Design and Development of System for the Estimation of State of Charge (SOC) of Lithium-ion Battery for Electric Loads
At present, electric vehicles are more and more common. Usually, the main source of energy is the battery. To achieve optimal utilization and protection of batteries, a battery management system (BMS) is used. This BMS prevents batteries from over-discharge and overcharge and provides cell. Accurate
2025-06-28 16:31:26 - Adil Khan
Design and Development of System for the Estimation of State of Charge (SOC) of Lithium-ion Battery for Electric Loads
Project Area of Specialization Electrical/Electronic EngineeringProject SummaryAt present, electric vehicles are more and more common. Usually, the main source of energy is the battery. To achieve optimal utilization and protection of batteries, a battery management system (BMS) is used. This BMS prevents batteries from over-discharge and overcharge and provides cell. Accurate state of charge estimation is necessary for these tasks. There are several methods for the state of charge determination which can be divided as direct measurement methods, book-keeping methods, adaptive methods, and hybrid methods. State of Charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under ?ve groups, such as the conventional method, adaptive ?lter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristics, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Kalman ?ltering (KF) is the most cited technique in which compensating for measurement noise is the main property. Because of the model’s nonlinearity, extended Kalman ?ltering (EKF) is applied wherein the model is linearized around the most recent updated estimate. Here accurate Extended Kalman filter (EKF) algorithm is proposed to estimate the battery nonlinear dynamics. This non-linear filter linearizes the current mean and covariance. Due to its discrete form and ease of implementation, this straightforward approach could be more suitable for real applications. It can accurately demonstrate the characteristics of the lithium-ion battery to show the feasibility and effectiveness of the algorithm for the applications.
Project ObjectivesThe main aim and objective of our project are:
Develop an efficient and accurate system for the battery management system.
- To reduce the mean absolute error (MAE) and root means square error (RMSE) between reference SOC and estimated SOC.
- To enables the reliable and safe operation of Lithium-ion Battery.
- Accurately estimate the state of charge of Lithium-ion battery.
- The Definition of SOC
The SOC of the battery refers to the ratio of the current remaining battery capacity to the available capacity under certain conditions (temperature, charge and discharge ratio, etc.)
- SOC Estimation Methods:
SOC is an important part of BMS. Based on the theoretical and experimental characteristics, various estimation methods are classified into three groups: the traditional estimation algorithm based on experiments, modern methods based on control theory, and other methods based on innovative ideas.
- Direct Measurement
Direct measurement methods refer to some physical battery properties such as the terminal voltage and impedance. Many different direct methods have been employed: open circuit voltage method, terminal voltage method, impedance measurement method, and impedance spectroscopy method.
- Open Circuit Voltage
The Open Circuit Voltage method, also known as the Voltage measurement method, is based on the corresponding relationship between Open Circuit Voltage (OCV) and SOC.
the open-circuit voltage method is always together with other methods at the initial or final stage of charge and rarely used alone, such as the SOC estimation method combining the open-circuit voltage method with the ampere-hour integral method, and the SOC estimation method combining the open-circuit voltage method with the Kalman filtering method.
3. Kalman Filter
The Kalman filter is a recursive predictive filter that is based on the use of state-space techniques and recursive algorithms. It estimates the state of a dynamic system. This dynamic system can be disturbed by some noise, mostly assumed as white noise. To improve the estimated state the Kalman filter uses measurements that are related to the state but disturbed as well.
Thus the Kalman filter consists of two steps
- Predict system status, system output, and error
- Correct the current state estimate value based on the system output value
When the system's state equation is nonlinear, the Kalman filtering method cannot be put into use directly, system equation discretization is required, and this kind of nonlinear equation for the discretization of the Kalman filtering method is called the extended Kalman filtering method (EKF).
The core of EKF was to compare the predicted value and the measurement value, adjusting Kalman gain according to the size of the error, the gain will be used to calculate the next predicted value. The bigger the error, the greater the gain, the bigger the estimated value will be corrected; the lesser the error, the smaller the gain, the lesser the estimated value will be corrected. The Extended Kalman filtering method has good real-time calculation performance.
