🧑🎓
What exactly is a Ragone plot? I see a graph with a bunch of colored blobs and diagonal lines.
🎓
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.
🧑🎓
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?
🎓
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.
🧑🎓
So the best technology is the one whose blob contains my yellow cross? What if my cross is between two blobs?
🎓
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.
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 $\log(t_d) = \log(E_d) - \log(P_d)$.
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".
Related Engineering Fields
The concept behind this Ragone plot is engineering itself, centered on "characteristic mapping" and "using the right tool for the job". The field most directly connected is "Materials Engineering". The main reason the lithium-ion battery "cloud" on the graph has expanded significantly to the upper right over the last 20 years is the evolution of cathode materials (lithium cobalt oxide → NMC → high-nickel systems). Conversely, the lead-acid battery area has barely moved, indicating fundamental material limits.
Next, the connection to "Thermo-fluid Engineering" and "Structural Mechanics" is also deep. Continuous high-power output inevitably generates heat. With insufficient thermal design, you cannot achieve the performance the simulator indicates. For instance, air cooling vs. liquid cooling significantly changes system volume and auxiliary mass, reducing effective energy density. Furthermore, the weight of protective structures to withstand vibration and shock becomes a non-negligible "hidden parameter", especially in automotive and aerospace applications.
At a more advanced level, it directly connects to the fields of "Control Engineering" and "System Optimization". When designing a hybrid system (e.g., battery + capacitor), how do you decide the capacity split? This boils down to a mathematical optimization problem: predicting the load profile (what power is needed at what timescale) and minimizing total cost or weight. If you've learned the concept of "discharge time $t = E/P$" with this tool, your next step could be advanced thinking like applying Fourier transform to time-series load data to allocate tasks to devices with different time constants.
For Further Learning
First, "getting hands-on to develop an intuition" is most important. Use this simulator to input values from actual product specification sheets. For example, search for "home battery storage ○○kWh, rated output △△kW", replicate those values with the sliders, and check where it plots, and whether the estimated mass and cost seem realistic. Doing this for just 5 products will give you a tangible feel for market technology trends.
If you want to deepen the mathematical background, study using the keywords "dimensionless numbers" and "trade-off relationships". The Ragone plot classifies technologies using two dimensionless performance metrics: energy density and power density. In many engineering systems, it's impossible to simultaneously maximize multiple desirable characteristics; they exist in a trade-off relationship. In the battery world, a formulation close to this relationship is the concept of "Ragone's relation" (though not exactly the same). For instance, from the relationship between internal resistance $R$ and capacity $C$, we have relations like $P_{max} \propto 1/R$ and $E \propto C$, and $R$ and $C$ often trade off based on materials and structure.
As a next step, I recommend moving to "dynamic simulation". This tool is primarily for steady-state performance comparison. However, real energy systems change state moment by moment (State of Charge (SOC) change, temperature rise, degradation, etc.). For more realistic design, you need to use tools like Simulink or AMESim to simulate how voltage, current, and temperature change over time under a given load pattern. The professional design workflow is to narrow down candidates using this Ragone plot, then use their parameters for dynamic simulation.