Lead Acid Research Papers

On-line measurement of cell impedance during charging and discharging process

2024, Journal of Electroanalytical Chemistry

The paper describes the principles of cell impedance measurement method, which, unlike other impedance techniques used for cell examination, operates on-line. In order to present the capabilities of the proposed method, experimental... more

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Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

by Francisco Javier Asensio

2024, Energies

Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for... more

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Controlador Difuso Inteligente para un Cargador de Baterías de Plomo-ácido

by Cesar Abraham Carranco Rodriguez

2023, Ingeniería

During the last ten years, Fuzzy Control has become the most popular method for controlling non-linear processes, due to its implementation simplicity, robustness and lack of a rigurous mathematic presentation. A fuzzy controller for a... more

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Ultrasonic interrogation of AGM-VRLA batteries

by Angel Kirchev

2023

International audienceThis work propose a simple, non-destructive and non-intrusive characterization technique, applied to lead acid batteries for battery monitoring during operation. It consists in installing two piezoelectric... more

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Choosing communication protocols , microcontrollers and batteries in the use of a BMS

by Miguel Ángel Mamani Rodríguez

2023

Traction applications in automotive have traditionally used lead acid batteries. However, the situation is changing as new battery chemistries and supporting technologies have brought with them new technical and economic benefits, making... more

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Choosing communication protocols , microcontrollers and batteries in the use of a BMS

by Miguel Ángel Domínguez Rodríguez

2023

Traction applications in automotive have traditionally used lead acid batteries. However, the situation is changing as new battery chemistries and supporting technologies have brought with them new technical and economic benefits, making... more

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Development of a battery energy loss observer based on improved equivalent circuit modelling

by Ahmed Fares

2023, 2016 18th European Conference on Power Electronics and Applications (EPE'16 ECCE Europe)

The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing... more

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On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model

by Raja Sha

2022, Applied Energy

On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model. Applied Energy, 204. pp. 497-508.

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Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

by JOSÉ IGNACIO SAN MARTÍN DÍAZ

2022, Energies

Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for... more

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Analysis of the Current Electric Battery Models for Electric Vehicle Simulation

by Jose Ignacio San Martín

2022, Energies

Electric vehicles (EVs) are a promising technology to reduce emissions, but its development enormously depends on the technology used in batteries. Nowadays, batteries based on lithium-ion (Li-Ion) seems to be the most suitable for... more

Figure 1. Lithium-ion (Li-Ion) technology comparison. (a) LCO; (b) LMO; (c) LFP; (d) NMC; (e) NCA; (f) LTO. to the cathode chemical composition. Figure 1 shows a comparative summary of the best-known lithium ion batteries.

Figure 17. An impedance model of Li-Ion battery. The analytical expression is as follows:

Figure 18. Nyquist plot of electrochemical impedance spectroscopy (EIS) measurements on (a) different temperatures; for each temperature, the 1 Hz point is marked; (b) different SoC state; (c) several ages.

Runtime-combined models are generally composed by two sub-circuits connected to each other. Generally, a runtime model combined with a Thevenin one is widely used. In Figure 20, a typical circuit based on the third-order Thevenin is shown. In the first sub-circuit, Reeip-dis is a self-disc he charge stored in battery, and I(t) is a current lowing in sub-circuit 2. The first sub-circuit is d harge resistance, Ccqy is a capacitor representing controlled current source, measuring the current esigned for energetic considerations, such as SoC neasurement, remaining capacity, or self-discharge ratio. The second sub-circuit is composed of a hird-order Thevenin model but replacing the vo tage source by a voltage-controlled voltage source, neasuring voltage (SoC-dependent) from the firs imulating I-V performance. sub-circuit. The second sub-circuit is designed for

Figure 3. Simple or linear battery model.

Figure 4. R;,; variation in NMC with (a) state of charge (SoC); (b) state of health (SoH); and (c) temperature. concentration. Resistance is constant too, independent from SoC or temperature. In this sense, it has to be noted that, in a real battery, the resistance is highly dependent on battery type, SoC and SoH state, and temperature (Figure 4 [18,19]). Generally, resistance increases when SoC lowers (Figure 4a), SoH lowers (degradation increases) (Figure 4b), and temperature lowers (Figure 4c). Applicability of this model is restricted to studies where the battery operates at the middle range of SoC, where the internal resistance and temperature are almost constant. At low SoC, however, resistance varies too much. Available energy to be released, that is, capacity, cannot be depicted, and it is supposed to be unlimited [20]. The most common application is the feeding of power devices as converters or inverters [21]. oe ae ae ee es ee ae aa % a 4 € wi %

Figure 5. Simple battery model considering charging and discharging resistances.

