Introduction

In the last blog, we delved into the introduction to a Battery Management System (BMS) and explained its role in managing and protecting battery packs. We discussed the importance of accurate estimations of battery parameters such as State of Charge (SoC), State of Health (SoH), State of Power (SoP), and Distance to Empty (DTE). The blog also highlighted the factors that affect the accuracy of parameter estimations. It emphasized the need for precise estimations to ensure effective battery management and optimal battery performance, as battery packs are the major contributors to the cost of an EV (close to 50% in some cases).

This blog focuses on improving state estimations’ accuracy, specifically SoC for Li-ion battery packs. Accurate SoC estimation is crucial for efficient battery usage, and using Extended Kalman Filters ensures that the estimated SoC is the most accurate.

As the EV sector grows, every BMS manufacturer in India is competing to deliver battery management system for electric vehicle solutions that offer greater precision, efficiency, and lifespan—making SoC estimation a competitive advantage. For foundational information on battery management systems and parameter estimations, refer to Battery Management System & the Need for Accurate Estimations.

Why SoC Estimations Matter in Electric Vehicle Batteries

The SoC tells us how much usable energy remains in a battery pack in electric vehicle systems. For drivers, it’s the equivalent of a fuel gauge—without it, planning trips or charging stops becomes guesswork. For fleet operators, inaccurate SoC readings can disrupt schedules, increase downtime, and raise operational costs.

Inaccurate SoC estimations can lead to:

• Overcharging, which accelerates battery degradation
• Over-discharging, which risks deep-cycle damage
• Poor range prediction, frustrating drivers and operators
• Suboptimal energy use, leading to performance losses

Given that modern lithium battery with BMS setups power everything from city cars to high-performance electric trucks, precision is non-negotiable.

The Extended Kalman Filter: A Game-Changer for SoC Estimation

The Kalman Filter is a mathematical algorithm used to estimate unknown variables by combining predictions from a model with actual measurements. The Extended Kalman Filter (EKF) adapts this principle for nonlinear systems—like BMS in electric vehicle applications—where the relationship between variables isn’t always linear.

In SoC estimation, the EKF works in two phases:

Prediction step:

The prediction phase uses the state estimate from the previous timestep estimate at the current timestep. For instance, if we’re predicting SoC at t=1sec, we use measurements at t = 1 sec and estimates at the previous timestep, let’s say SoC at t = 0.9. Although predictions are made for the current timestep, observations from the current timestep are not included.

x_k = f(x_k-1, u_k-1)

Where:

x_k: Predicted state estimate at time k

f: Process model that describes the system dynamics 

x_k-1: State estimate at time k-1

u_k-1: Control input at time k-1

Correction step:

After the prediction step, the EKF moves on to the correction step. This step compares its prediction with actual measurements of the battery’s voltage, current, and temperature to ensure accuracy. If not, the EKF adjusts its internal parameters based on the error between measurement and estimation to get a more accurate SoC estimate. This correction process helps the EKF adapt to the changing battery behavior and environmental conditions, such as high/low temperature and heavy usage.

x_k = x_k + KG_k(y_k – h(x_k))

Where:

x_k: Corrected state estimate at time k

KG_k: Kalman Gain at time k

y_k: Measurement at time k

h: Measurement model that relates the state to the measurements

This approach allows the EKF to remain accurate even in dynamic conditions—such as rapid acceleration, regenerative braking, or sudden temperature shifts—making it an indispensable tool for EV battery monitoring system accuracy.

For an authoritative technical overview on SoC estimation methods including EKF, see State of Charge Estimation Techniques of Li-ion Batteries.

Why Not Just Use Voltage vs. SoC?

The Voltage vs. SoC method measures a battery’s open-circuit voltage (OCV) to estimate its charge. While simple, it has major limitations:

• Requires the battery to rest for at least 10 minutes before taking an accurate reading.
• Useless for real-time SoC estimation in moving vehicles.
• The OCV curve for many lithium chemistries is flat between 90% and 10% SoC, offering little resolution.

This means a battery management system in electric vehicles relying solely on OCV could misread the SoC by a wide margin in its most common operating range.

Why Extended Kalman Filter?

