Lithium -Ion battery li fetimes from cyclic and calend ar aging tests of more than 1000 cells were compared employing novel plots termed ENPOLITE (energy -power-lifetime -temperature) . Battery cell data from in-house measurements and published data were combined into a uniform database ; the total dataset size exceeds 1000 GB . At a glance, ENPOLITE p lots inform about the nominal capacity, cell format, cell chemistry , average aging test duration, measurement temperature, specific power employed for testing , energy density, and the achieved lifetime for every cell. A battery lifetime coefficient was derived , allowing the comparison of lithium -ion batteries with different weights or volumes , capacities , and cell chemistries. The combination of multiple parameters in ENPOLITE facilitated a thorough comparison of various batteries' respective lifetim es. In addition to the cel l-specific parameters during cycling , the specific stored energy and the storage temperature were depicted in a calendar ENPOLITE -Plot. "Table of Contents" TOC Graphic : Energy Density Power Density tCapacity Lifetime TemperatureWh/kgW/kg ENPOLITE Plot Figure 1: Limitation of existing graphical comparisons of batteries. (a) Ragone plot o f different lithium -ion battery chemistries. The plot contains no information about lifetime or different usage scenarios. Cells with a high power density can also be used at a lower power level than depicted. (b), (c) Lifetime comparison of two cells, one from dataset NMC01 and one from dataset LFP02. (b) shows relative c apacity vs. equivalent full cycles , while (c) shows r emaining specific energy vs. specific energy throughput. Both comparisons are common, but by including weight and energy in the comparison , different conclusions are drawn about the relative lifetime of the cells . b) c) Due to their impressive energy density, power density, lifetime , and cost , lithium -ion batteries have become the most important electrochemical storage system , with applications including consumer electronics, electric vehicles, and stationary energy storage1. However, each application ha s unique , often conflicting product specifications , requiring a balanced overall assessment . The Ragone plot2, shown in Figure 1 a for lithium -ion battery chemistries, is a commonly -used plot to compare two of these specifications , energy and power; however, important parameters including cost, lifetime, and temperature sensitivity are not considered . A standardized and balanced repo rting and visualization of specifications would greatly help an informed cell selection process . Comparisons of energy and power density can be made relatively easily via standard test protocols and within a short timeframe . However, comparing cells across other dimensions presents some practical challenges, particularly in an academic setting. First, e ven though the price is an essential criterion in many applications, it is also the most controversial since individual cell prices are not openly shared and depend on nontechnical factors such as production volume and operating margin. Second, lifetime comparisons of lithium -ion batteries are widely discussed in the literature3–8, but these comparisons are especially challenging due to the high sensitivity of lithium -ion battery lifetime to usage conditions (e.g. , fast charge, temperature control, cell interconnection , etc .). Additionally, the metrics for lifetime are not standardize d, and conclusions about lifetime performance are generally dependent on the choice of metrics used (e.g., relative vs. absolute capacity or energy; see Figures 1b – 1c). Furthermore, battery degradation is often nonlinear8–10; therefor e, using a single parameter from a linear fit (e.g., the slope) to represent nonlinear aging trends must be handled with care . Despite these challenges , standardized reporting and visualization of these parameters is still useful for both fundamental understanding and practical concerns such as cell selection . To this end, bubble plots have been used on the material level to compare lithium metal electro des on four dimensions11. In