Figure 6. Simple battery model considering power fade (PF). Figure 6 shows the enhanced simple battery model, which considers the effect of the SoC in the resistance. 2.1.3. Enhanced Simple Battery Model

Figure 7. Voc variation with SoC in NMC [28]

Figure 8. Voltage sources-based model. The voltage sources-based model is based on the connection of several voltage sources, which represent different phenomena. The general scheme for this model is shown in Figure 8.

Figure 9. Resistor-capacitor (RC) or dynamic model. This model includes a capacitor Cg, which represents the stored capacity, a series resistance Rg, which represents the propagation effect, a capacitor Cp, and a current dependent resistance Rp, which represent the polarization and diffusion effects, respectively, and an internal resistance Rjy;. The value of Cp is very small, while the value of Cg usually takes very large values. Generally, the self-discharge resistance is neglected in Li-Ion batteries [31,32]. SoC value is represented in the voltage variation through the capacitor Crp. The equations that govern its operation are: 2.1.5. Resistor-Capacitor (RC) or Dynamic Model

Figure 10. (First-order) Thevenin model. The simplest Thevenin model, commonly called first order or one time constant (OTC) [17] ; composed by a voltage source Voc, an internal resistance Rj, and a RC pair (R; and C7) representing 1e capacitance effect between two parallel plates and the contact resistance. This model is shown ir igure 10. The aim of adding a RC pair to the simple linear model is to represent transient phenomena. The main drawback of the Thevenin model is that all the parameters are considered to be constant. However, it is known that parameters are dependent on SoC, C-Rate, temperature, SoH, etc.

Figure 11. Second-order Thevenin model. The second-order model, two time constants (TTC), or dual polarization model, adds a second RC pair (Ry and C2) with a larger time constant (Figure 11) to the previous model. Thus, it is possible ‘o accurately represent the terminal voltage when the current is zero, which was not possible for the OTC [17]. Therefore, the first RC pair has a low time constant for describing short-term transient effects, while the second RC pair has a larger time constant for describing long-term transient effects. These transient effects are related to electrochemical and concentration polarization effects, including charge transfer effect, diffusion, and other factors. Therefore, the first RC pair has a low time constant for describing short-term transient effects, 2.2.2. Second-Order Thevenin Model

Figure 12. Third-order Thevenin model. Terminal voltage Vr is given by:

Figure 13. (First-order) partnership for a new generation of vehicles (PNGV) model. A partnership for a new generation of vehicles (PNGV), composed of a cooperative research program between U.S. government and the three major domestic auto corporations (DaimlerChrysler, Ford, and General Motors), proposed the PNGV model, which is shown in Figure 13.

Figure 14. Second-order PNGV model. In this model, Ry and C, represent polarization effects by concentration, as in classic PNGV, but R, and C, are added to represent polarization effects by activation. The general equation that governs its operation is: 2.3.2. Second-Order PNGV Model

Figure 15. Noshin’s model. Parameters of the Thevenin and PNGV models are obtained by a hybrid pulse power characterization (HPPC) test [58] and, generally, making several assumptions, such as same charging and discharging resistances, or same charging and standing resistances. However, these resistances do vary in a real battery, and therefore, it may be necessary to consider all them to obtain a high accuracy model. Figure 15 shows the Noshin’s model electrical scheme.

Figure 16. Randle’s circuit and its approximation. The internal resistance R;,; used to represent the electric conductivity of the electrolyte, separator, and electrodes, matches with horizontal displacement, that is, where the curve meets the x-axis. The Zarc impedance element is composed of a parallel association of the charge transfer resistance R, and the double-layer capacitance Cy, which represent the activation polarization voltage drop, and is graphed as a semicircle, while Zw is a specific CPE, which models the diffusion effects, and is graphed as a line with 45-degree slope at very low frequencies [59]. These parameters are shown in Figure 17, in a commonly used circuit in these studies, and its Nyquist diagram with its corresponding physical interpretation.

