What Are SOH and SOC in a Battery? How Are They Measured and Key Differences?

Quick Read

Two numbers define the practical state of every lithium-ion battery in operation. One tells you what the battery can do right now. The other tells you what the battery has become over time.

State of Charge (SOC) and State of Health (SOH) are the foundational metrics of battery management the numbers that power the range indicator in your EV, determine whether a battery pack qualifies for warranty replacement, guide fleet maintenance decisions, and sit at the core of every BMS algorithm running inside a lithium-ion battery pack.

Understanding what they mean, how they’re measured, and how they interact is essential knowledge for anyone working with battery systems, from EV OEMs and fleet operators to ESS developers and BMS engineers.

What Is SOC in a Battery?

State of Charge is the percentage of usable energy remaining in a battery relative to its current maximum capacity. It is the battery’s fuel gauge that answers the question: how much energy is left right now?

SOC is expressed as a percentage from 0% (fully discharged) to 100% (fully charged). A battery at 60% SOC has 60% of its current maximum capacity available for use.

The critical word is current. SOC is measured against what the battery can hold today, not what it could hold when new. A battery pack aged to 80% SOH has a smaller total capacity than it did when new, but SOC still reports a percentage of that reduced capacity. A 100% SOC in an aged battery contains less absolute energy than a 100% SOC in the same battery when it was new.

SOC full form in battery terminology: State of Charge.

How Does SOC Work and How Is It Measured?

Three primary methods are used to estimate SOC  and in practice, robust BMS implementations combine all three:

Voltage-Based Estimation (OCV Method) Open Circuit Voltage (OCV)  the cell voltage measured after a rest period with no current flowing  has a known relationship to SOC for each battery chemistry. By measuring OCV and looking up the corresponding SOC on a calibrated curve, a rough SOC estimate is possible. The limitation: this only works accurately at rest (no current flow), and LFP’s characteristically flat voltage curve makes the OCV-SOC relationship very weak across most of the SOC range, rendering voltage-based estimation unreliable for LFP chemistry.

Coulomb Counting (Current Integration) The BMS measures current flowing in and out of the battery continuously and integrates it over time to track charge added and removed. SOC formula: SOC(t) = SOC(t₀) + (∫I dt) / Q_nominal, where I is current, t is time, and Q_nominal is the battery’s nominal capacity. Coulomb counting is accurate in the short term but accumulates error over time  any sensor inaccuracy or unmeasured self-discharge introduces drift that compounds with every cycle. The BMS must periodically recalibrate the Coulomb counter against a known reference point (typically full charge or full discharge) to control drift.

Algorithm-Based Estimation (Kalman Filter / Adaptive Methods) Extended Kalman Filters (EKF) and other state estimation algorithms combine voltage measurement, current measurement, and an electrochemical battery model to produce a SOC estimate that is more accurate than either method alone. The algorithm continuously corrects its SOC estimate based on the difference between predicted and measured voltage  compensating for sensor drift, temperature effects, and aging. Modern BMS implementations use EKF or equivalent adaptive algorithms as the primary SOC estimation method, with Coulomb counting as the integration backbone and voltage measurement as the correction signal.

These estimation methods are part of the broader functions handled by a Battery Management System. Learn more about how a BMS monitors, protects, and manages a battery pack in our guide to What is BMS? A Complete Guide to Battery Management Systems.

What Is SOH in a Battery?

State of Health is the measure of a battery’s current condition relative to its original specification  expressed as a percentage from 100% (new, full capacity) declining toward 0% (end of usable life). It answers the question: how much of the battery’s original capability remains?

SOH captures battery aging and the cumulative effect of charge-discharge cycling, temperature exposure, and calendar time on the battery’s capacity, internal resistance, and power delivery capability. A battery at 80% SOH can store and deliver only 80% of what it could when new.

The conventional end-of-life threshold for most EV applications is 70–80% SOH  the point at which capacity loss is perceptible enough to significantly impact range and performance, and below which warranty replacement obligations typically trigger.

How Does SOH Work and How Is It Measured?