this work, w e introduce ENPOLITE (energy -power -lifetime -temperature) plot s to compare cells across various chemistries, designs, and usage conditions. ENPOLITE plots represent multidimensional bubble plots derived from a non-logarithmic version of the Ragon e diagram2. Leverag ing the increasing number of open -source battery datasets , ENPOLITE compare s several hundred battery cells within a single bubble plot derived from a raw data set exceeding 1000 GB. While this plot makes some simplifications to represent the multidimensional dataset, it can be effectively used for cell comparison and selection . ENPOLITE plots of aging -related parameters illustrate differences between lifetimes . Although various age-specific variables and metadat a are contained in each set of aging data, composed of a data set of cells , the ENPOLITE plots present a simple two-dimensional graph , allowing easy compar ison of individual battery cell types . The S upporting Information depicts an overview of the cells evaluated in this study ; we also created a public website (enpolite.org) that hosts interactive versions of these plots . In contrast to the Ragone plot, the ENPOLITE plots do not show the power and energy capabilities of a cell, but the energy and power density at the respective lifetime's operating condition . The refore, the same cell type is shown at different energy and power levels when cycling currents or cycl e depths are different. The x -axis represents the used specific energy density of the individual battery cell at the begin ning of life (BOL) within its aging test and is calculated using Equation 1 . In this work, we base calculations on battery cell weight , which is less controversial yet equally important in many technical applications; cell volume -based calculations were also performed and can be found in the Supporting Information . (1) ∆𝑫𝑶𝑫 ∗𝑪𝐂𝐞𝐥𝐥(𝐁𝐎𝐋) ∗𝑼̅𝐂𝐲𝐜𝐥𝐞 𝒎𝐂𝐞𝐥𝐥= 𝐖𝐡𝐂𝐲𝐜𝐥𝐞 𝐤𝐠 Here, ∆𝐃𝐎𝐃 represents the cycling depth, 𝐂𝐂𝐞𝐥𝐥(𝐁𝐎𝐋) the discharge capacity , 𝐔̅𝐂𝐲𝐜𝐥𝐞 the mean voltage while cycling , and 𝐦𝐂𝐞𝐥𝐥 the cell mass . Thus, the x -axis represents the average amount of energy a cell has charged and discharged per kg an d per cycle in the completed ag ing test and describes the battery cell-specific operating point . The y -axis represents the specific power of the individual battery cells during cycling and is calculated using Equation 2: (2) 𝑰̅𝐂𝐡𝐚𝐫𝐠𝐞 ∗𝑼̅𝐂𝐲𝐜𝐥𝐞 𝒎𝐂𝐞𝐥𝐥= 𝐖𝐂𝐡𝐚𝐫𝐠𝐞 𝐤𝐠 Here, 𝑰̅𝐂𝐡𝐚𝐫𝐠𝐞 represents the mean charge current, 𝐔̅𝐂𝐲𝐜𝐥𝐞 the mean cycling voltage , and 𝐦𝐂𝐞𝐥𝐥 the cell mass . A typical Ragone plot depicts the dischargeable power, which can be used to determine the capability of a cell to fulfill a n application requirement. Since charge currents generally have a more significant influence on aging than discharge currents5,12,13, the y -axis shows the power used while charging . Similar to the x -axis, the y -axis in the ENPOLITE plots is normalized to the cell weight s. The achieved lifetime of the individual cells is also portrayed in the ENPOLITE plots . The comparison of lifetime data requires a measure of aging that normalizes cell weight or volume and reflects the cell realistic usability . It is expressed in the graphic as the bubble area . In this work, the used lifetime coefficient is a linear aging model , expressed in energy throughput per percentage point of cell capacity lost, normalized to the respective c ell weight. While the linear model is a simplified description of cell degradation, unable to accurately follow nonlinear aging patterns, it allows a comparison with only one value. O ther more complex aging models could be part of further Big-Data lithium -ion aging analysis that can be executed with the datasets used in this paper. The lifetime coefficient (and therefore the area of the bubble ) is calculated using Equation 3 : (𝟑) 𝑵𝐅𝐂𝐄(𝐄𝐎𝐓|𝐄𝐎𝐋)∗𝑪𝐂𝐞𝐥𝐥(𝐁𝐎𝐋)∗𝑼̅𝐂𝐲𝐜𝐥𝐞 𝒎𝐂𝐞𝐥𝐥∗ %𝐂𝐚𝐩.𝐋𝐨𝐬𝐬(𝐄𝐎𝐓|𝐄𝐎𝐋)= 𝐖𝐡𝐓𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭 𝐤𝐠 ∗%𝐂𝐚𝐩.