Figure 21. Li-Ion voltage and current in a cycle. Starting from SoCg, an instantaneous voltage drop Vog-, happens when the discharge is started due to Rj; (Electrolyte resistance majorly). Then, voltage goes on decreasing exponentially, due to a combination of RC pairs (diffusion and surface reactions) and SoC decrease in the voltage source. Voltage drop Vogch is recovered instantaneously when current falls to zero, and due to RC pairs, voltage needs some time to be stabilized in a new SoC state, SoC;. When the charging state starts, an instantaneous voltage increase Vo-y¢ happens due to Rin, followed by an exponential increase due to RC pairs and SoC variation in voltage source. Finally, when the charging stops, an instantaneous voltage drop happens due to R;,;, and an exponential decrease due to RC pairs, until final SoC is reached, SoCy. Thevenin and PNGV models are more accurate when RC pairs are increased and can model these effects. However, when using an ideal model with SoC consideration, as it can be seen in Figure 21, Voc graphed in purple only considers part of the dynamic: Voltage variation with SoC variation. The enhanced simple model with SoC consideration is more accurate, since it considers the instantaneous voltage drops too, graphed in green in Figure 21. Therefore, a linear model cannot be considered for SoC direct measurement, as it does not consider SoC in the voltage variation [17] Consequently, Thevenin and PNGV models are best considered for most applications. Based on

* E: Empirical, H: High, L: Low, M: Medium, P: Physical, SE: Semi-Empirical, VH: Very High. Table 1. General classification of battery models.

Table 2. Batteries electrical model classification. This paper is organized as follows: Section 2 analyzes several electrical models currently applied to Li-Ion batteries within electromobility. These models are subcategorized as simple models, Thevenin models, PNGV models, and Noshin model, arranged from the simplest, which considers only ideal elements, to the most complex, which could be a third-order model or a model considering a large number of elements. Section 3 explains impedance models, which can also be useful for other models’ parameter definition. Section 4 introduces runtime models, and V-I performance of the models is explained in Section 5. Finally, conclusions are shown in Section 6.

CF: Capacity fade, ECM: Equivalent circuit model; Freq: Frequency, Hyst: Hysteresis, ID: lon-diffusion, Pol: Polarization, Prop: Propagation, RT: Runtime, SoC: State of charge, SoF: State of function, SoH: State of health, T: Temperature, Thev: Thevenin. Table 4. Cont. Power variation studies, so-called power fade (PF), and capacity variation studies, so-called CF are within the most usual and interesting current applications of bat tery models in electromobility These studies represent the capacity or power variation when a battery is aged. It is known that average current, temperature, DoD, and average SoC are most influent varia bles in battery degradation by cycling [93]. However, an adequate model is required to know the performance differences, as can be a runtime-combined model with a first-order Thevenin considering hys or Noshin model. This model should not only consider the current and the temperature in its parameters (Rj, Voc, etc.). Therefore, it would teresis, second-order Thevenin the SoC, but also the SoH and be possible to predict a battery performance when it is degraded by the use and charging events. Indeed, charging events can be optimized if degradation is foreseen.

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Modeling of the charge acceptance of lead–acid batteries

by Julia Kowal

2022, Journal of Power Sources

This paper presents a model for flooded and VRLA batteries that is parameterized by impedance spectroscopy and includes the overcharging effects to allow charge-acceptance simulations (e.g. for regenerative-braking drive-cycle profiles).... more

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An Op Amp transfer circuit to measure voltages in battery strings

by Thomas Stuart

2022, Journal of Power Sources

To measure the individual voltages in a series connected battery pack, each measurement must be transferred to a common reference level. A transconductance amplifier using an operational amplifier (Op Amp) provides a convenient circuit... more

Continuing research has produced an alternative circuit, shown in Fig. 4, that simplifies the component matching requirements by using an operational amplifier (Op Amp) in place of discrete transistors. This circuit has already proven to be effective for lower voltage segments below 5 V dc, which is satisfactory for applications, such as lithium ion (Li-ion) batteries which are typically <4 V dc. However, care must be taken to avoid certain leakage current paths that exist while the circuit is supposed to be inactive. The Reference [13] proposed the transfer circuit in Fig. 3 to perform the required voltage shifting for measuring the segment voltages in a series battery pack. As stated earlier,

Fig. 1. Voltage measurement system for a series battery pack.

this circuit has certain advantages over other methods, but it does require the matching of several discrete components, as discussed in [13]. The bipolar transistor circuits are inactive until Qs, is turned on, which provides a path for ig, and igo. If Vep, cancels Vepo and Vp; cancels Vp (R, is selected such that 1; © ico),

Fig. 4. Basic transfer circuit using Op Amps.

