State space modeling of parallel capacitors

In summary, the conversation discusses the difficulty in describing a system input filter consisting of several capacitors and an inductance in the state-space representation. The topology is described as a DC source connected to an inductor, which is then connected to two parallel capacitors with different properties. The person is struggling to write the governing equations in a linear form, but eventually finds a solution using a system of first order differential equations. The conversation also mentions that if the filter is connected to a stiff voltage source, the output voltage will ramp up to the input voltage according to the system's eigenvalues.
  • #1
SunnyBoyNY
63
0
Hi there,

I have been thinking about how to actually describe a system input filter consisting of several caps and an inductance in the state-space representation.

The topology follows: a DC source to inductor to two parallel caps that are referenced to the same potential as the voltage source. One cap is film and the other one is electrolytic. Hence, the latter cap has significant resistance.

I cannot write the governing equations in a form where a time derivative of energy storage element could be written as a linear sum of other state space variables. This makes me think that the system cannot be described in the present form.

On the other hand, each cap can have different voltage so the two voltages are not slaved one to the other. Circulating current between the two (provided zero inductor current) is determined by the voltage difference and ESR (resistance).

Any thought?

SunnyBoy
 
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  • #2
Okay, so it took me one more day to solve the problem. Of course, the system can be described via a system of first order DEs. Solution follows:

[itex]

\begin{pmatrix}
\frac{d}{dt} i_L \\
\frac{d}{dt} v_{C1} \\
\frac{d}{dt} v_{C2} \\
\end{pmatrix}
=

\begin{pmatrix}
-\frac{\text{Rl}}{L} & -\frac{1}{L} & 0 \\
\frac{1}{\text{C1}} & -\frac{1}{\text{C1} \text{Rc}} & \frac{1}{\text{C1} \text{Rc}} \\
0 & \frac{1}{\text{C2} \text{Rc}} & -\frac{1}{\text{C2} \text{Rc}}
\end{pmatrix}
.
\begin{pmatrix}
i_L \\
v_{C1} \\
v_{C2} \\
\end{pmatrix}
[/itex]

If the filter connects to a stiff voltage source, the output voltage ramp will ramp up to Vin according to the system eigenvalues.

I.e. for Vin = 40 V, C1 = 50 uF, C2 = 11 mF, Rc = 18 mOhms, L = 10 nH, Rl = 10 mOhms the eigenvalues are

-1056456+1411806 i
-1056456-1411806 i
-3249
 

What is state space modeling of parallel capacitors?

State space modeling is a mathematical technique used to describe the behavior of a system over time. In the context of parallel capacitors, it involves creating a mathematical model that represents the relationship between the voltages and currents of the capacitors in the circuit.

Why is state space modeling important for parallel capacitors?

State space modeling allows us to accurately predict the behavior of parallel capacitors in a circuit, which is essential for designing and optimizing electronic systems. It also helps us understand the underlying principles and relationships between the different components in the circuit.

What are the advantages of using state space modeling for parallel capacitors?

State space modeling offers several advantages, including the ability to model complex systems, handle nonlinearities, and easily incorporate external inputs. It also provides a more intuitive and visual representation of the system, making it easier to analyze and understand.

Are there any limitations to state space modeling of parallel capacitors?

While state space modeling is a powerful tool, it does have some limitations. It assumes that the system is linear and time-invariant, which may not always be the case in real-world circuits. Additionally, creating an accurate model may require detailed knowledge of the circuit components and their behaviors.

How is state space modeling of parallel capacitors different from other modeling techniques?

State space modeling is a time-domain approach that focuses on the internal states of the system, such as voltages and currents. In contrast, other techniques like frequency-domain modeling, such as using transfer functions, focus on the input-output relationship of the system. State space modeling is also more flexible and can handle a wider range of system complexities compared to other methods.

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