𝐋𝐨𝐬𝐬 Here, 𝑁FCE(EOT|EOL) represents the equivalent full cycles to reach the end -of-life-criterion (EOL) or the end of the test (EOT), 𝐂𝐂𝐞𝐥𝐥(𝐁𝐎𝐋) the discharge capacity , 𝐔̅𝐂𝐲𝐜𝐥𝐞 the mean cycling voltage , 𝐦𝐂𝐞𝐥𝐥 the cell mass , and %Cap.Loss(EOT|EOL) the capacity percentage points lost. If raw data is available, the charge throughput is extracted directly. A doubling of the circle area is equivalent to a doubling of the lifetime coefficient. In line with published literature, t he lifetime linearization was normalized to a relative capacity loss of 20%. If cells did not lose 20% of their initial capacity within their aging tests , the linearization was based on their EOT. Generally, different EOL criteria can also be used. Unlike most published lifetime comparisons, the lifetime coefficient calculated here also considers cell size and compares battery cells using the mass -normalized energy throughput . Thus, different cell masses , charge throughput s, and cycli ng voltages , driven by different mean DOD s and different cell chemistries, can be considered. Finally, the cell-specific cycli ng temperature also plays a decisive role in explaining the aging behavior of lithium -ion cells5. The color of each bubble circumference depicts the cell temperature during cycling, provid ing vital infor mation to explain differences in individual cell lifetime s at similar operation points. If available, the mean cycling temperature at the can or pouch of the cell is used. If the individual cell temperature is not available, the temperature chamber setpoin t was used. It should be noted that the temperature within a cell and within the temperature chamber can vary significantly, especially for high power cycling. For cells without extractable cycli ng temperature, the bubble circumference is grey. With the values for the x - and y -axis and the lifetime bubble area established, all cyclic aging datasets can be sorted into the ENPOLITE plot. Figure 2 (a) shows the translation of an initial aging test resul t to the ENPOLITE plot for a cell from a data set with 2.9 Ah LTO cells. In addition to the metadata (cell chemistry, nominal capacity, cell format, cycli ng time) of the individual datasets, Figure 2b depicts an ENPOLITE plot of one complete data set of 10 cells. Not surprisingly, we find that cells cycled at higher temperatures (i.e., red -colored bubble edges) also have shorter lifetimes (i.e., bubble sizes) . Figure 2 : (a) Calculation of a single bubble data point for the ENPOLITE plot. Data for one individual cell from dataset LTO02 was added in the cyclic lifetime diagram; relevant metadata for the lifetime coefficient calculation s are given in the lower -left corner . The 2∗1 = 2 % Loss ∗ 𝐖𝐡 𝐤𝐠∗% Remaining Capacity Energy Throughput Cell metadata LTO02 : 𝑪𝐂𝐞𝐥𝐥(𝐁𝐎𝐋) 𝟑. 𝐡 𝒎𝐂𝐞𝐥𝐥 . 𝐤𝐠 ∆𝑫𝑶𝑫 % 𝑰 𝐂𝐡𝐚𝐫𝐠𝐞 𝑼̅𝐂𝐲𝐜𝐥𝐞 . 𝑵𝐅𝐂𝐄(𝐄𝐎𝐋) 𝟑 𝑁∗ ∗ ∗%= ∗ .1 ∗2. .1 ∗2 . % 𝐖𝐡𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐤𝐠∗% ∗ = ∗2. .1 𝐖 𝐤𝐠∆ ∗ ∗ = . ∗ .1 ∗2. .1 𝐖𝐡 𝐤𝐠 X-Axis Y-Axis Bubblesize a b green area shows the calculation for the x -axis; the red area shows the calcula tion for the y -axis; the blue color shows the calculation for the lifetime coefficient and is reflected in the bubble area within the graphic . A higher lifetime coefficient and more normalized energy throughput are reflected in a larger bubble area. (b) Dataset LTO02 with 10 cells illustrated in the ENPOLITE p lot; the color of the bubble circumference , which corresponds to the temperature, explains the differences in the lifetime coefficient for cells with similar electric operating points . Figure 3: Complete cyclic ENPOLITE lifetime plot with 783 cells . An interactive version can be found online at enpolite.org Figure 3 shows the complete ENPOLITE p lot for the cyclic aging tests of 783 different cells from 19 datasets . An interactive version and detailed description can be found in the Supporting Informatio n and on the website (enpolite.org ). In total, the ENPOLITE p lot in Figure 3 displays eight critical parameters determining the lifetime behavior of lithium -ion batte ry cells : i) used energy density, ii) used power density , and iii) energy throughput per percentage point , as well as the m etadata on the aging test including iv) cycle temperature , v) cycle duration, vi) cell chemistry , vii) cell format, and viii) nominal capacity . The plot reflects the general trend that lifetimes tend to decrease with higher energy densities and power densities. The dark -yellow -colored dataset with cells from Devie et al.14 (NMC15| 2.8Ah|18650|~670d.) show s the highest used specific energy with up to 230 Wh /kg. The high energy cells from LG Chem were cycled at 100% DOD. Only a few cells achieve d above -average lifetimes at either high energy or high power densities , such as the dark -blue -colored dataset (NMC10 |0.24Ah|pouch|~860d. ) from Harlow et al .15. Furthermore, we also o bserve that high power densities were only achieved in conjunction with lower energy densities. In most datasets, higher cycli ng temperatures resulted in a reduced lifetime , corroborating the well-known behavior of lithium - ion battery cells . This can also be seen for the above mentioned NMC10 cells at 165 Wh/kg and 50 W/kg , for which the circ le area significantly decreases for cells cycled at higher temperatures within the same operating point. Occasionally, tiny data points are also visible, representing cells quickly destroyed at their operating point in the aging test , for example , due to lithium plating at low temperatures . The exceptionally high lifetimes of c ells with Lithium Titanate ( LTO) anode s are also well represented ; the peach -colo red dataset (LTO02 |2.9Ah|prismatic|~630d. ) of Nemeth and Schröer et al.16,17 stands out with the highest lifetime observed of any cells in this plot. Furthermore , these cells also achieve the highest used specific power with up to 950 W/kg , which corresponds to current rates of up to 20C. Lastly, neighboring data points with a similar circumference color but strongly differing circle areas illustrate cell -to-cell a ging variation within a dataset , with an in -depth evaluation in an upcoming paper . We also created a n ENPOLITE p lot depicting calendar life using a similar approach . The X-axis for the calendar ENPOLITE plot depicts the state of charge (SOC) of the cells in storage ; we express this as the usable stored energy in the cell normalized to its weight. The used specific storage energy coefficient was calculated using Equation 4 : ( ) 𝑪𝐂𝐞𝐥𝐥(𝐁𝐎𝐋)∗𝑼𝐧𝐨𝐦∗𝑺𝑶𝑪𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝒎𝐂𝐞𝐥𝐥 = 𝐖𝐡𝐒𝐎𝐂 𝐤𝐠 Here, 𝐂𝐂𝐞𝐥𝐥(𝐁𝐎𝐋) represents the discharge capacity at begin ning of life, 𝑼𝐧𝐨𝐦 the nominal voltage, 𝑺𝑶𝑪𝐒𝐭𝐨𝐫𝐚𝐠𝐞 the state of charge , and 𝐦𝐂𝐞𝐥𝐥 the cell mass. The X-axis indicates how much energy per kg was stored by the cell at the begin ning of life (BOL) , and the Y-axis coefficient represents the cell storage temperature in degrees Celsius (°C). Figure 4 depicts a calendar ENPOLITE diagram , representing 307 calend ar-aged cells in total . In this plot, the typical calend ar aging test matrices are readily recognized. Most cells are stored at temperatures of 25°C, 40 °C , or 50 °C . Each cell type is typically stored at multiple SOC levels ; therefore , multiple points of the used specific stor age energy are shown . Simila r to the cyclic ENPOLITE plot, an aging measure was developed for the calendar aging data, which better reflects the battery cells ' actual usability than the relative loss of capacity metric often used in literature . The passive anode effect18,19, commonly seen in calend ar tests , was included in the evaluation and described in detail in the Supporting Information . The aging coefficient 's bubble area corresponds to the number of days until the cell energy density is reduced by 1 Wh/kg due to degrading capacity . The lifetime coefficient was calculated using Equation 5 : ( ) 𝑫𝒂 𝒆𝐓 −𝑫𝒂 𝒆𝐓 (𝑪𝐓 −𝑪𝐓 )∗𝑼𝐧𝐨𝐦 𝒎𝐂𝐞𝐥𝐥= 𝐃𝐚𝐲𝐬 𝐖𝐡𝐋𝐨𝐬𝐭 𝐤𝐠 Here, 𝑫𝒂 𝒆𝐓 −𝑫𝒂 𝒆𝐓 represents the time difference between measurements , 𝑪𝐓 and 𝑪𝐓 the first and second capacity measurements , ( T1− T2) the capacity lost between measurements , 𝑼𝐧𝐨𝐦 the nominal voltage , and 𝐦𝐂𝐞𝐥𝐥 the cell mass. The structure and input of each data point with respect to the X- and Y-axis and the size of the circle area, represented by the aging coefficient, are similar to the cyclic diagram structure shown in Figure 2. For the representation