Fig. 5. Transfer circuit with revisions to avoid leakage and gate breakdown. Although, these changes eliminate the leakage, the value of Vsg in the off state is variable and can drift above the breakdown value of Q;. Therefore, the D1 zeners are added to protect these gates. Naturally, the Q; breakdown problem can be avoided by replacing the Q, FETs with BJT2 as in Fig. 6. However, in the off state, the forward biased emitter base junction of Q, provides a leakage path through R,, back Th e basic circuit in Fig. 4 works quite well while the circuit is active, but it must be revised to avoid certain prob ems while it is off. These revisions are included in Fig. 5. The first of these problems is leakage current drawn from off. T smal cified the battery pack while the circuit is supposed to be he supply current drawn by the Op Amps is typically in comparison to the battery ratings, e.g., the spe- maximum value for the LM224 is 3 madc [14]. However, even this low value can add up to a significant loss i f the system remains in storage for several weeks, e.g.,

Fig. 6. Transfer circuit using BJTs for Q, transistors.

Fig. 9. Measured voltage error vs. temperature for V; = 1.970 V dc in Fig. 7. Fig. 8. Measured voltage vs. temperature for V; = 4.082 V dc in Fig. 7.

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On-line measurement of cell impedance during charging and discharging process

by Kazimierz Darowicki

2022, Journal of Electroanalytical Chemistry

The paper describes the principles of cell impedance measurement method, which, unlike other impedance techniques used for cell examination, operates on-line. In order to present the capabilities of the proposed method, experimental... more

Fig. 2. Block diagram of the meter circuit. Interpretation of impedance data of full cell or batteries might be difficult if characteristics of positive and negative electrodes separately are not given [3,16]. Description of such system during cyclic charge/discharge would exceed this research purpose, which goal is to present methodology of technique. Therefore, lead anode in 4.8 M H2SO, solution was chosen to represent performance of the technique. During the experiment we measured the impedance using the described technique. The proposed meter circuit is pre- sented in Fig. 2. It was composed of the examined lead plate 2 x 1cm (working area 4cm7) and the reference electrode Pb/ PbSO, with the auxiliary platinum sheet. Switching Ig. between 10 mA charging and —10 mA discharging current was realized with the use of AUTOLAB PGSTAT30. The polarization was changed 10 times after the potential of the examined electrode vs the reference electrode reached 1.87 V and 1.33 V, respectively. PGSTAT30 was also used for registering Ig- and Ege. The ac current I,,, composed

Fig. 3. Potential variations registered while cyclic charging and discharging of the electrode.

Fig. 5. Cdl variations obtained during the consecutive cycles of charging.

Fig. 6. Rct variations obtained during the consecutive cycles of charging.

Fig. 4. Impedance spectrogram obtained during the experiment.

Fig. 8. Rct variations obtained during the consecutive cycles of discharging. Fig. 7. Cdl variations obtained during the consecutive cycles of discharging.

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Utilizing turbine ventilator as supplier of electrical energy resources for home information

by I Wayan Supardi

2022, International Journal of Physical Sciences and Engineering (IJPSE)

The power plant has been created with turbine ventilator as a source of electrical energy for lighting. Voltage generator generated when wind speed between 0 m/s up to 6 m/s with no given load is between 0 volts up to 8.38 volts.... more

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An electromechanical transfer circuit to measure individual battery voltages in series packs

by Sukumar Kamalasadan

2022, Journal of Power Sources

A novel approach has been developed to measure the voltages of individual batteries used in electric vehicle (EV) battery packs using a unique selective battery measurement system. This system consists of a voltage measurement circuit... more

