A Comparative Review of Capacity Measurement in Energy Storage Devices
Abstract
1. Introduction
2. Capacity Measurement
3. Applications of Capacity Measurements
3.1. Energy Management Techniques
3.2. Battery Management System
3.3. Charge Controller
3.4. Hybrid Energy Storage System
Advantages | Disadvantages | |
---|---|---|
Passive | Simplest form | Poor overall performance |
Direct connections between ESDs | Uncontrollable power sharing | |
Single converter | ESDs are coupled | |
Lightweight | Exhibits highly volatile drive cycles | |
Cheap | No control over power/energy split | |
Reduction in individual ESD stresses | High dynamic current draw leads to increased ESD degradation | |
Improves peak deliverance capability, efficiency, and cycle life of individual ESD | Over-and under-utilisation leads to increased degradation and reduced use | |
Peak-shaving capability | Poor response to high power demands | |
Semi-active | Increased controllability | Two converters |
Extended ESD usable life | Increased costs | |
More practical power/energy split | Increased weight | |
Further advantages are dependent on the placement of the 2nd converter | Decreased efficiency due to increased operational losses | |
Active | Optimal ESD use | Converter on each ESD |
Reduced ESD degradation | Increased costs | |
Practical flexibility and controllability of energy/power flow | Decreased efficiency due to increased operational losses | |
Increased complexity |
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Advantages | Disadvantages | |
---|---|---|
Experimental—Direct | ||
Ah counting | Simple application Least affected by other parameters (i.e., DoD, temperature and c-rate) | Time and energy consuming Accuracy relies on the quality of the measuring probes Requires a constant low current feed and constant 25 °C—this is unrealistic in real-life applications |
Capacity test | Easy method Good accuracy | Challenging to inspect in real-time as fully charged capacity is not transient |
Ohmic internal resistance | Simple and easy technique | Sensitive to sampling frequency, SOC, temperature, and timescale of measuring techniques |
Electrochemical impedance spectroscopy (EIS) | Several crucial battery parameters are measured—double layer capacitance, SEI-resistance and charge-transfer resistance Noninvasive | Large fluctuations are observed due to insufficient algorithm and calibration platforms SOC and temperature sensitive |
Destructive test | Precise deterioration information can provide high SOH estimation accuracy | Techniques require destructive intervention, thus not suitable for systems in industrial settings |
Cycle number counting | Simple and easy technique No requirement for specialised equipment | Full cycles are rarely used Capacity fade alters the duration of a cycle |
Experimental—Indirect | ||
Charging curve | Good reliability Easy implementation | Less accurate—does not account for effect of temperature Accuracy requires discharge/charge maximum and minimum voltage be the same as that of the full-health charging curve |
Ultrasonic analysis | Detects internal flaws without dismantling Noncontact, nondestructive method; can be combined with other techniques to improve accuracy | Requires a pulse generator, receiver, transducer and monitor Extensive research and refinement of this method is still required |
ICA-DVA | Applicable to various types of batteries Provides more sensitive ageing-information than charge/discharge curves Can be combined with machine learning to improve precision | Requires small current rates—C/25, for credible accuracy Requires microcontrollers to perform complex numerical deductions with higher computational work Requires effective filtering to remove noise Estimation is sensitive to temperature change |
Acoustic emission | Seldomly requires the battery’s history Detects sound waves where the battery is not subjected to external mechanical stimulus | Less effective on a battery that is not in the charge/discharge process |
FBG | Nonelectrical Outputs are not affected by electromagnetic interference Can simultaneously measure battery surface strain and temperature distribution | Needs further research and refinement |
