Set your application energy and power requirements, then explore the Ragone plot to identify suitable storage technologies. Compare Li-ion, supercapacitors, flywheels and more with real-time cost estimates.
Application Requirements
Energy demand E
Power demand P
System mass m
kg
Results
Recommended Technology
Calculating...
Results
2.0 h
Discharge time t=E/P
100
Required E density Wh/kg
50
Required P density W/kg
—
Estimated cost
Ragone Plot (log-log scale)
Cost Comparison by Technology (approx.)
Theory & Key Formulas
X-axis: specific energy [Wh/kg] | Y-axis: specific power [W/kg] (both log scale).
Diagonal lines = constant discharge time t = Ed/Pd.
The yellow cross marks your application requirement point.
What is a Ragone Plot?
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What exactly is a Ragone plot? I see a graph with a bunch of colored blobs and diagonal lines.
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Basically, it's the "map" of the energy storage world. The x-axis shows how much energy a device can store per kilogram (specific energy), and the y-axis shows how fast it can deliver that energy per kilogram (specific power). Each blob represents a different technology, like Li-ion batteries or supercapacitors. Try moving the "Energy Demand" and "Power Demand" sliders above—the yellow cross shows your target on this map.
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Wait, really? So if my application needs a lot of energy but not much power, I'd look on the far right of the plot?
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Exactly! For instance, a remote weather station needs to run for months on a small solar panel—that's high energy, low power. You'd look for technologies on the right side. The diagonal lines are key: they show constant discharge time. A line sloping down from left to right means if you need power for a long time (like 10 hours), you must pick a technology on that line or to the right of it. Adjust the "System Mass" slider to see how your total weight requirement changes the specific values.
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So the best technology is the one whose blob contains my yellow cross? What if my cross is between two blobs?
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Great question. If your cross is between blobs, no single ideal technology exists. In practice, you'd make a trade-off or design a hybrid system. A common case is an electric vehicle: it needs high energy for range (right side) AND high power for acceleration (top side). That's why many EVs use Li-ion—it sits in a middle "sweet spot." Drag your power demand way up and watch the cross move; you'll see only a few technologies like supercapacitors can keep up, but they can't store much energy.
Physical Model & Key Equations
The core idea of a Ragone plot is comparing technologies based on two specific (per mass) properties. The key relationship is between energy, power, and discharge time.
$$ t_d = \frac{E_d}{P_d}$$
Where $t_d$ is the discharge time (in hours), $E_d$ is the deliverable energy (in Watt-hours, Wh), and $P_d$ is the deliverable power (in Watts, W). On the log-log plot, this equation appears as a straight diagonal line because $\log(t_d) = \log(E_d) - \log(P_d)$.
The plot axes use specific (per unit mass) values to allow fair comparison between systems of different sizes. The fundamental calculations to place your requirement (the yellow cross) are:
Here, $E$ is your total energy demand, $P$ is your power demand, and $m$ is the total allowable system mass. This is why changing the mass slider in the simulator directly moves your requirement point—it changes the specific performance your system must achieve.
Frequently Asked Questions
From the set energy amount E [Wh] and output P [W], the system mass m [kg] is assumed, and the energy density E/m and power density P/m are calculated. The intersection of these values is plotted. Since the mass is back-calculated from the typical density of each technology, the optimal region changes in real time as the slider is moved.
The discharge time is calculated as energy density divided by power density and is displayed as diagonal lines on the plot. For example, the area above and to the right of the 1-hour line indicates discharge in less than 1 hour, while the area below and to the left indicates it takes longer. By seeing which line the value set with the slider is closest to, the suitability of the technology for an application can be intuitively judged.
Lithium-ion batteries have high energy density (100–250 Wh/kg) and are suitable for long-duration power supply (several hours). In contrast, supercapacitors have extremely high power density (thousands of W/kg) and excel in short-duration, high-power pulses (several seconds to tens of seconds), but their low energy density makes them unsuitable for long-term energy storage.
Flywheels have high power density and excellent responsiveness, but they suffer from significant self-discharge (several percent to tens of percent loss over several hours to a day) and are not suitable for long-term energy storage. Additionally, bearing wear in the rotating parts and the need to maintain a vacuum chamber must be considered, along with installation costs and maintenance requirements.
Real-World Applications
Electric Vehicle Design: Engineers use Ragone plots to choose between battery chemistries. A city car might prioritize power for stop-and-go traffic (higher on the plot), while a long-haul truck prioritizes energy density (farther right). The simulator's power and energy sliders let you explore these competing needs.
