Engineering:State of charge

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Short description: Value of the charge level of an energy storage system relative to its capacity
"Coulomb counter" redirects here. For circuits consuming all of a battery's charge, see Joule ringer.

State of charge (SoC) quantifies the remaining capacity available in the battery at a given time and in relation to a given state of ageing.[1] It is usually expressed as percentage (0% = empty; 100% = full). An alternative form of the same measure is the depth of discharge (DoD), calculated as 100 - SoC (100% = empty; 0% = full). It refers to the amount of charge that may been used up if the cell is fully discharged.[2] State of charge is normally used when discussing the current state of a battery in use, while depth of discharge is most often used to discuss a constant variation of state of charge during repeated cycles.[1][3]

In electric vehicles

In a battery electric vehicle (BEV), the state of charge indicates the remaining energy in the battery pack.[4] It is the equivalent of a fuel gauge.


The state of charge (SOC) can help to reduce electrical car's owners' anxiety when they are waiting in the line or stay at home since it will reflect the progress of charging and let owners know when it will be ready.[5] However on any vehicle dashboard, especially in plug-in hybrid vehicles, the state of charge presented as a gauge or percentage value may not be representative of a real level of charge. A noticeable amount of energy may be reserved for hybrid-work operations. Examples of such cars are Mitsubishi Outlander PHEV (all versions/years of production), where 0% of the state of charge presented to the driver is a real 20-22% of charge level (assuming zero level as the lowest level of charge permitted by car producer). Another one is BMW i3 REX (Range Extender version), where about 6% of SOC is reserved for PHEV-alike operations.


State of charge is also known to impact battery aging.[1][6] To extend battery lifetime, extremes of state of charge should be avoided and reduced variations windows are also preferable.[7][8][9]

Determining SoC

Usually, SoC cannot be measured directly but it can be estimated from direct measurement variables in two ways: offline and online. In offline techniques, the battery desires to be charged and discharged in constant rate such as Coulomb-counting. This method gives precise estimation of battery SoC, but they are protracted, costly, and interrupt main battery performance. Therefore, researchers are looking for some online techniques.[10] In general there are five methods to determine SoC indirectly:[11] [12]

  • chemical
  • voltage
  • current integration
  • Kalman filtering
  • pressure

Chemical method

This method works only with batteries that offer access to their liquid electrolyte, such as non-sealed lead acid batteries. The specific gravity of the electrolyte can be used to indicate the SoC of the battery.

Hydrometers are used to calculate the specific gravity of a battery. To find specific gravity, it is necessary to measure out volume of the electrolyte and to weigh it. Then specific gravity is given by (mass of electrolyte [g]/ volume of electrolyte [ml])/ (Density of Water, i.e. 1g/1ml). To find SoC from specific gravity, a look-up table of SG vs SoC is needed.

Refractometry has been shown to be a viable method for continuous monitoring of the state of charge. The refractive index of the battery electrolyte is directly relatable to the specific gravity or density of the electrolyte of the cell.[13][14]

Voltage method

This method converts a reading of the battery voltage to SoC, using the known discharge curve (voltage vs. SoC) of the battery. However, the voltage is more significantly affected by the battery current (due to the battery's electrochemical kinetics) and temperature. This method can be made more accurate by compensating the voltage reading by a correction term proportional to the battery current, and by using a look-up table of battery's open circuit voltage vs. temperature.

In fact, it is a stated goal of battery design to provide a voltage as constant as possible no matter the SoC, which makes this method difficult to apply.

Current integration method

This method, also known as "Coulomb counting", calculates the SoC by measuring the battery current and integrating it in time. Since no measurement can be perfect, this method suffers from long-term drift and lack of a reference point: therefore, the SoC must be re-calibrated on a regular basis, such as by resetting the SoC to 100% when a charger determines that the battery is fully charged (using one of the other methods described here).

Combined approaches

Maxim Integrated touts a combined voltage and charge approach that is claimed superior to either method alone; it is implemented in their ModelGauge m3 series of chips, such as MAX17050,[15][16] which is used in the Nexus 6 and Nexus 9 Android devices, for example.[17]

Kalman filtering

To overcome the shortcomings of the voltage method and the current integration method, a Kalman filter can be used. The battery can be modeled with an electrical model which the Kalman filter will use to predict the over-voltage, due to the current. In combination with coulomb counting, it can make an accurate estimation of the state of charge. The strength of a Kalman filter is that it is able to adjust its trust of the battery voltage and coulomb counting in real time.[18][19]

Pressure method

This method can be used with certain NiMH batteries, whose internal pressure increases rapidly when the battery is charged. More commonly, a pressure switch indicates if the battery is fully charged. This method may be improved by taking into account Peukert's law which is a function of charge/discharge current.


