an efficient and chemistry independent analysis to quantify resistive and capacitive loss contributions to battery degradation - bms battery management system

The degradation mechanism that causes deterioration of battery performance is inevitable.
While there are detailed physical and equivalent circuit-based models to predict losses due to degradation when estimating battery health, they are either incomplete or costly to calculate or both
In this study, we propose a very simple and elegant mathematical analysis independent of chemistry, which accurately calculates the period-
Loss associated with life in the battery system.
We demonstrate that the discharge curve obtained in any given degradation state of the battery can be represented by an analytical function, using simple parameter fitting, whose origin lies at the core of battery dynamics.
The model parameters are related to the potential, resistance and capacity of the battery.
We first validate our protocol using simulated cyclic data from lithium degradation
Ion battery systems are modeled with detailed electrolytic thermal calculations and show that the estimation of capacity and power attenuation> 99% accuracy using our method.
In addition, we construct a unique phase space diagram of normalized energy, power, which gives a compact representation of the system degradation state and the quantitative and qualitative trends of available power and energy.
The increasing demand for clean and abundant renewable energy makes it very attractive to use electrolytic energy storage in applications from power grids to electric vehicles.
Huge application space and huge Society
From an economic and environmental point of view, battery-based energy storage systems have become the forefront of energy research.
One of the key challenges faced widely
The widespread adoption of battery technology is a tendency for the degradation of an electrochemical battery, thus limiting their ability to provide an acceptable level of power and storing/distributing enough energy as needed
In addition to the potential huge cost of replacing the battery, uncertainty about power and energy capabilities associated with degradation can prevent faster adoption of battery technology.
Therefore, knowledge of cell health status (SOH)
The ability to quantify its impact on battery performance is critical to accurately predict the state of battery charging (SOC)
And define battery performance for different operating conditions to be well designed
Informed control algorithm in battery management system (BMS).
Health status (SOH)
It is usually used to quantify the degree of degradation including capacity and power attenuation.
Although the main electro-chemical reaction to achieve charge storage is reversible, there is an irreversible parasitic reaction in the system, resulting in deterioration of battery performance.
The interweaving of different side effects is inherently complex, and the aging/degradation of the system through capacity/power fading cannot be attributed to a single mechanism, nor can they be studied independently of each other.
For example, in lithium-ion batteries (LIB)
, Forming a fixed layer of inactive material on the electrode surface known as ei (
Solid electrolyte interface)
Resulting in an increase in internal resistance, resulting in a decrease in power and capacity.
However, the dissolution of the transition metal of the cathode material in the acidic electrolyte mainly results in capacity attenuation.
These and other mechanisms affect the attenuation of power and capacity when interacting. Build-
The inactive material on the surface of the electrode active material is up, and the blockage of the ion/electron migration channel leads to an increase in system resistance.
This is manifested as a vertical drop in the discharge profile (Voltage and time
As the term "IR "(
Current times resistance)
Negative contribution (
Discharge period)
And yes (during charge)
In the whole battery voltage.
This is considered a reversible loss because running the system at a very low current value is bound to eliminate this contribution.
Another type of degradation is the loss of the active substance (AM)
Loss of carrying active lithium or Recyclable Lithium itself (
Loss of lithium inventory (LLI))
The overall capacity of the battery is reduced.
This is represented by shrinkage on the timeline of the emission curve.
This loss is irreversible, unlike the previous one, because the lost active substance is unrecoverable.
Resistance loss can also cause capacity attenuation.
This is due to operational limitations on the system.
The discharge voltage drops due to resistance, resulting in a minimum voltage cut of the profile-
It turns off faster during the discharge, resulting in shrinkage on the capacity/timeline.
As mentioned earlier, in general, this is a recoverable loss as it is reversible.
Therefore, the reliable operation of the battery needs to accurately determine the degradation mechanism and quantitative loss in an efficient but simple way.