of the calendar lifetime data, however, some s triking differences have to be considered . Particularly in calendar aging tests, cells sometimes retain over 100% of their initial capacity even after a long aging period. Examples of this can be found in dataset NMC11 from Harlow et al .15 It can be seen that individual cells still increase in capacity even up to the last capacity measurement published after 5 80 days . However, an increase in capacity in the linear part of the aging process leads to a negative value by definition of the used lifetime coeff icient according to the formula above. For this reason, the valu e ∞ was inserted into the calend ar ENPOLITE plot. This value does not i mply that the cells last forever but is used when the lifetime coefficient cannot be evaluated because the corresponding cell capacitie s are still rising . The size of the se bubbles is fixed and does not represent an absolute lifetime ; they should not be compared with other datapoint sizes . Cell data with this exception are illustrated with transparent shading in the calendar ENPOLITE plot and may in fact point to excellent durability . Figure 4 shows the ENPOLITE plot of calendar lifetime data for a total of 307 cells from 11 datasets . A reference bubble, equivalent to 100 days to a loss of 1 Wh/kg, can be found in the bottom -right corner of the diagram for better estimation of the bubble area . In this diagram , a doubling of the circle area also doubles the calculated lifetime coefficien t. Generally , Figure 4 illustrates that cells stored at higher energ y/charge states lose storable energy (and thus capacity ) faster than cells stored at low energy/charge states . Used specific storage energies range from 0 Wh /kg (0% SOC) up to 225 Wh/kg represented by the dark -red-colored dataset (NCA04|2.8Ah|18650|~290d.) from Keil et al.20, which were stored at 100% SOC at 25 °C and 50 °C , respectively . Outstanding life times were achieved by lithium -nickel -manganese -cobalt -oxide (NMC) cells (NMC 11|0.24Ah|pouch|~ 580d.) from Harlow et al.15, depicted by turquoise dots , even at high used specific storage energies. Especially at 2 0 °C, they outperformed other cells without visible aging after 580 days even at high SOC. The authors attributed this to the single -crystal structure of the NMC532 cathode particles and electrolyte additives15. The influence of the storage temperatures on the lif etimes is also clea rly visible. In general, within all data sets, higher temperatures were associated with shorte ned lifetime. Few data points were aged below 20 °C and none of these belonged to the datasets showing the longest lifetime . No cells tested above 60 °C were part of the datasets in the calendar ENPOLITE plot , since side reactions prevent valid accelerat ed aging test s21. Figure 4: Complete calendar ENPOLITE lifetime plot with 307 cells . An interactive version of the plot can be found online at enpolite.org Conclusion: Lithium -ion batteries must satisfy multiple requirements for a given application, including energy density, power density, and lifetime. However, visualizing the tradeoffs between these requirements is often challenging; for instance , battery aging data is presented as a line plot with capacity fade versus cycle count , a difficult format for viewing multiple datasets . Also, s tandard lifetime plots can be challenging to interpret (e.g. , high cycle count with low energy throughput). In this work , we introduced the ENPOLITE plots , which can be used to compare large datasets of lithium -ion batter y cycli ng and calendar aging across multiple battery chemistries and usage conditions . ENPOLITE plots capture performance metrics that are relevant for applicat ions. Similar comprehensive representations of large datasets of var iable battery aging data were, to the best of our knowledge , never before shown in published literature. ENPOLITE plots (and, more generally, multidimensional plotting ) may greatly facilitate informed decisions o n battery technology development . Some of the observations were known before , e.g., that LTO are suitable for high power , or