Model-based—Data-driven | ||
Optimization algorithm | Small requirement of prior knowledge Stable outcome High accuracy | Different model parameter combinations result in different discrepancies Long computational time |
Empirical and fitting | Does not require a thorough understanding of the electrochemical cell design or material properties Faster computational deductions | Quality of experimental data largely influences this model; certainty of a single variable is difficult to achieve |
Sample entropy | Higher computational speed than approximate entropy Self-match cancellation features Can be combined with machine learning to improve performance | Can require large memory for computation as well as large computational time |
Machine learning | Flexible Real-time implementation High prediction accuracy | Collecting training data is lengthy and expensive |
Model-based—Adaptive filtering | ||
Electrochemical model | Combination of various validation data can yield very accurate results; usage in real-time battery state-estimation | Solution deduction complexity High computational load Validation data combination is difficult to achieve; |
Equivalent circuit model (ECM) | Low computational load Convenient real-time application | Computational complexity Results are sensitive to model accuracy |
Hybrid techniques | High accuracy Good online application prospect; | Noise can diminish parameter identification Can lead to cross-interference, which can impede algorithm accuracy and numerical stability Require further testing on the variety of batteries |
Direct | State Estimation | Prediction | |||||
---|---|---|---|---|---|---|---|
Time (s) | SOC (%) | Function | SOC (%) | m | Function | SOC (%) | |
0 | 100 | Historical data: linear pattern (y = mx + c) where m = Δy/Δx | |||||
1 | 99.5 | ||||||
2 | 99 | ||||||
3 | 98.5 | ||||||
4 | 98 | ||||||
5 | 97.5 | ||||||
6 | 97 | ||||||
7 | 96.5 | ||||||
8 | 96 | ||||||
9 | 95.5 | ||||||
10 | 95 | ||||||
11 | 94.5 | 94.5 | −0.5 | f(x) = (−0.5)x + 100 | 94.5 | ||
12 | 94 | 94 | −0.5 | f(x) = (−0.5)x + 100 | 94 | ||
13 | 93 | 93.5 | −0.5 | f(x) = (−0.5)x + 100 | 93.5 | ||
14 | 91.5 | 93 | −1 | f(x) = (−1)x + 106 | 92 | ||
15 | 88 | 92.5 | −1.5 | f(x) = (−1.5)x + 112.5 | 90 | ||
16 | 83.5 | 92 | −3.5 | f(x) = (−3.5)x + 140.5 | 84.5 | ||
17 | 77.5 | 91.5 | −4.5 | f(x) = (−4.5)x + 155.5 | 79 | ||
18 | 70 | 91 | −6 | f(x) = (−6)x + 179.5 | 71.5 | ||
19 | 59.5 | 90.5 | −7.5 | f(x) = (−7.5)x + 205 | 62.5 | ||
20 | 48 | 90 | −10.5 | f(x) = (−10.5)x + 259 | 49 |
Direct Measurement | State Estimation | Prediction | |||
---|---|---|---|---|---|
Time (s) | SOC (%) | SOC(%) | % Error | SOC (%) | % Error |
11 | 94.5 | 94.5 | 0.00 | 94.5 | 0.00 |
12 | 94 | 94 | 0.00 | 94 | 0.00 |
13 | 93 | 93.5 | 0.54 | 93.5 | 0.54 |
14 | 91.5 | 93 | 1.64 | 92 | 0.55 |
15 | 88 | 92.5 | 5.11 | 90 | 2.27 |
16 | 83.5 | 92 | 10.18 | 84.5 | 1.20 |
17 | 77.5 | 91.5 | 18.06 | 79 | 1.94 |
18 | 70 | 91 | 30.00 | 71.5 | 2.14 |
19 | 59.5 | 90.5 | 52.10 | 62.5 | 5.04 |
20 | 48 | 90 | 87.50 | 49 | 2.08 |
Load Difference | Load Efficiency | Total Time Delay (s) | Time Efficiency (%) | ||
---|---|---|---|---|---|
2 s Sample interval | RB | 0 | ~1 | 4 | ~79 |
OB | 0 | ~1 | 4 | ~79 | |
1 s Sample interval | RB | 0 | ~1 | 3 | ~84 |
OB | +5 | ~1.2 | 2 | ~89 | |
0.5 s Sample interval | RB | 0 | ~1 | 2.5 | ~87 |
OB | +1.25 | ~1.03 | 1 | ~95 | |
0.1 s Sample interval | RB | 0 | ~1 | 2.1 | ~89 |
OB | +0.25 | ~1.003 | 0.2 | ~99 |
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Townsend, A.; Gouws, R. A Comparative Review of Capacity Measurement in Energy Storage Devices. Energies 2023, 16, 4253. https://doi.org/10.3390/en16104253
Townsend A, Gouws R. A Comparative Review of Capacity Measurement in Energy Storage Devices. Energies. 2023; 16(10):4253. https://doi.org/10.3390/en16104253
Chicago/Turabian StyleTownsend, Ashleigh, and Rupert Gouws. 2023. "A Comparative Review of Capacity Measurement in Energy Storage Devices" Energies 16, no. 10: 4253. https://doi.org/10.3390/en16104253
APA StyleTownsend, A., & Gouws, R. (2023). A Comparative Review of Capacity Measurement in Energy Storage Devices. Energies, 16(10), 4253. https://doi.org/10.3390/en16104253