Grid Energy Storage: For stabilizing the power grid, discharge time is critical. A 1-hour backup needs a technology on the ~1h diagonal (like some advanced Li-ion), while 4-hour storage for solar shifting needs a technology farther right (like flow batteries). The diagonal lines in the tool represent these critical time constraints.
Consumer Electronics: Smartphone design is a battle against mass. A designer sets a target mass (like 200g) and uses a Ragone plot to see which battery tech provides the most energy (longest runtime) within that weight limit. Try fixing the mass and sliding the energy demand to see if your "phone" requirement falls inside the Li-ion blob.
Hybrid Energy Systems: For applications like a hybrid electric bus, the plot shows why you can't use one device. Supercapacitors (high power) handle acceleration and braking regeneration, while batteries (higher energy) provide the base range. The simulator clearly shows these two technologies at opposite corners of the plot, explaining the need to combine them.
Common Misconceptions and Points to Note
First, don't assume the position on the Ragone plot is everything. While a marker being within a technology's area does make it a preliminary candidate, this is strictly a two-dimensional story of "energy density" and "power density". In real-world design, "third axes" like cycle life (how many charge/discharge cycles are possible), operating temperature range, safety, and maintainability become decisive. For example, even within lithium-ion, Lithium Iron Phosphate (LFP) has lower energy density than Nickel Manganese Cobalt (NMC) but wins on lifespan and safety. The simulator's "cost" primarily considers the initial purchase price, so you'll need to calculate the total cost of ownership (TCO) over, say, 10 years of operation separately.
Next, note that the "Required Energy" and "Required Power" you set with the sliders are not independent. For instance, if an electric vehicle requires "100kW output for instant acceleration", constraints come not only from the battery but also from factors like the motor and inverter's current limits and voltage drop. The simulator shows theoretical values for the storage device alone, so you must separately account for losses and limitations from the system's overall power management (like the BMS or PCS). For example, even if the calculation suggests 50kg is sufficient, a practical rule of thumb is to add at least +20% for the cooling system's weight.
Finally, avoid being overly fixated on a "single technology". This tool plots technologies separately for "comparison", but in reality, hybrid systems often become the optimal solution. The bus regenerative braking system (combined with capacitors) that your senior mentioned is a perfect example. If you set demanding conditions like "5kWh energy, 200kW power" in the simulator, the point might fall outside all single-technology areas. In such cases, think of combinations like "lithium-ion battery (for energy) + supercapacitor (for power)". The tool is the first step, visualizing "which technology excels at which characteristic".
Set your required energy capacity using slENum (range 0.1–1000 kWh) and confirm with slE slider
Set your required power output using slPNum (range 1–100 kW) and confirm with slP slider
Adjust slMNum and slM to set total mass budget in kg; the simulator calculates required energy density (Wh/kg) and power density (W/kg)
Read discharge time t=E/P and estimated cost; compare against technology envelopes (Li-ion batteries: 150–250 Wh/kg, 500–2000 W/kg; supercapacitors: 3–10 Wh/kg, 5000–15000 W/kg; lead-acid: 30–50 Wh/kg, 300–1000 W/kg)
Select the technology zone that encompasses your point on the Ragone plot
Worked Example
Electric forklift application: required energy E=50 kWh, required power P=25 kW, mass budget M=800 kg. Discharge time t=50/25=2 hours. Energy density needed=50000/800=62.5 Wh/kg; power density needed=25000/800=31.3 W/kg. This point falls below Li-ion envelope (150–250 Wh/kg available), so Li-ion with 48 V, 1000 Ah cells (cost ~USD 35000–45000) is optimal. Lead-acid would require M>1200 kg for same specs, exceeding budget.
Practical Notes
Supercapacitors excel for peak power bursts (construction drill: 5 kW for 10 seconds), but poor for sustained discharge; hybrid Li-ion+supercap architecture (75 kWh primary, 5 F module) recovers energy and extends cycle life 2× over Li-ion alone
Cost per Wh degrades sharply outside technology envelopes; requesting 300 Wh/kg from lead-acid requires parallel modules, inflating cost 40–60% versus moving to LiFePO4
Thermal limits reduce usable power density: Li-ion at 3C discharge (75 kW from 25 kWh pack) dissipates ~2 kW; ensure cooling capacity or derating occurs