that batteries cannot be both high power and high energy density . This commonplace know ledge is now substantiated by specific values for energy and power application -specific selection. The ENPOLITE plots also reveal exemplary cells across a number of dimensions. As the battery community continues to publish data, particularly on new chemist ries, the ENPOLITE plots enable unbiased comparisons of key operating parameters to be added to published battery data bases . Finally, we mention that with non -uniform data formats being the biggest hurdle for inclusion of additional datasets, the battery c ommunity would greatly benefit from standardized and uniform battery data formats for automated evaluation. Appendix: Table I. Summary of all datasets used in this work. Cell type identifier Identifier Raw data / External # Brief description: Cell name, nominal capacity, format Aging - Type Test- duration Ref, Year e- production NMC 01 NMC 02 Raw data Raw data 48 24 Sanyo UR18650E, 1.85 Ah, 18650 Sanyo UF121285, 5 Ah, prismatic Cyc. Cyc. ~170 d ~480 d 22, 2014 GOELK NMC 03 NMC 04 Raw data Raw data 13 30 LiTec 40 Ah, pouch LiTec 40 Ah, pouch Cyc. Cal. ~550 d ~650 d 23, 2019 23, 2019 FutureBus LTO01 Raw data 23 Microvast 10 Ah, pouch Cyc. ~230 d 24, 2019 e- perfor mance NMC 05 NMC 06 Raw data Raw data 65 48 Sanyo UR18650E, 2.05 Ah, 18650 Sanyo UR18650E, 2.05 Ah, 18650 Cyc. Cal. ~410 d ~450 d 25, 2014 26, 2014 HV-Modal LTO02 LTO03 Raw data Raw data 10 16 Toshiba SCiB 2.9 Ah, prismatic Toshiba SCiB 2.9 Ah, prismatic Cyc. Cal. ~630 d ~590 d 16,17, 2020 16,17, 2020 MobilEM NCA 01 NCA 02 Raw data Raw data 183 60 Samsung INR18650 -35E, 3.4 Ah, 18650 Samsung INR18650 -35E, 3.4 Ah, 18650 Cyc. Cal. ~240 d ~450 d 27, 2020 LiMobility NMC 07 Raw data 27 440-Kokam, 40 Ah, pouch Cyc. ~300 d 28, 2015 HiEnd LFP01 Raw data 26 OMLIFE8AHC -HP, 8 Ah, cylindrical Cyc. ~260 d 29, 2016 DriveBattery NMC 08 NMC 09 Raw data Raw data 39 27 Samsung INR18650 -15L, 1.5 Ah, 18650 Hitachi 5 Ah, prismatic Cyc. Cyc. ~250 d ~580 d 30, 2017 30, 2017 Severson et al. Attia et al. LFP02 LFP03 Raw data Raw data 124 45 A123 APR18650M1A, 1.1 Ah, 18650 A123 APR18650M1A, 1.1 Ah, 18650 Cyc. Cyc. - - 9, 2019 31,2020 Naumann et al. & Spingler et al. LFP04 LFP05 External External 14 17 Sony US26650FTC1, 3 Ah, 26650 Sony US26650FTC1, 3 Ah, 26650 Cyc. Cal. ~900 d ~900 d 32,33, 2020 34, 2020 Harlow et al. NMC 10 NMC 11 External External 11 24 Li-FUN Technology 0.24 Ah, pouch Li-FUN Technology 0.24 Ah, pouch Cyc. Cal. ~860d ~580d 15, 2019 15, 2019 Preger et al. LFP06 NCA 03 NMC 12 Raw data Raw data Raw data 28 22 24 A123 APR18650M1A, 1.1 Ah, 18650 Panasonic N CR18650B , 3.2 Ah, 18650 LG Chem 18650HG2, 3 Ah, 18650 Cyc. Cyc. Cyc. ~640d ~170d ~220d 8, 2020 8, 2020 8, 2020 Schmitt et al. NMC 13 External 8 Sony US18650V3, 2.15 Ah, 18650 Cal. ~470 d 35, 2017 Schimpe et al. LFP07 External 10 Sony US26650FTC1, 3 Ah, 26650 Cal. ~230 d 36, 2018 Keil et al. NMC 14 LFP08 NCA 04 External External External 32 32 32 Sanyo UR18650E, 2.05 Ah, 18650 A12318650M1A, 1.1 Ah, 18650 Panasonic NCR18650PD, 2.8 Ah, 18650 Cal. Cal. Cal. ~310 d ~280d ~290d 37, 2016 37, 2016 20, 2016 Devie et al. NMC15 Raw data 15 LG Chem ICR18650C2 , 2.8 Ah, 18650 Cyc. ~670d 14, 2018 Acknowledgments : This work was mainly supported by the German Federal Ministry for Economic Affairs and Energy (BMWi) within the research project "E2Fuels " (03EIV011F) and "Topologieanalyse von Speichersystemen " (IGF 18957 N/1) . Y.P. was supported by the U.S. Department of Energy, Office of Electricity, Energy Storage Program. Sandia National Laboratories is a multi -missi on laboratory managed and operated by The National Technology & Engineering Solutions of Sandia, LLC, which is a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under con tract DE -NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government . The authors would like to thank J. Harlow and J. Dahn from Dalhousie University for providing updated data on their publication. P.D. thanks P. Althoff for support on the interactive version of the plot and website. The authors also thank V. De Angelis for his insights. The raw data for