Fig. 2. Typical voltage measurement system. Another obvious method is to simply provide each battery with its own isolation amplifier [11]. The isolation amplifier shifts the battery voltages to the common reference, and the accu- racy problems related to resistive dividers are avoided. A typical circuit is shown in Fig. 4, where the individual Vg, can be mul- tiplexed as in Fig. 3. Sample and hold circuits also can be added to both Figs. 3 and 4 to provide a simultaneous measurement scan. This system meets the necessary accuracy requirements, There are several techniques to measure the battery voltages in series packs, the most evident method being using a resis- tive divider. Fig. 3 shows such a circuit for 12 series connected

batteries. The disadvantages for such a system are fairly clear. Firstly, switches must be provided to prevent the resistors from drawing current from the batteries when not in use. Secondly, the voltages near the top of the stack require very accurate (and expensive) divider ratios. For example, consider the top bat- tery in a stack of 12 12 Vac batteries. Here, Kj2 =Ri/(Ri + R12), Ki, =R,/(R, + R11) and so on. If the ideal values of K,; and Kj as defined in Fig. 3 are, Kj, = 1/11 and Kj2=1/12 ages and temperatures of individual batteries, and take corrective action whenever an imbalance arises in the battery pack [9,10]. Fig. | shows a typical modular battery management system that consists of several local modules (LMs) each catering to the need of a well-defined section of the battery pack. In this case, each LM monitors eight batteries and consists of several key components that include a transfer circuit, multiplexer, analog- to-digital (A/D) converter, microcontroller, and an input-output (I/O) buffer. A serial data link provides communication between each LM and a common central module (CM). The CM com- mands all LMs to simultaneously measure and store their battery voltages locally, and the results are then sequentially transmitted to the CM. It is apparent that the transfer circuit is one of the important elements in each LM and thus, needs special attention in design and development. In fact, these voltage measurements can be quite tricky because each measurement must be trans- ferred from the battery pack to the ground reference used by the data processing system as shown in Fig. 2. Although this might seem to be a simplistic task, the system needs to obtain data quickly to prevent a catastrophic condition from occurring. High performance batteries such as lead-acid, lithium-ion and NiMH have higher energy densities, and therefore, they require precise monitoring to ensure safety and performance.

but it is physically large and expensive due to the high cost of the amplifiers. A different method that is commercially available uses localized data processing. Exact details are usually propri- etary, but a typical system can be implemented with a circuit similar to Fig. 5. In this type of system, each battery has its own microcontroller, including an A/D converter and a galvanically isolated serial port. The serial data link provides communication between each microcontroller and a common central processing unit similar to the CM in Fig. 1. Although technically feasible, these systems are still relatively expensive.

Fig. 5. Localized data processing measurement system. This paper looks into the feasibility of using small telecom- munication electromechanical relays in a unique matrix for- mation to acquire voltage measurements from each battery in Research work in this direction evolved from the idea that transfer circuits using both bipolar junction transistors (BJTs) and operational amplifiers (op-amps) [12,13] can be used to measure individual battery voltages in series packs. Garrett and Stuart [12] describes a BJT transfer circuit that provides the required voltage shifting for measuring the segment voltages ina series battery pack. This circuit, however, requires the matching of several discrete components. Here, each voltage measurement can be processed by a microcontroller and the initial tolerances can be reduced considerably by using correction factors in flash memory for each measurement. But temperature induced varia- tions prove to be more difficult to solve. For example Garrett and Stuart [12] presents data showing how temperature variations

‘ihe circuit perrormance was examined over a temperature range of —20 to 40°C [20]. During each temperature test, the circuit was allowed to “soak” for atleast 4h at each tempera- ture level in order to establish thermal equilibrium. The batteries themselves were always kept at 20 °C since only circuit-induced measurement errors were of interest. Table 4 shows the voltage deviations (in mV) from the 20 °C measurements for the —20 to 40 °C temperature range. It can be observed that the fluctuations in voltage measurement were minimal in the 20-40 °C temper- ature range. In fact, the worst case deviation was only +20 mV for battery Bg. The measurement deviations for the rest of the batteries were within +10mV. For temperatures below 20°C, it can be seen that the worst case Ba, Be, and B12 varied only —30mV over the 20 to —20°C temperature range. The devi- ation in the By, Be, and B;2 voltage measurement is shown in Fig. 9 with respect to the initial value at 20 °C. The values on the y-axis are the difference in voltage measurements with respect to the corresponding 20°C voltage measurement. For example, the y-axis values for By are calculated as follows, Fig. 9. B4, Be, and Bj2 results for the —20 to 20°C range.