Voltage vs. SoC

The most basic estimation methods typically use the ‘Voltage vs. SoC plot (V vs. SoC)’ or the ‘Coulomb Counter (CC)’ technique have some elementary flaws. These are mostly due to errors in the measurements of voltages, currents, and the transients of a cell while switching larger currents in and out of the battery pack. The first method (V vs. SoC) depends on the Open Circuit Voltage of a cell, which requires the cell to be resting (dependent on the currents being drawn before the rest period, but at least 10 mins). Therefore, using this method, the SoC can only be estimated when the vehicle is switched OFF for some duration. However, we expect the instantaneous SoC while the vehicle is in motion.

Another critical issue with this method is that the Open Circuit Voltage (OCV) vs SoC plot is mostly flat in the high operation range for the battery. The figure below depicts the OCV vs SoC plot for a typical LFP (Lithium Iron Phosphate) cell. The major voltage drop occurs only between 100-90% and 10-0% SoC, while the plot is mostly flat in the high operation range of 90-10% SoC. This characteristic makes it almost impossible for a BMS to estimate SoC by employing this method.

The Coulomb Counting Method

Coulomb Counting tracks the flow of current into and out of the battery over time, integrating these values to estimate SoC.

Advantages:

• Works in real-time.
• Simple to implement in electric vehicle BMS hardware.

Drawbacks:

• Accumulates small measurement errors over time, leading to drift.
• Sensitive to sensor accuracy and calibration.
• Cannot detect capacity loss due to aging without external correction.

While Coulomb Counting is common in BMS EV designs, it’s often paired with EKF or other algorithms to improve long-term accuracy.

Why the EKF Stands Out

The Extended Kalman Filter combines the strengths of Coulomb Counting and Voltage vs. SoC while mitigating their weaknesses. It uses a battery’s mathematical model—often derived from the Hybrid Pulse Power Characterization (HPPC) test—to continuously refine SoC estimations, even under rapidly changing loads.

For example, when regenerative braking feeds a sudden burst of current back into the battery pack BMS, the EKF can quickly adjust the SoC reading without waiting for voltage stabilization.

This ability to adapt in real time is why many BMS manufacturers in India are integrating EKF algorithms into their battery management system PDF specifications for both passenger and commercial EVs.

Challenges in Implementing EKF

While powerful, EKF implementation isn’t without challenges:

• Requires accurate battery models, which vary by chemistry (LFP, NMC, etc.).
• Needs precise current, voltage, and temperature measurements from high-quality sensors.
• Must be periodically recalibrated as the battery ages and its internal parameters change.

This is why choosing the right BMS manufacturer in India is critical. The best providers combine robust hardware with advanced software algorithms to ensure long-term SoC accuracy.

The Bigger Picture: SoC Estimation in a Complete BMS

In a modern battery management system for electric vehicle, SoC estimations don’t work in isolation. They interact with:

• SoH estimations: to track degradation and adjust range predictions.
• SoP estimations: to ensure safe power delivery during acceleration or hill climbs.
• DTE calculations: to give drivers accurate remaining range data.
• Cell balancing techniques: to maintain voltage uniformity and prevent safety risks.

When integrated into a complete EV battery management system, accurate SoC estimations help optimize charging schedules, prevent deep discharges, and extend the overall life of the lithium battery with BMS.

Learn more about integration with SoH and SoP in our blog “Battery Management System & the Need for Accurate Estimations”.

Future Trends in SoC Estimation

As EV adoption accelerates, SoC estimation methods will evolve further:

• AI-assisted algorithms could use big data from thousands of vehicles to refine predictions.
• Adaptive BMS will adjust estimation models on the fly based on usage patterns.
• Integration with smart charging infrastructure will allow real-time optimization of charging rates based on accurate SoC feedback.

The role of BMS manufacturers in India will be central in adopting these innovations, especially for tailoring algorithms to Indian driving patterns, climate conditions, and infrastructure challenges.

Conclusion

Accurate SoC estimations are the backbone of reliable EV performance. While basic methods like Voltage vs. SoC and Coulomb Counting have their uses, they fall short for the high demands of modern EVs. The Extended Kalman Filter offers a sophisticated, adaptive, and robust approach, ensuring drivers and operators can trust their range and performance data.

By partnering with an experienced BMS manufacturer in India, OEMs can integrate advanced SoC estimation methods into their BMS EV designs, enhancing safety, efficiency, and customer satisfaction.In our next blog, we’ll move from SoC estimations to State of Health (SoH)—exploring how understanding battery health can further improve range predictions, maintenance planning, and total cost of ownership.