Online State-of-Health Estimation for NMC Lithium-Ion Batteries Using an Observer Structure
Abstract
:1. Introduction
2. Online State-of-Health Estimator
2.1. OCV Curve
2.1.1. Basics
- Due to current direction, the OCV potential can be higher or lower; therefore, a hysteresis is observed for most cell chemistries. While the hysteresis for NMC cells is only a few mV, it can be a multiple of 10 mV for lithium iron phosphate (LFP) cells. Baghdadi et al. [40] and Lavigne et al. [41] both show in their studies that the hysteresis increases at low and high SoC. In typical SoC usage windows, the effect is very small. In this investigation, the focus is on NMC. Therefore, hysteresis effects are neglected for the moment.
- Often the relaxation time also becomes larger with older cells [36].
2.1.2. Aging Characteristics
2.2. Approach
2.3. Realization
2.3.1. Weighting
2.3.2. OCV Gradients
2.3.3. Mean Correction Factor
2.3.4. Conditional SoH Update
3. Experimental Evaluation
3.1. Battery System under Test
3.2. Real Usage Data
3.3. Checkup Measurements
3.4. Parametrization
3.4.1. Weighting-Rules Parameters
3.4.2. Dynamic Parameters
4. Result and Discussion
4.1. Result
4.2. Discussion
4.2.1. Dynamic Behavior
- The estimator is capable of compensating for initial SoH deviations. With the chosen parameters, the initial deviation of approximately 2% is compensated for in about four months. This might seem slow, but compared to the expected lifetime of the battery used it is rather a short period. The dynamic parameters were intentionally chosen to be conservative to avoid overshooting and to obtain a smooth curve. In practice, unknown initial deviations significantly larger than 2% are not to be expected.
- The recorded data used for the evaluation have a sample time of 6 s. Typically, BMS tasks have a cycle time in the range of several milliseconds. Therefore, we expect to see a different dynamic behavior when the observer runs directly on a BMS, as the observer will be acting on more dynamic data because of the higher sample rate. This will primarily affect the data being selected by the weighting rules (Equations (5a) and (5b)), which in turn results in a different calculation of the mean correction factor mmean (Equation (13)). Therefore, it is expected that it is necessary to adapt the dynamic parameters of the algorithm when the observer is run directly on a BMS.
- The accuracy and dynamic behavior of the estimator needs to be better verified. The presented results are initial validation results that give an impression of the performance and behavior of the estimator. The two available checkup measurements are not enough to evaluate the accuracy of the estimator in general. It was not possible to carry out more checkup measurements because the bus with the battery pack under consideration was in operational use most of the time and the workshop routine for carrying out the checkup measurement (see Section 3.3) is time-consuming. Therefore, the fleet operator did not agree to any further checkup measurements. But a new field study has already been started to validate the estimator, integrated in a BMS, with more frequent checkup measurements. The results will be published in future work.
- It must be noted that the observed period of about two years is short in comparison to the expected lifetime of the battery examined. Therefore, the dynamic behavior in the future cannot fully be inferred from the assessed data. It is planned to further monitor the system and to perform additional checkup measurements to obtain more confidence in the dynamic interpretation.
4.2.2. Parametrization
- The parameterization demonstrated in Section 3.4 is based on statistical analysis of the available real operating data. In cases where this is not possible, it might be an option to use synthetic data from simulations to find a suitable parameter set for the respective system. Ultimately, the option remains to set the parameters based on know-how about the system used, as the rule-based design of the estimator gives an intuitive way to understand the influence of each parameter.
4.2.3. Alternative Methods and Possible Extensions
- A common approach to SoH estimation is to use a Kalman filter (KF) [48]. The difference to the approach chosen in this work lies in the calculation of the feedback term L (see also Figure 2). In the Kalman filter, the feedback is computed recursively by a computational rule which results from minimizing the mean square estimation error while considering uncertainties in the system model as well as in the measurements. It can be proved that the KF gives the optimal minimum mean square error estimate under some specific conditions. While in practice these conditions are often violated, the KF still results in acceptable performance in most cases. Thus, the KF is widely used in practice. The “magic” to acquire an acceptable dynamic behavior lies mainly in the parameterization of the system noise covariance matrix. However, there is no universal approach to this, leaving engineers with a lengthy trial-and-error process. In contrast, our motivation was to develop feedback that could be parametrized in an intuitive way, since the meaning of each parameter is directly interpretable.
