Introduction:

In the last blog, we explored methods for accurate SoC estimations of the batteries. The Extended Kalman Filter (EKF) has emerged as a powerful algorithm for precise SoC estimation. It adapts to dynamic changes and ensures optimal battery performance. 
In the penultimate installment of the BMS blog Series – ‘‘An Introduction to Battery Management Systems (BMS) and the Art of Accurate Estimations,” we will focus on the methods used to estimate SoH in more detail, uncovering innovative approaches and algorithms that can be utilized for state estimation. This information will help you enhance your understanding of battery management and to grasp the full life of your battery packs.

In India, leading BMS manufacturers in India are developing EV battery management system solutions with advanced SoH estimation algorithms to ensure EV batteries deliver reliable performance in diverse climatic and usage conditions.

For foundational knowledge and context, see What are SOC and SOH of a battery, how to measure them?

SoH Estimation 

State of Health represents the current maximum charge a battery can store compared to its original rated capacity. Over time, chemical reactions, temperature extremes, and usage patterns degrade a battery’s internal structure, lowering its SoH and reducing its effective range and performance.

For example:

• A new battery pack rated at 60 kWh may actually hold 60 kWh of usable energy.
• After years of usage, that same pack might only hold 48 kWh. This means its SoH has dropped to 80%.

An accurate SoH estimation allows a battery management system for electric vehicle to:

• Optimize charging and discharging to reduce further degradation
• Plan timely maintenance or replacement
• Prevent failures and reduce safety risks

For deeper insights on estimation techniques, see State of Health Estimation Methods for Lithium‐Ion Batteries. Explore our internal guide Exploring Methods for Accurate SoC Estimations in Electric Vehicle Batteries.

Why Accurate SoH Estimation Matters

The battery pack in electric vehicle applications is often the most expensive single component, accounting for up to 40–50% of the total vehicle cost. Inaccurate SoH readings can lead to:

• Premature replacement of batteries that still have usable life
• Unexpected failures leading to downtime or safety hazards
• Inefficient warranty planning for OEMs

With an accurate SoH, BMS in electric vehicle designs can balance performance with longevity, reducing the total cost of ownership.

A comprehensive review of battery SoH estimation and impacts is available at Review of battery state estimation methods for electric vehicles

Methods for Estimating SoH

The EV battery management system uses a combination of measurement, modeling, and historical data to determine SoH. Common methods include:

1. Capacity-Based Estimation

Measures the total energy a battery can store compared to its original capacity. Works well for stationary testing but is less practical for real-time vehicle operation.

2. Internal Resistance Measurement

Tracks increases in internal resistance over time—higher resistance means reduced efficiency and SoH. This method is common in EV battery monitoring system designs.

3. Model-Based Estimation

Uses algorithms such as the Extended Kalman Filter or machine learning models to estimate SoH from voltage, current, and temperature data. This approach is widely used by BMS manufacturers in India for real-time applications.

This is why modern battery pack BMS solutions integrate passive cell balancing or active balancing to maintain voltage uniformity and minimize degradation rates. For more information on balancing methods, see Active and Passive Battery Pack Balancing Methods and our blog Cell Balancing in Electronic Devices: Why It Matters & Best Methods.

Methods for Estimating SoH

The EV battery management system uses a combination of measurement, modeling, and historical data to determine SoH. Common methods include:

1. Capacity-Based Estimation
   Measures the total energy a battery can store compared to its original capacity. Works well for stationary testing but is less practical for real-time vehicle operation.
2. Internal Resistance Measurement
   Tracks increases in internal resistance over time—higher resistance means reduced efficiency and SoH. This method is common in EV battery monitoring system designs.
3. Model-Based Estimation
   Uses algorithms such as the Extended Kalman Filter or machine learning models to estimate SoH from voltage, current, and temperature data. This approach is widely used by BMS manufacturers in India for real-time applications.

Technical reviews: A review on state of health estimations and remaining useful life prediction of lithium-ion batteries. A Review of Battery State of Health Estimation Methods.

Key Strategies to Improve SoH

Improving State of Health is not just about monitoring—it’s about proactive measures in BMS EV design and usage practices.

Temperature Management

Maintaining operating temperatures between 20°C and 30°C significantly extends lifespan. A battery thermal management system—with heating and cooling elements—ensures optimal performance in both summer heat and winter cold.

Example: Tesla uses liquid cooling loops in its electric vehicle BMS to maintain even temperature distribution, while Indian OEMs work with BMS manufacturer in India to design air-cooled systems for local cost and climate conditions.

Optimal Charging and Discharging Currents:

Avoid high C-rates for regular use. Charging at 0.5C to 1C is ideal. Fast charging is convenient but accelerates SoH degradation. A car BMS system can limit current dynamically to protect the battery pack.

Appropriate Depth of Discharge and Recharge Levels:

Limiting DoD to between 10% and 90% reduces electrode stress and slows degradation. Some battery management system PDF guidelines recommend even narrower ranges for high-value applications.

Regular Cell Balancing

Evenly balanced cells age more slowly. A lithium battery with BMS will use passive cell balancing or active balancing to equalize voltage levels, reducing weak-cell failures. Learn the technical details at Passive Battery Cell Balancing – Analog Devices.

Internal resource: Battery Analytics in India’s EV Ecosystem: What’s Missing, and How to Fix It.

Cycle Life Calculation Example

Cycle life is directly tied to DoD and usage patterns. For example:
If a battery rated for 1,000 cycles at 80% DoD is used at only 60% DoD, its cycle life could increase to 1,300–1,400 cycles. This means BMS in electric vehicle settings can extend total lifespan simply by limiting DoD.

Formula for Instantaneous Cycle Count:
Instantaneous Cycle Count = (DoD / 80%) * (1 / 2)

Applying this formula helps BMS EV designs predict when maintenance or replacement will be required—critical for fleet operators and OEMs.

Impact of SoH on EV Ownership

For EV owners, State of Health estimation is the difference between confidence and range anxiety. When SoH drops too low, range diminishes noticeably. Accurate readings allow for:

• Maintenance planning before failures occur
• Resale value preservation by proving battery condition
• Warranty claims with verified health data from the EV battery monitoring system

Role of BMS Manufacturers in India

A high-quality SoH estimation depends on both hardware precision and software intelligence. The best BMS manufacturer in India companies are:

• Integrating advanced algorithms into battery management system for electric vehicle designs
• Optimizing battery pack configuration for Indian climates
• Providing adaptive charging profiles based on real-time SoH

By combining these innovations, battery management system in electric vehicles can adapt to varying usage patterns, climates, and energy infrastructure challenges.

Conclusion

Maximizing State of Health is central to extending the lifespan of battery pack in electric vehicle systems. Accurate SoH estimation ensures that EV battery management systems can proactively manage temperature, charging currents, depth of discharge, and cell balancing techniques to slow degradation.

Incorporating advanced algorithms from a trusted BMS manufacturer in India ensures that both OEMs and end-users benefit from longer battery life, improved safety, and reduced total ownership costs.

In the final part of this series, we’ll explore State of Power (SoP) and Distance to Empty (DTE) estimations—two other key pillars of effective BMS EV performance. Stay tuned for actionable insights that will help you get the most from your lithium battery with BMS.