“Battery packs based on lithium-ion (Li-ion) battery cells are widely used in various applications such as hybrid electric vehicles (HEV), electric vehicles (EV), storage of regenerative energy for later use, and for various purposes ( grid stability, peak shaving and time-shifting of renewable energy, etc.) for grid energy storage. This article will introduce you to the technical development of measuring the state of charge (SOC) and state of operation (SOH) of battery cells, as well as the related solutions introduced by ADI.
Battery packs based on lithium-ion (Li-ion) battery cells are widely used in various applications such as hybrid electric vehicles (HEV), electric vehicles (EV), storage of regenerative energy for later use, and for various purposes ( grid stability, peak shaving and time-shifting of renewable energy, etc.) for grid energy storage. This article will introduce you to the technical development of measuring the state of charge (SOC) and state of operation (SOH) of battery cells, as well as the related solutions introduced by ADI.
Precise estimation of battery SOC can prevent battery overcharging and discharging
Measuring the state of charge (SOC) of a battery cell is very important in electric vehicle and energy storage system applications. SOC is defined as available capacity (in Ah), expressed as a percentage of rated capacity. The SOC parameter can be regarded as a thermodynamic quantity, which can be used to evaluate the potential electric energy of the battery. It is also important to estimate the battery’s state of health (SOH), which measures a battery’s ability to store and deliver electrical energy against new batteries.
However, determining the battery SOC is a complex task, which is related to the battery type and its application, so many development and research efforts have been carried out in recent years to improve the accuracy of SOC estimation. Accurately estimating the SOC is one of the main tasks of a battery management system, which helps to improve system performance and reliability, and also prolongs battery life.
In fact, a precise estimate of the battery SOC can avoid unexpected system interruptions and prevent the battery from overcharging and discharging (which could lead to permanent battery damage, depending on the internal structure of the battery). However, battery charging and discharging involves complex chemical and physical processes, and it is not trivial to estimate SOC accurately under different operating conditions.
The general approach to measuring SOC is to measure very accurately the amount of charge (coulombs) and current flowing into and out of the battery pack under all operating conditions, as well as the voltage at each cell in the battery pack, and then use this data and a previously loaded The same battery pack data yields an accurate estimate of SOC. Other data required for this calculation include battery temperature, battery mode (whether the battery is charging or discharging when the measurement is made), battery age, and other relevant battery data obtained from the battery manufacturer.
Data on the performance characteristics of lithium-ion batteries under different operating conditions is sometimes available from the manufacturer. Once the SOC is determined, it is the system’s responsibility to update the SOC on subsequent runs, basically counting the amount of charge (coulombs) flowing into and out of the battery. The accuracy of this method may not be satisfactory if the accuracy of the initial SOC is not high enough, or is affected by other factors, such as battery self-discharge and leakage effects.
Equivalent circuit model of a Li-ion battery pack
Evaluation platform measures SOC and SOH of typical energy storage modules
In order to measure the SOC and SOH of typical energy storage modules, the design and development of a coulomb counting evaluation platform is involved. The evaluation platform mainly consists of the following parts: hardware system, including MCU and required interfaces and peripherals, embedded software, which can be used for SOC and SOH algorithm implementation, and PC-based application software, which is used as user interface for system configuration, Data Display and analysis.
The evaluation platform periodically measures the voltage value of each battery cell, as well as the current and voltage of the battery pack through appropriate ADCs and sensors, and runs the SOC estimation algorithm in real time. This algorithm uses measured voltage and current values, collected by temperature sensors and/or some other data provided by the PC software program (such as manufacturer specifications from a database). The output of the SOC estimation algorithm will be sent to the PC GUI for dynamic Display and database update. SOC and SOH estimation mainly use three methods, including coulomb counting method, voltage method and Kalman filter method. These approaches are applicable to all battery systems, especially hybrid electric vehicle (HEV), electric vehicle (EV) and photovoltaic (PV) applications.
Coulomb counting, also known as ampere-hour counting and current integration, is the most common technique used to calculate SOC. This method calculates the SOC value by integrating the battery current reading with the usage time. Coulomb counting calculates remaining capacity by accumulating charge into or out of the battery. The accuracy of this method mainly depends on the precise measurement of the battery current and the accurate estimation of the initial SOC. Using a predicted capacity (either stored in memory or initially estimated from operating conditions), the SOC of the battery can be calculated by integrating the charge and discharge currents with respect to the run time.
The voltage law is that the SOC of a battery (ie its remaining capacity) can be determined using a discharge test under controlled conditions. The voltage method uses the battery’s known discharge curve (voltage vs. SOC) to convert the battery voltage reading to an equivalent SOC value. However, the impact of battery current on voltage is more severe due to the electrochemical kinetics and temperature of the battery. Compensating the voltage reading with a correction term proportional to the battery current and using a lookup table of the battery’s open-circuit voltage (OCV) versus temperature can make this method more accurate.
The Kalman filter is an algorithm that can estimate the internal state of any dynamic system and can also be used to estimate battery SOC. In Contrast to other estimation methods, the Kalman filter automatically provides a dynamic error bound on its own state estimate. By modeling the battery system to include the required unknowns (such as SOC) in its state description, the Kalman filter estimates its value and gives an error bound for the estimate. It then becomes a model-based state estimation technique that utilizes error correction mechanisms to provide real-time predictions of SOC.