- Currently, the internal resistance Ri in Equation (9) is considered to be constant over the battery’s lifetime, which is not true, as the resistance increases significantly as the battery ages. Therefore, the estimate of SoHc unintentionally also compensates for aging effects originating from the resistance increase. The solution would be to implement a separate estimator for the aging effects in Ri and to use the estimate in Equation (9). For example, the structure shown here could also be applied to implement such an Ri estimator.
- Another possible extension to the estimator would be to add an additional model which accounts for calendric aging during long standstill phases where the estimator is not updated. However, in the commercial vehicle applications the standstill phases are significantly lower in comparison to passenger car applications, so that the estimator would be most probably able to self-correct for these deviations during the runtime after standstill phases.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Specification | |
---|---|
Cell chemistry | NMC—Graphite |
Cell design | pouch |
Pack configuration | 90s2p |
Nominal capacity | 92 Ah |
Energy | 30.6 kWh |
Nominal voltage | 333 V |
Voltage (min) | 243 V |
Voltage (max) | 378 V |
Discharging power max (10 s) | 266 kW |
Charging power max (10 s) | 153 kW |
Continuous power (RMS) | 77 kW |
Charge/discharge current max (10 s) | 736 A |
Continuous current (RMS) | 230 A |
Parameter | Value |
---|---|
Time period | 1 December 2016 to 22 January 2019 |
Operational days/Overall days | 712/783 |
Mean daily operation time in hours | 13.8 |
Mean cell temperature in °C | 27 |
Mean SoC in % | 83 |
Test Device | Nominal Power | Measurement | Full Scale | Accuracy |
---|---|---|---|---|
Gustav Klein Type 3865 | 100 kW | Voltage | Up to 1000 V DC | 0.5% fs ± 1 Digit/12 Bit |
Current | Up to 600 A | 0.5% fs ± 1 Digit/12 Bit |
Parameter | Value |
---|---|
12 A | |
15 A/30 s | |
3.5 h | |
6%/31% | |
23 °C/27 °C |
Rule | Proportion in % | RMSE() |
---|---|---|
Current Limitation | 38.69 | 0.0211 |
Current History | 35.27 | 0.0283 |
Time since last update | 44.08 | 0.0227 |
Delta SoC | 66.26 | 0.0276 |
Temperature | 30.15 | 0.0251 |
All | 0.78% | 0.0155 |
Parameter | Value |
---|---|
0.90/1.05 | |
0.01 |
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Neunzling, J.; Winter, H.; Henriques, D.; Fleckenstein, M.; Markus, T. Online State-of-Health Estimation for NMC Lithium-Ion Batteries Using an Observer Structure. Batteries 2023, 9, 494. https://doi.org/10.3390/batteries9100494
Neunzling J, Winter H, Henriques D, Fleckenstein M, Markus T. Online State-of-Health Estimation for NMC Lithium-Ion Batteries Using an Observer Structure. Batteries. 2023; 9(10):494. https://doi.org/10.3390/batteries9100494
Chicago/Turabian StyleNeunzling, Jan, Hanno Winter, David Henriques, Matthias Fleckenstein, and Torsten Markus. 2023. "Online State-of-Health Estimation for NMC Lithium-Ion Batteries Using an Observer Structure" Batteries 9, no. 10: 494. https://doi.org/10.3390/batteries9100494
APA StyleNeunzling, J., Winter, H., Henriques, D., Fleckenstein, M., & Markus, T. (2023). Online State-of-Health Estimation for NMC Lithium-Ion Batteries Using an Observer Structure. Batteries, 9(10), 494. https://doi.org/10.3390/batteries9100494