Logic table for battery selection Table |

Initial battery voltage measurements Table 3

Voltage deviations (in mV) from 20°C measurements at different ambient temperatures memory. These correction factors will have to be updated at specific intervals of time to account for the changing electro- chemical properties of the batteries over time. A procedure to this effect is currently under development.

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Modeling of the charge acceptance of lead�acid batteries

by Dirk Uwe Sauer

2021, Journal of Power Sources

This paper presents a model for flooded and VRLA batteries that is parameterized by impedance spectroscopy and includes the overcharging effects to allow charge-acceptance simulations (e.g. for regenerative-braking drive-cycle profiles).... more

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Tracking battery state-of-charge in a continuous use off-grid electricity system

by Mark Apperley

2021, Itise 2014

The growing importance of batteries in the delivery of primary energy, for example in electric vehicles and isolated off-grid electricity systems, has added weight to the demand for simple and reliable measures of a battery's remaining... more

Figure 2: A typical off-grid installation battery voltage meter. Figure 1: The example of a car fuel gauge, which gives a rough indication of the fuel remaining, sufficient for most purposes. more difficult than that for an electric vehicle, where the battery will normally have been at rest for a period prior to each use of the vehicle.

Figure 3: Neither the inverter/charger nor the solar controller can independently determine the actu battery current, i).

Figure 4: An overview of the Pauaeke off-grid electricity system, showing the two voltages (V,; and V,) and the three currents (1,, I, and I,) measured by the Pentametric monitor.

Figure 5: Detail of power in and out of the Pauaeke batteries over two days, showing the cyclic nature of both consumption and generation. other off-grid system parameters (those on the 230 volt side) which are recorded in addition to the Pentametric outputs described.

Figure 6: Power in and out of the Pauaeke batteries sampled at 10-second intervals over the 12-day perio« 23/07/13 to 03/08/13.

Figure 7: Net energy into the batteries (effectively energy stored) from the data of Figure 6, assuming (a) an overall charge/discharge efficiency (Cer) of 100%, and (b) an efficiency of 80%.

Figure 8: Upward drift of calculated charge level caused by the charge efficiency value, Cer, being too high, and the correction of this drift.

Figure 9: Downward drift of calculated charge level caused by the charge efficiency value, Cer, being too low, and the correction of this drift.

Figure 10: Battery full-charge estimate, E199, proves to be too low when the battery reaches 105% charge and the charging module does not recognize a full-charge state. exactly 100%. Of course, as suggested in the previous paragraph, the battery charge level may continue to rise without a battery full event, in which case there may result a sequence of these recalibrations until charging ceases, or a battery full event does occur.

Figure 11: To ensure an isolated high charge level does not continue to distort SoC calculations, the full-charge estimate, E499, can be reduced by 2% each day until a full-charge estimate too low situation (see Figure 10) occurs. This ensures a reasonably frequent (~ <10 days) recalibration of the Ey99 value.

Figure 12: Algorithm and parameter development based on the 3 week sample data, showing battery stored energy E;, battery maximum capacity E499, and state-of-charge SoC. The overlayed numbers indicate notable points which are described in the text. The operation of the B3SOC algorithm is best explained with reference to the graphs and notabl points of Figure 12. This shows the algorithm in operation, and plots the changes in calculated E (energy stored in the batteries), as well as changes in the estimate of Ejo9 (maximum capacity), and th resulting calculated state-of-charge (SoC) at each data point, over the 21-day sample period.

Figure 13: A comparison of the retrospectively established SoC with the real-time estimate produced by the B3SOC algorithm. The data shown is the percentage point difference between the two values, and the overlaid numbers refer to specific points shown in Figure 12. For completeness, for the initial 5-day settling-in period, the values of the three parameters at the end of that period have been retrospectively applied to the full period.

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Tracking battery state-of-charge in a continuous use off-grid electricity system. Working Paper 04/2013, Department of Computer Science, The University of Waikato

by Mark Apperley

2021

The growing importance of batteries in the delivery of primary energy, for example in electric vehicles and isolated off-grid electricity systems, has added weight to the demand for simple and reliable measures of a battery’s remaining... more

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