1.5v Carbon battery

　　Power monitoring technology for 1.5v Carbon battery applications

　　The biggest challenge facing power management systems is how to extend battery run time. In addition to looking for new power supplies with higher energy density, system designers are also looking for ways to use battery power as efficiently as possible. Most of them focus on improving DC/DC conversion efficiency, thereby extending battery running time, but often ignore the issue of battery fuel gauge accuracy, which is equally important as power conversion efficiency and battery capacity.

　　If the error range of the battery fuel gauge is ±10%, the system can only utilize 90% of the battery power to prevent the loss of critical data. This equates to a loss of 10% of battery capacity or battery run time.

　　Many mobile applications such as wireless access account management, data processing and medical monitoring have high requirements for the accuracy of remaining battery capacity measurement to avoid sudden shutdown due to battery exhaustion. However, ensuring the accuracy of the measurement of remaining energy over the entire battery life cycle, over-temperature conditions or when using a load is difficult and is underestimated by end users and even some system designers. The main reason is that the available electric energy of the battery is functionally related to its discharge speed, operating temperature, aging degree and self-discharge characteristics. Developing an algorithm to accurately define battery self-discharge characteristics and the impact of aging on battery capacity is almost impossible to achieve.

　　Furthermore, traditional battery fuel gauges require the battery to be fully charged and fully discharged to update the battery capacity, which rarely occurs in real-world applications, resulting in greater measurement errors. Therefore, it is difficult to accurately predict the remaining battery capacity and working time during the battery operating cycle. This article will introduce how to use the latest battery power measurement technology-impedance tracking measurement technology to solve the above problems. The article will also list a simple design case of a single-cell lithium-ion battery pack solution.

　　Problems with Existing Fuel Measurement Technology It is a common misconception that reduced lithium-ion battery capacity is the primary cause of reduced battery run time. In fact, the continued increase in battery impedance (rather than a decrease in battery capacity) is the key factor leading to shortened battery runtime and premature system shutdown. During about 100 cycles of battery charge and discharge, the battery capacity only drops by 5%, while the DC impedance of the battery increases by a factor of one or two. The direct result of the increase in the impedance of an aging battery is the increase in the internal voltage drop caused by the load current.

　　As a result, aging batteries reach the system's minimum operating voltage (or termination voltage) much earlier than new batteries. Traditional battery power measurement technology is mainly developed based on voltage and Coulomb counting algorithms, and has obvious limitations in measurement performance.

　　Due to low cost and simple implementation, voltage-based measurement methods are widely used in handheld devices such as mobile phones. However, the battery impedance will change after a period of use, affecting the measurement accuracy of this method. The battery voltage can be obtained from the following formula: where Vocv is the battery open circuit voltage, and RBAT is the battery's internal DC impedance. As can be seen from Figure 1, the voltage of aging batteries is lower than that of new batteries, which will advance the system shutdown time.

　　Changes in load conditions and temperature can reduce the available battery capacity by up to 50%. Most end users have experienced sudden shutdowns due to battery drain when using portable devices that were not equipped with a true fuel gauge.

　　On the other hand, the Coulomb counting method takes another approach: by continuously performing Coulomb integration, the amount of charge consumed and the state of charge (SOC) are calculated, and the entire capacity is known, so the remaining capacity can be obtained value. The disadvantage of this method is that it is difficult to accurately quantify (model) the self-discharge capacity, and because this method does not perform periodic full cycle correction, the measurement error increases over time.

　　None of these algorithms address changes in battery impedance. In order to prevent sudden shutdowns, designers must terminate system operation early to retain more energy, which results in a large amount of wasted power.

　　Dynamic monitoring of battery impedance and chemical capacity Impedance Tracking (IT) technology is unique and more accurate than existing solutions because the technology has a self-learning mechanism that can solve the problem of battery impedance and full chemical capacity (QMAX) under no-load conditions ) changes in aging problems. Impedance tracking technology uses dynamic simulation algorithms to learn and track battery characteristics. That is, during the actual use of the battery, it first measures the impedance and capacity values, and then tracks their changes. Using this algorithm eliminates the need for periodic full cycle capacity corrections.

　　Using knowledge of battery impedance, accurate load and temperature compensation can be achieved. Most importantly, through dynamic learning of battery parameters, this measurement method can accurately measure the power throughout the battery's service life.

　　Impedance tracking technology is superior at measuring remaining battery capacity compared to coulomb counting or battery voltage correlation methods alone. During IT operation, it is necessary to continuously maintain the table database that maintains the functional relationship between battery impedance (RBAT), depth of discharge (DOD), and temperature. Understanding what happens in different states can help determine when these tables need to be updated or used. In the meter, the non-volatile memory stores multiple current thresholds that define states such as charging, discharging, relaxation after charging, and relaxation after discharge.

　　After stopping charging or stopping discharging, "relaxation time" can allow the battery voltage to stabilize. Before turning on the handheld device, the accurate charging status of the battery is determined by measuring the battery open circuit voltage (OCV) and then comparing it with the OCV (DOD, T) table. When the handheld device is active and connected to a load, a coulomb counting algorithm based on current integration begins.

　　The Coulomb counter measures the passing charge and integrates it to continuously calculate the SOC value. The total capacity QMAX can be calculated from two OCV readings when the voltage change of the battery before and after charging or discharging is small enough and the battery is in a fully relaxed state.

　　For example, before the battery is discharged, the SOC can be obtained by the following formula: When the battery is discharged and the passed charge is ΔQ, the SOC can be obtained by the following formula: By subtracting the two formulas: It can be seen from the equation that there is no need to go through full charge and discharge Cycles determine the total battery capacity.

　　This also eliminates the time-consuming battery learning cycle during battery pack production. The RBAT(DOD,T) table is continuously updated during the discharge process. IT uses this table to calculate when the termination voltage is reached under current load and temperature conditions.

　　The overall battery impedance increases as the battery ages and the charge and discharge cycles increase. The impedance is given by: With the battery impedance information, the remaining capacity (RM) can be determined using a voltage simulation algorithm contained (in the firmware) in the program instructions in the read-only memory. The simulation algorithm first calculates the current SOCstart value, and then calculates the future battery voltage value when the load current is the same and the SOC value continues to decrease.

　　When the simulated battery voltage VBAT (SOCI, T) reaches the battery termination voltage (typical value is 3.OV), obtain the SOC value corresponding to this voltage and record it as SOCFINAL.

　　The battery voltage is measured through the BAT2 pin input terminal, and the current is monitored through the coulomb counter differential signal input terminal (SRp and SRN). The system uses the power gauge to obtain information such as SOC and Run-Time-to-Empty from the single-wire SDQ communication port.

　　The IT fuel gauge accurately predicts the remaining battery capacity even under load changes.

　　For example, when a digital camera is in different working modes, the load on the battery is also different. Figure 3 shows how an IT fuel gauge accurately predicts remaining battery power. The error rate of remaining power prediction can be less than 1%. And because the battery impedance and aging effects used to predict remaining power are updated in real time, this small error is maintained throughout the battery's lifetime.

　　The impedance tracking battery power monitor combines the advantages of the Coulomb counting algorithm and the voltage-related algorithm to achieve higher battery power monitoring accuracy.

　　Accurate SOC values can be obtained by measuring OCV in a relaxed state. Since all self-discharge activity is reflected in the battery OCV reduction process, self-discharge correction is not required. When the device is in active mode and a load is connected, the coulomb counting algorithm based on current integration begins. Battery impedance is updated via real-time measurements.

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