In the current method, a detailed physical-based model containing all the relevant electro-chemical processes in the battery can accurately predict the degradation state of the battery.
These protocols take into account all conservation equations, resulting in a partial differential equation system that performs numerical solutions on platforms such as MATLAB, COMSOL, to obtain detailed contours.
Given geometry, operations, and other system parameters, the conservation equations of mass, charge, and energy and their associated initial and boundary conditions are solved using numerical formats.
These calculations are called electricity. chemical-Thermal Model (ECT)
, Based on pseudo two-dimensional (P2D)
Model developed by Newmann group.
Recently, the ECT model has been extended to consider the side effects that cause the mechanism of resistance and capacitance degradation.
In this case, the aging mechanism is studied in detail and its effect on the system is mathematically modeled and incorporated into the conservation equation.
For example, the anode experienced degradation due to the formation of a solidelectrolyte-
Interface as mentioned earlier.
SEI formation on the anode side was simulated using Tafel dynamics, which consumes part of the applied current, and its size depends on the over
The potential of the reaction.
Therefore, the charge conservation equation will have an additional "sink" term to explain this side effect.
Due to the resistance provided by the SEI layer formed, the voltage drop is proportional to this resistance, so the capacity will also decrease.
The detailed ECT model captures these mechanisms well, as detailed in the later sections, in particular supplementary information and cited references.
Although ECT-based models can accurately describe degradation, they are very expensive to calculate and to some extent accurate because it contains all the relevant degradation mechanisms in a representative way
The calculation cost of these detailed models is calculated through the so-called order reduction model (ROM)
Where the significant reduction in computational time is by approaching the partial differential equation in the ECT model and concentrating the detailed physics by means of volume averaging through ordinary differential equations or linear algebraic expressions.
However, when simulating degradation, they are unable to capture the degradation dynamics of spatial dependence, so for those with thick electrodes or high C-
Rate operating conditions.
In addition to these physical-based models, there are other ways to estimate the SOC/SOH of the system based on the use of simpler algorithms, lacking the accuracy of ECT-based models.
For example, the equivalent circuit model (ECM)
Technology-based technology relies on a series of circuits with resistors and capacitors to represent the battery.
Although the thevedo model is simple and widely used, it can only be used to explain the temporary response of the battery under a given soc.
The relationship between SOC and OCV, etc.
Cannot be described using these models.
Model Parameter identification suitable for specific ECM is also a tedious process.
In addition, there are non-
Invasive and non-invasive
Disruptive technologies like electrical impedance spectrum (EIS)
It reveals the mechanism of cell aging.
This method relies on the analysis of the Quest graph with frequency, which is characteristic of different geometric properties and reactions/processes in the system.
This agreement also has its own shortcomings because it is not suitable
Board application.
In addition, processes with very similar time scales (
This is very possible)
, Cannot be distinguished on the frequency chart.
All these observations indicate the need for a simpler but complete protocol that quantifies the different types of degradation in the system.
In the current study, we propose a simple and powerful protocol to represent the discharge profile of the battery system that captures its distinctive features, compared to the new battery (
Or earlier degradation status)
Can predict the degradation state and solve the loss of capacitance and resistance.
Due to the operating conditions and the thermodynamics of the system, we recognize the open potential (OCP)
Therefore, the discharge curve in the battery has a logarithmic change in voltage over time.
We use this concept and use the asymmetric sigmoidal function with linear perturbation to represent the discharge profile, the coefficient of which can represent the physical properties of the system in a minimum but accurate manner.
These coefficients also result in separate prediction of loss, resulting in simultaneous estimation of soc soh and available power in the system.
We also present a new method for analyzing degradation whereby the normalized power and capacity of the battery are plotted as a function of aging.
These maps quantitatively and qualitatively predict the type of degradation, the losses generated, and the transfer between resistance losses and capacity losses during the cycle, with a comprehensive understanding of the state of the system.