Evaluation of electrochemical performance and redox activity of Fe in Ti doped layered P2-Na0.67Mn0.5Fe0.5O2 cathode for sodium ion batteries

Darbar, Devendrasinh; Muralidharan, Nitin; Hermann, Raphaël; Nanda, Jagjit; Bhattacharya, Indranil

doi:10.1016/j.electacta.2021.138156

Cited by 24 publications

(24 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Crystallographic evaluations of the sol-gel synthesized P2-type Na 0.67 Fe 0.5 Mn 0.5 O 2 were performed using the XRD patterns, shown in Figure 3a. The patterns showed that the NFM powder samples had a hexagonal, layered structure with a P63/mmc space group, as reported in our previous paper [32,33]. The morphology of the powder samples was observed using an ultra-high-resolution Field Emission Scanning Electron Microscope (FE-SEM) Hitachi SU7000.…”

Section: Materials Characterizationsupporting

confidence: 73%

“…Figure 7 shows the graph of Voltage vs. SOC for the test data run at a 0.1 C rate for a different cycle (5th, 10th, 20 th , and 50th), and the model performed much better in estimating the SOC value, having the R 2 value of ~0.99-0.97. The battery degraded when it was cycled for a longer period because of crystal structure instability, an increase in the impedance by forming a passivation layer on the electrode, the decomposition of the electrolyte, and many more reasons, as explained in our previous paper [32]. However, the model overcame the degradation nature of the battery and estimated the SOC value for a higher cycle (50th cycle) with good accuracy, of ~0.99, making the model more robust.…”

Section: Resultsmentioning

confidence: 88%

See 1 more Smart Citation

Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

Darbar

Bhattacharya

2022

Electrochem

Self Cite

View full text Add to dashboard Cite

Estimating the accurate State of Charge (SOC) of a battery is important to avoid the over/undercharging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equations in the Extended Kalman Filter (EKF) and the equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate the SOC value accurately where battery parameters such as current, voltage, and charge are mapped directly to the SOC value at the output. A FNN could self-learn the weights with each training data point and update the model parameters such as weights and bias using a combination of two gradient descents (Adam). This model comprises the Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and biases. Our FNN model was trained with data comprising different current rates and tested for different cycling data, for example, 5th, 10th, 20th, and 50th cycles and at a different cutoff voltage (4.5 V). The battery used for estimating the SOC value was a Na-ion based battery, which is highly non-linear, and it was fabricated in a house using Na0.67Fe0.5Mn0.5O2 (NFM) as a cathode and Na metal as a reference electrode. The FNN successfully estimated the SOC value for the highly non-linear nature of the Na-ion battery at different current rates (0.05 C, 0.1 C, 0.5 C, 1 C, 2 C), for different cycling data, and at higher cut-off voltage of –4.5 V Na+, reaching the R2 value of ~0.97–~0.99, ~0.99, and ~0.98, respectively.

show abstract

Section: Materials Characterizationsupporting

confidence: 73%

Section: Resultsmentioning

confidence: 88%

Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

Darbar

Bhattacharya

2022

Electrochem

Self Cite

View full text Add to dashboard Cite

show abstract

“…The increased thickness of TMO 2 slab may be attributed to the low bond dissociation energy of Zn-O (∆Hf 298K = 284.1 kJ/mol) compared to Fe-O (∆Hf 298K = 409 kJ/mol) and Mn-O (∆Hf 298K = 402 kJ/mol) [35]. Based on this work, we-doped Ti 4+ in our future work, where Ti-O has higher bond dissociation energies (∆Hf 298K = 662 kJ/mol) [23].…”

Section: Resultsmentioning

confidence: 95%

“…Electrochem 2021, 2, FOR PEER REVIEW 12 kJ/mol) [35]. Based on this work, we-doped Ti 4+ in our future work, where Ti-O has higher bond dissociation energies (ΔHf 298K = 662 kJ/mol) [23].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Understanding the Effect of Zn Doping on Stability of Cobalt-Free P2-Na0.60Fe0.5Mn0.5O2 Cathode for Sodium Ion Batteries

2021

Self Cite

View full text Add to dashboard Cite

In this work, we report a sol-gel synthesis-based Zn-doped Na0.6Fe0.5Mn0.5O2 (NFM) cathode and understand the effect of Zn doping on the crystal structure and electrochemical performances such as discharge capacity and rate capability. Detailed X-Ray diffraction (XRD) pattern analysis indicated a decrease in the Na-layer thickness with Zn doping. Small amount of Zn2+ dopant (i.e., 2 at.%) slightly improved cycling stability, reversibility, and rate performances at higher discharge current rates. For example, at 1 C-rate (1 C = 260 mAh/g), the Zn2+-doped cathode retained a stable reversible capacity of 72 mAh/g, which was ~16% greater than that of NFM (62 mAh/g) and showed a minor improvement in the capacity retention of 60% compared to 55% for the pristine NFM after 65 cycles. Slight improvement in the electrochemical performance for the Zn-doped cathode can be attributed to a better structural stability, which prevented the initial phase transition and showed the presence of electrochemical active Fe3+/4+ even after 10 cycles compared to NFM.

show abstract

Prospective Sustainability Screening of Sodium‐Ion Battery Cathode Materials

Baumann¹,

Häringer

Schmidt

et al. 2022

Advanced Energy Materials

View full text Add to dashboard Cite

Sodium‐ion batteries (SIB) are considered as a promising alternative to overcome existing sustainability challenges related to Lithium‐ion batteries (LIB), such as the use of critical and expensive materials with high environmental impacts. In contrast to established LIBs, SIBs are an emerging technology in an early stage of development where a challenge is to identify the most promising and sustainable cathode active materials (CAM) for further research and potential commercialization. Thus, a comprehensive and flexible CAM screening method is developed, providing a fast and comprehensive overview of potential sustainability hotspots for supporting cathode material selection. 42 different SIB cathodes are screened and benchmarked against eight state‐of‐the‐art LIB‐cathodes. Potential impacts are quantified for the following categories: i) Cost as ten‐year average; ii) Criticality, based on existing raw material criticality indicators, and iii) the life cycle carbon footprint. The results reveal that energy density is one of the most important factors in all three categories, determining the overall material demand. Most SIB CAM shows a very promising performance, obtaining better results than the LIB benchmark. Especially the Prussian Blue derivatives and the manganese‐based layered oxides seem to be interesting candidates under the given prospective screening framework.

show abstract

Evaluation of electrochemical performance and redox activity of Fe in Ti doped layered P2-Na0.67Mn0.5Fe0.5O2 cathode for sodium ion batteries

Cited by 24 publications

References 32 publications

Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

Understanding the Effect of Zn Doping on Stability of Cobalt-Free P2-Na0.60Fe0.5Mn0.5O2 Cathode for Sodium Ion Batteries

Prospective Sustainability Screening of Sodium‐Ion Battery Cathode Materials

Contact Info

Product

Resources

About