Kalman filter principle
Choosing the Appropriate SOC and SOH Estimation Method
Several criteria should be considered when selecting an appropriate SOC estimation method. First, SOC and SOH estimation techniques should be available for HEV and EV applications, renewable energy storage for later use, and Li-ion batteries for grid energy storage. It is also critical that the method chosen should be an online, real-time technique with low computational complexity and high accuracy (low estimation error). It is also required that the estimation method use voltage, current measurements, and other data collected by the temperature sensor and/or provided by the PC software program.
In order to overcome the shortcomings of the coulomb counting method and improve its estimation accuracy, an enhanced coulomb counting algorithm has been proposed to estimate the SOC and SOH parameters of lithium-ion batteries. The initial SOC is obtained from the applied voltage (charging and discharging) or the open circuit voltage. Losses are compensated by considering charging and discharging efficiencies. By dynamically recalibrating the maximum releasable capacity of the working battery, the SOH of the battery can also be estimated at the same time, which will further improve the accuracy of SOC estimation.
The battery has three working modes: charge, discharge and open circuit. During the charging phase, when the battery is charged in constant current constant voltage (CC-CV) mode, the manufacturer usually accounts for the changes in the battery voltage and current. With a constant charge current, the battery voltage gradually increases until it reaches a threshold. Once the battery is charged in constant voltage mode, the charge current decreases rapidly at first and then decreases slowly. Finally, when the battery is fully charged, the charge current tends to zero. This charging curve can be converted into the relationship between SOC and charging voltage in the constant current stage, and can be converted into the relationship between SOC and charging current in the constant voltage stage. The initial SOC during charging can be calculated from these relationships.
During the discharge phase, typical voltage curves for batteries discharged at different currents are given by the manufacturer. As the operating time goes by, the terminal voltage will decrease. The larger the current, the faster the terminal voltage drops, so the shorter the working time. In this way, the relationship between SOC and discharge voltage at different currents can be obtained, and then the initial SOC in the discharge stage can be deduced.
The open phase requires a relationship between OCV and SOC. Before disconnecting the load, the battery is discharged at different currents. If the rest time is long, OCV can be used to estimate SOC. The operating efficiency of a battery can be assessed by Coulombic efficiency, which is defined as the ratio of the amount of charge that can be drawn from the battery during discharge to the amount of charge that enters the battery during charge.
Wired Battery Management System (BMS)
Diverse solutions to meet battery monitoring needs
In order to solve various battery monitoring problems, ADI has also launched a variety of product solutions, including the ADBMS6815 multi-cell battery stack monitor, which can measure up to 12 battery cells in series with a total measurement error (TME) of less than 1.5 mV. The ADBMS6815 has a battery measurement range of 0 V to 5 V, suitable for most battery chemistry applications. All 12 cells can be measured in 304 μs, with a lower data acquisition rate selected for noise reduction.
In addition, multiple ADBMS6815 devices can be connected in series to simultaneously monitor very long high voltage battery strings. Each ADBMS6815 has an isoSPI™ interface for high-speed communication over long distances free from RF interference. Multiple devices are connected in a daisy chain, with the topmost or bottommost device connecting to the main processor. The daisy chain is bi-directional, ensuring communication integrity even if the communication path fails.
The battery stack can power the ADBMS6815 directly, or it can be powered by an isolated power supply. The ADBMS6815 includes passive balancing for each cell, allowing individual pulse width modulation (PWM) duty cycle control for each cell. Other features include an on-board 5 V regulator, seven general-purpose input/output (GPIO) lines, and a sleep mode that reduces current consumption to 5.5 µA. The ADBMS6815WFS model is designed for ISO 26262 applications up to Automotive Safety Integrity Level D (ASIL D).
On the other hand, ADI also introduced the LTC2949, a high-precision current, voltage, temperature, and power monitor for electric vehicles and hybrid vehicles and other isolated current sensing applications. By simultaneously monitoring the voltage drop across up to two sense resistors and the battery pack voltage, it can infer the charge and energy flowing into and out of the battery pack.
In addition, the LTC6820 of the isoSPI isolated communication interface introduced by ADI can provide bidirectional SPI communication between two isolated devices through a single twisted pair connection. Each LTC6820 encodes a logic state into a signal and transmits the signal across an isolation barrier to another LTC6820. The receiving LTC6820 decodes the transmitted signal and drives the slave bus to the appropriate logic state. The isolation barrier can be bridged with a simple pulse transformer to achieve isolation of several hundred volts.
Whether it is an electric vehicle or an energy storage system application, the operating efficiency of the battery is an important key to improving the performance of related products. By monitoring the SoC and SoH status of the battery, it will ensure that the battery operates in a high-efficiency and stable manner. ADI’s related solutions for battery monitoring applications will improve the performance and safety of battery operation. For more related technology and product details, please contact ADI or Arrow Electronics for more detailed information.
The Links: SKKQ1500/18E PM100RSE060