Benefits of the ProjectBattery state of charge (SoC) estimation is very crucial for the safe operation of electric vehicles (EVs). For practical application, dynamic profiles resembling the EV drive cycle profiles should be considered for SoC estimation of lithium-ion batteries with high accuracy.
State of Charge test determines the battery's state of charge; it does not measure the battery's ability to deliver adequate cranking power. A capacity or heavy-load test measures the battery's ability to deliver current.
Battery Management Systems (BMS) are used to monitor and control battery banks used in many industries and electrical vehicles. With the dominance of Lithium-Ion (Li-Ion) batteries in most energy storage applications, BMS has become the critical enabler, from both a functionality and safety perspective.
Battery Management Systems serve two main industrial segments:
1. Electric Vehicles:
Electric Vehicles, which includes electric cars, trucks, and non-road type vehicles such as golf carts, as well as electric-powered machinery such as forklifts. Typically, battery banks within the forklift, car, and bus, communicate with the Battery Control Module.
2. Grid Power Infrastructure:
Grid Power infrastructure where battery banks are used for backup power or to protect from power fluctuations on the grid. Applications include cell phone towers, A/C power substations, Internet infrastructure equipment, aviation ground support systems, tower communications and weather stations, plus Distributed Energy Resources.
The ability to monitor the physical condition and the state of charge on many batteries throughout a system creates opportunities to improve system performance and reduce the cost to the industries that use them.
Technical Details of Final DeliverableProject key Milestone:
| Elapsed time in (days, or weeks or month or quarter) since the start of the project | Milestone | Deliverable |
| Month 1 | Literature review | Yes |
| Month 2 | Components | Yes |
| Month 3 | Simulation | Yes |
| Month 4 | Hardware | Yes |
| Month 5 | Interfacing | Yes |
| Month 6 | Result | Yes |
| Month 7 | Making database | Yes |
| Month 8 | Complete hardware integration & report binding | Yes |
Project Equipment Details:
| Item name | Type | No. of unit | Per unit cost (In Rs) | Total cost (In Rs) |
| Raspberry Pi 4, | Equipment | 1 | 11,000 | 11,000 |
| ACS 712 Current Sensor | Equipment | 2 | 350 | 700 |
| INA 219 Current Sensor | Equipment | 1 | 650 | 650 |
| Voltage Sensor | Equipment | 1 | 150 | 150 |
| 16x2 LCD | Equipment | 1 | 270 | 270 |
| I2C Module | Equipment | 1 | 180 | 180 |
| ADS 1115 | Equipment | 1 | 450 | 450 |
| 12V, 7Ah Lithium-Ion Battery | Equipment | 1 | 21,150 | 21,150 |
| Connecting& |
Elapsed time in (days, or weeks or month or quarter) since the start of the project
Month 1
Month 2
Month 3
Month 4
Month 5
Month 6
Month 7
Month 8
Item name
Raspberry Pi 4,
ACS 712 Current Sensor
INA 219 Current Sensor
Voltage Sensor
16x2 LCD
I2C Module
ADS 1115
12V, 7Ah Lithium-Ion Battery
Connecting&
Final Deliverable of the Project Hardware SystemCore Industry Energy Other Industries IT , Transportation , Health Core Technology Artificial Intelligence(AI)Other Technologies Blockchain, Clean TechSustainable Development Goals Clean Water and Sanitation, Affordable and Clean Energy, Sustainable Cities and Communities, Responsible Consumption and Production, Climate ActionRequired Resources| Item name | Type | No. of unit | Per unit cost (In Rs) | Total cost (In Rs) |
| Raspberry Pi 4, | Equipment | 1 | 11,000 | 11,000 |
| ACS 712 Current Sensor | Equipment | 2 | 350 | 700 |
| INA 219 Current Sensor | Equipment | 1 | 650 | 650 |
| Voltage Sensor | Equipment | 1 | 150 | 150 |
| 16x2 LCD | Equipment | 1 | 270 | 270 |
| I2C Module | Equipment | 1 | 180 | 180 |
| ADS 1115 | Equipment | 1 | 450 | 450 |
| 12V, 7Ah Lithium-Ion Battery | Equipment | 1 | 21,150 | 21,150 |
| Connecting& |