage temperature, pressure levels for all cycles, ambient temperature…); exergy and exergo-economic analyses are performed to evaluate the economic competitiveness and in order to identify the critical items in the system. A sensitivity analysis on the main parameters affecting the produced power cost of the system per unit electric energy is carried out.

An Attention-augmented Deep Architecture for Hard Drive Status Monitoring in Large-scale Storage Systems

Data centers equipped with large-scale storage systems are critical infrastructures in the era of big data. The enormous amount of hard drives in storage systems magnify the failure probability, which may cause tremendous loss for both data service users and providers. Despite a set of reactive fault-tolerant measures such as RAID, it is still a tough issue to enhance the reliability of large-scale storage systems. Proactive prediction is an effective method to avoid possible hard-drive failures in advance. A series of models based on the SMART statistics have been proposed to predict impending hard-drive failures. Nonetheless, there remain some serious yet unsolved challenges like the lack of explainability of prediction results. To address these issues, we carefully analyze a dataset collected from a real-world large-scale storage system and then design an attention-augmented deep architecture for hard-drive health status assessment and failure prediction. The deep architecture, composed of a feature integration layer, a temporal dependency extraction layer, an attention layer, and a classification layer, cannot only monitor the status of hard drives but also assist in failure cause diagnoses. The experiments based on real-world datasets show that the proposed deep architecture is able to assess the hard-drive status and predict the impending failures accurately. In addition, the experimental results demonstrate that the attention-augmented deep architecture can reveal the degradation progression of hard drives automatically and assist administrators in tracing the cause of hard drive failures.

Can Erasure Codes Damage Reliability in SSD-Based Storage Systems?

Erasure codes are applied in storage systems including both Hard Disk Drive (HDD) and Solid State Disk (SSD) to protect arrays of disks against failures. Applying these codes in SSD-based systems incurs additional number of Program/Erase (P/E) cycles on each disk, which may accelerate the wear-out of disks. This means that while erasure codes improve reliability of SSD-based systems, they impose a side-effect that may degrade reliability as the number of P/E cycles increases. This paper investigates the benefit and side-effect of erasure codes on reliability of SSD-based systems. The investigation attempts to find out the parameters which improve/damage reliability. This study has been evaluated using the DiskSim and SOYA simulators, and exploiting different configurations for a storage system running nine real workloads. In the simulation, the effect of five parameters on the improvement/damage of reliability is studied; these parameters are mapping P/E cycles with failure rate, disk failure rate, disk repair time, number of disks, and coding pattern of erasure codes. Simulation results show that in almost all cases, the effect of erasure codes on reliability improvement is on average 16 percent greater than their effect on reliability degradation.

A Distributed Storage System Using a Mobile Ad hoc Network

The paper proposes a distributed storage service utilizing ad hoc networking between mobile nodes in a localized geographical area. The service is based on a distributed architecture and allows a group of users to utilize their mobile devices for implementing a transient storage system. Using mobile devices alone as storage media without any wired infrastructure support presents numerous challenges. These challenges include stored data’s inaccessibility due to nodes mobility or data loss due to wireless communication failure or even the unexpected “death” of a node. The proposed solution successfully offsets these problems by using an erasure coding based scheme for achieving a reliable storage system with a low space overhead. The proposed solution has been analyzed for its performance through simulation experiments. The results prove the efficacy of the solution in achieving high data recoverability with low communication overhead.

Sodium Vanadium Fluorophosphates Morphology Effects on Rechargeable Sodium Ion Batteries

With growing demands of using sustainable energy, a reliable energy storage system is truly essential. Sodium ion battery is one of the candidates which features in low cost and reasonable battery performances for future energy storage. In our previous works, promising cathode material, carbon-coated sodium vanadium fluorophosphates (Na3V2(PO4)2F3/C, NVPF/C) nanocomposites with sodium superionic conductor (NASICON) structures, have been investigated and displayed high capacities at high operation voltage. Some thorny issues arising, however, during the small-scale production. First, the discharging capacities decrease as the loading of active materials increase. Second, the original synthesis method is time consuming and low yield. Considering the economic and technical feasibility, we introduce some measures to improve both the manufacture process and the battery performance. The adoption of spray-drying not only effectively reduces the production time of NVPF/C but transforms the shape of NVPF/C from blocks to spheres. In addition, different recipes of additives results in different morphology of NVPF/C particles. Here we present three types of NVPF/C, including blocks, hollow spheres and solid spheres, which are confirmed by SEM. X-ray diffraction indicated that the purity of NVPF crystalline increased by the new process. Coin cells assembled with Na foils reversible discharge capacities of 105 mAh g−1 at 1C, accompanied with high active material loading over 10 mg/cm2.

Benign Recycling of Spent Batteries towards All-Solid-State Lithium Batteries.

Lithium-ion batteries (LIBs) are one of the most significant energy storage devices applied in power supply facilities. However, a huge number of spent LIBs would bring harmful resource waste and environmental hazards. In this study, a benign hydrometallurgical method using phytic acid as precipitant is proposed to recover useful metallic Mn ions from spent LiMn2 O4 batteries. Besides Mn-based cathodes, this recovery process is also applicable for other commercial batteries. More importantly, for the first time, the as-obtained manganous complex is employed as a nanofiller in a polyethylene oxide matrix to largely improve Li+ conductivity and transference number. As a result, when applied in all-solid-state lithium batteries, high capacity and outstanding cyclic stability are achieved with capacity retention of 86.4 % after 60 cycles at 0.1 C. The recovery of spent lithium batteries not only has benefits for the environment and resources, but also shows great potential application in all-solid-state lithium batteries, which opens up a costless and efficient circulation pathway for clean and reliable energy storage systems.