See also

References

  1. ↑ 1.0 1.1 1.2 Hassini, Marwan; Redondo-Iglesias, Eduardo; Venet, Pascal (2023-07-19). "Lithium–Ion Battery Data: From Production to Prediction" (in en). Batteries 9 (7): 385. doi:10.3390/batteries9070385. ISSN 2313-0105. 
  2. ↑ "Investigating the Optimal DOD and Battery Technology for Hybrid Energy Generation Models in Cement Industry Using HOMER Pro | IEEE Journals & Magazine | IEEE Xplore". doi:10.1109/ACCESS.2023.3300228. https://ieeexplore.ieee.org/document/10197407. 
  3. ↑ Saxena, Saurabh; Hendricks, Christopher; Pecht, Michael (2016-09-30). "Cycle life testing and modeling of graphite/LiCoO2 cells under different state of charge ranges". Journal of Power Sources 327: 394–400. doi:10.1016/j.jpowsour.2016.07.057. ISSN 0378-7753. https://www.sciencedirect.com/science/article/pii/S0378775316309247. 
  4. ↑ Espedal, Ingvild B.; Jinasena, Asanthi; Burheim, Odne S.; Lamb, Jacob J. (4 June 2021). "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles" (in en). Energies 14 (11): 3284. doi:10.3390/en14113284. ISSN 1996-1073. 
  5. ↑ Xia, Bizhong; Zhang, Guanyong; Chen, Huiyuan; Li, Yuheng; Yu, Zhuojun; Chen, Yunchao (January 2022). "Verification Platform of SOC Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles" (in en). Energies 15 (9): 3221. doi:10.3390/en15093221. ISSN 1996-1073. 
  6. ↑ Chowdhury, Nildari Roy; Smith, Alexander J.; Frenander, Kristian; Mikheenkova, Anastasiia; Lindström, Rakel Wreland; Thiringer, Torbjörn (2024-01-15). "Influence of state of charge window on the degradation of Tesla lithium-ion battery cells". Journal of Energy Storage 76: 110001. doi:10.1016/j.est.2023.110001. ISSN 2352-152X. https://www.sciencedirect.com/science/article/pii/S2352152X2303400X. 
  7. ↑ Grolleau, Sébastien; Baghdadi, Issam; Gyan, Philippe; Ben-Marzouk, Mohamed; Duclaud, François (2016-06-24). "Capacity Fade of Lithium-Ion Batteries upon Mixed Calendar/Cycling Aging Protocol" (in en). World Electric Vehicle Journal 8 (2): 339–349. doi:10.3390/wevj8020339. ISSN 2032-6653. 
  8. ↑ Redondo-Iglesias, Eduardo; Venet, Pascal; Pelissier, Serge (19 February 2020). "Modelling Lithium-Ion Battery Ageing in Electric Vehicle Applications—Calendar and Cycling Ageing Combination Effects" (in en). Batteries 6 (1): 14. doi:10.3390/batteries6010014. ISSN 2313-0105. 
  9. ↑ Wikner, Evelina; Björklund, Erik; Fridner, Johan; Brandell, Daniel; Thiringer, Torbjörn (2021-04-01). "How the utilised SOC window in commercial Li-ion pouch cells influence battery ageing". Journal of Power Sources Advances 8: 100054. doi:10.1016/j.powera.2021.100054. ISSN 2666-2485. https://www.sciencedirect.com/science/article/pii/S2666248521000093. 
  10. ↑ Seyed Mohammad Rezvanizaniani; Jay Lee; Zongchung Liu; Yan Chen (2014). "Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility". Journal of Power Sources 256: 110–124. doi:10.1016/j.jpowsour.2014.01.085. Bibcode: 2014JPS...256..110R. 
  11. ↑ "Battery State of Charge Determination". https://www.mpoweruk.com/soc.htm. 
  12. ↑ "Meters and battery testers". http://www.amperis.com/en/resources/articles/meters-battery-testers/. 
  13. ↑ S Accetta, Joseph. "Applications of Refractometry in Battery State-of-Charge (SOC) Measurements". https://www.jsaphotonics.com/_files/ugd/c44978_5b0e9ab9863847f4978a517b9195385b.pdf. 
  14. ↑ Patil, Supriya S.; Labade, V. P.; Kulkarni, N. M.; Shaligram, A. D. (2013-11-01). "Analysis of refractometric fiber optic state-of-charge (SOC) monitoring sensor for lead acid battery". Optik 124 (22): 5687–5691. doi:10.1016/j.ijleo.2013.04.031. ISSN 0030-4026. Bibcode: 2013Optik.124.5687P. https://www.sciencedirect.com/science/article/pii/S0030402613005172. 
  15. ↑ Fuller, Brian. "Live blogging Maxim's editor-analyst day". https://www.eetimes.com/author.asp?section_id=36&doc_id=1284601. 
  16. ↑ http://www.analog-eetimes.com/en/high-accuracy-battery-fuel-gauge-maximizes-battery-capacity-and-boosts-user-confidence.html?cmp_id=7&news_id=222904749
  17. ↑ "Power Profiles for Android". https://source.android.com/devices/tech/power. 
  18. ↑ Zhang, J. and Lee, J., A review on prognostics and health monitoring of Li-ion battery [1].
  19. ↑ Wei, He; Nicholas Williard; Chaochao Chen; Michael Pecht (2013). "State of charge estimation for electric vehicle batteries using unscented kalman filtering". Microelectronics Reliability 53 (6): 840–847. doi:10.1016/j.microrel.2012.11.010. 



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