A Method for Assessing the Technical Condition of Traction Batteries Using the Metalog Family of Probability Distributions
Abstract
1. Introduction
2. Research
2.1. Research Objects
2.2. Research Bench
2.3. Methodology
3. Discussion
3.1. Case 1
3.2. Case 2
3.3. Case 3
3.4. Case 4
4. Conclusions
- (1)
- A functional package in which the aging processes proceed properly. It is characterized by the capacity of individual battery cells ranging from 100 to 50% of the nominal capacity. The PDF waveform is close to a normal distribution with a very small standard deviation between the capacities of individual cells.
- (2)
- The package is faulty and needs to be repaired. It is characterized by the capacity of individual battery cells ranging from 0 to 100% of the nominal capacity. PDF waveform with one extreme with a very large standard deviation between the capacities of individual cells. The location of the extreme PDF indicates the purpose of repair. All cells with a capacity less than the extreme value should be replaced with cells with a capacity equal to or greater than the extreme value. All cells with a capacity greater than 50% of the nominal capacity can be reused to compose a package of a given capacity.
- (3)
- The package is beyond repair and should be disposed of. It is characterized by the capacity of individual battery cells ranging from 0 to 50% of the nominal capacity. PDF waveform with one or two extreme values with a very large standard deviation between the capacities of individual cells.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
BEV | Battery Electric Vehicles |
BMS | Battery Management System |
CNG | Compressed Natural Gas |
FCV | Fuel Cell Vehicle |
HEV | Hybrid Electric Vehicle |
HVO | Hydrogenated Vegetable Oil |
LNG | Liquide Natural Gas |
LPG | Liquide Petroleum Gas |
LTO | Lithium Titanium Oxide |
NG | Natural Gas |
NiMH | Nickel-Metal Hydride |
NMC | Nickel Manganese Cobalt |
PHEV | Plug-in Hybrid Electric Vehicle |
RES | Renewable Energy Source |
SoC | State of Charge |
TMS | Thermal Management System |
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Cell | Capacity (A/h) | Percent from Maximum Capacity | Working Time | Reason for Stopping |
---|---|---|---|---|
1 | 5.445 | 83.77% | 01:05:22 | Discharge minimum voltage |
2 | 5.316 | 81.79% | 01:03:49 | Discharge minimum voltage |
3 | 5.087 | 78.25% | 01:01:03 | Discharge minimum voltage |
4 | 5.245 | 80.7% | 01:02:59 | Discharge minimum voltage |
5 | 5.015 | 77.16% | 01:00:13 | Discharge minimum voltage |
6 | 5.13 | 78.93% | 01:01:36 | Discharge minimum voltage |
7 | 5.158 | 79.35% | 01:01:55 | Discharge minimum voltage |
8 | 5.18 | 79.69% | 01:02:11 | Discharge minimum voltage |
9 | 4.962 | 76.34% | 00:59:34 | Discharge minimum voltage |
10 | 5.084 | 78.22% | 01:01:02 | Discharge minimum voltage |
11 | 5.164 | 79.45% | 01:02:00 | Discharge minimum voltage |
12 | 5.204 | 80.06% | 01:02:28 | Discharge minimum voltage |
13 | 5.071 | 78.01% | 01:00:52 | Discharge minimum voltage |
14 | 5.146 | 79.16% | 01:01:46 | Discharge minimum voltage |
15 | 5.146 | 79.18% | 01:01:46 | Discharge minimum voltage |
16 | 5.039 | 77.52% | 01:00:30 | Discharge minimum voltage |
17 | 5.01 | 77.08% | 01:00:08 | Discharge minimum voltage |
18 | 5.063 | 77.9% | 01:00:46 | Discharge minimum voltage |
19 | 5.234 | 80.52% | 01:02:49 | Discharge minimum voltage |
20 | 5.206 | 80.09% | 01:02:29 | Discharge minimum voltage |
21 | 4.995 | 76.85% | 00:59:59 | Discharge minimum voltage |
22 | 5.236 | 80.55% | 01:02:51 | Discharge minimum voltage |
23 | 5.309 | 81.67% | 01:03:44 | Discharge minimum voltage |
24 | 5.489 | 84.44% | 01:05:55 | Discharge minimum voltage |
25 | 5.24 | 80.61% | 01:02:54 | Discharge minimum voltage |
26 | 5.42 | 83.39% | 01:05:03 | Discharge minimum voltage |
27 | 5.508 | 84.74% | 01:06:07 | Discharge minimum voltage |
28 | 5.633 | 86.67% | 01:07:39 | Discharge minimum voltage |
Count | 28 |
---|---|
Minimum | 4.962 |
Maximum | 5.633 |
Mean | 5.20482 |
StdDev | 0.169515 |
Probability | |
---|---|
0.05 | 4.994999885559 |
0.25 | 5.084000110626 |
0.5 | 5.179999828339 |
0.75 | 5.309000015259 |
0.95 | 5.507999897003 |
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Caban, J.; Małek, A.; Kroczyński, D. A Method for Assessing the Technical Condition of Traction Batteries Using the Metalog Family of Probability Distributions. Energies 2024, 17, 3096. https://doi.org/10.3390/en17133096
Caban J, Małek A, Kroczyński D. A Method for Assessing the Technical Condition of Traction Batteries Using the Metalog Family of Probability Distributions. Energies. 2024; 17(13):3096. https://doi.org/10.3390/en17133096
Chicago/Turabian StyleCaban, Jacek, Arkadiusz Małek, and Dariusz Kroczyński. 2024. "A Method for Assessing the Technical Condition of Traction Batteries Using the Metalog Family of Probability Distributions" Energies 17, no. 13: 3096. https://doi.org/10.3390/en17133096
APA StyleCaban, J., Małek, A., & Kroczyński, D. (2024). A Method for Assessing the Technical Condition of Traction Batteries Using the Metalog Family of Probability Distributions. Energies, 17(13), 3096. https://doi.org/10.3390/en17133096