SOH is not directly measurable with a single sensor; it must be inferred from observable battery behaviour. Three primary approaches:

Capacity Measurement The most direct SOH method: fully charge the battery, fully discharge it under controlled conditions, and measure the total charge delivered. Compared to the original rated capacity. SOH (%) = (Measured Capacity / Rated Capacity) × 100. Accurate but impractical for real-world deployment  requires controlled full charge/discharge cycles that interrupt operation. Used primarily for periodic maintenance checks and end-of-life assessment.

Internal Resistance Tracking Battery aging increases internal resistance, a measurable electrical characteristic that correlates with capacity loss. Electrochemical Impedance Spectroscopy (EIS)  applying a small AC signal and measuring the impedance response across frequencies  provides detailed characterisation of aging mechanisms. Simplified resistance estimation from voltage response to current pulses is used in production BMS implementations for continuous SOH tracking without requiring full charge/discharge cycles.

Incremental Capacity Analysis (ICA) and Data-Driven Methods ICA analyses the rate of capacity change versus voltage during charge/discharge cycles, identifying characteristic peaks and valleys that shift as the battery ages  providing quantitative aging signatures. Machine learning models trained on large datasets of real battery aging behaviour across diverse operating conditions are increasingly used in advanced BMS platforms to maintain SOH accuracy across the full battery lifecycle, adapting to individual cell aging trajectories rather than relying on fixed population averages.

Because SOH is closely tied to battery aging, understanding the factors that influence degradation is equally important. Explore our guide to Electric Car Battery Life: How Long They Last and How to Maximize Battery Health to learn how temperature, charging behaviour, and battery management affect long-term performance. 

Key Difference Between SOC and SOH

Metric	SOC	SOH
Definition	Percentage of current maximum capacity remaining	Percentage of original rated capacity that remains functional
Timeframe	Real-time, changes continuously with use	Long-term, changes slowly over weeks and months
Reset Ability	Resets to 100% with full charge	Does not reset  degrades progressively and irreversibly
Measurement	Current integration (Coulomb counting) + voltage + algorithm	Capacity testing, resistance measurement, data-driven models
Main Purpose	Fuel gauge  how much energy is available now	Health indicator  how much of original capability remains
Typical Value	0–100% in daily operation	100% (new) declining to 70–80% (end of life)
Operations	Managed cycle by cycle	Tracked over months and years of operation

Why Both SOC and SOH Are Critical

Tracking only one of these metrics gives an incomplete picture SOC without SOH can mask a battery that’s quietly losing capacity, while SOH without SOC leaves no visibility into day-to-day usable range. Here’s why a reliable BMS has to monitor both together, not as a substitute for one another.

Why SOC Is Critical

Real-time fuel gauge: SOC is the number that generates the range estimate displayed to the driver. Accurate SOC estimation  within ±2–3% across all operating conditions  is what determines whether the driver can trust the range readout. Inaccurate SOC estimation is one of the most common sources of EV range anxiety. A battery that reports 20% SOC when it’s actually at 10% creates a stranded vehicle risk that one accurate reading prevents.

Prevents extreme stress: The BMS uses SOC to enforce operating boundaries that protect cell health. Charging is terminated when SOC reaches 100% (preventing overcharge). Discharge is cut off when SOC approaches 0% (preventing deep discharge). Charge and discharge rate limits are adjusted based on SOC; high-rate charging is restricted at very high and very low SOC where lithium plating and other stress mechanisms are most active. Accurate SOC is the prerequisite for all of these protective functions.

Enables daily use: Every practical decision an EV operator or fleet manager makes about battery use  when to charge, how far to drive, which vehicle to dispatch  is based on SOC. Accurate, reliable SOC reporting is the foundation of confident EV operation.

Why SOH Is Critical

Measures degradation: SOH is the quantitative measure of how much capacity the battery has lost to aging. Without SOH tracking, degradation is invisible until it becomes severe enough to be perceptible as reduced range  at which point significant capacity has already been lost. Early SOH monitoring provides visibility into degradation before it reaches operationally significant levels.

Tracks life expectancy: SOH trajectory  the rate at which SOH is declining  enables prediction of when the battery will reach its end-of-life threshold. For fleet operators managing battery replacement planning across hundreds of vehicles, SOH trajectory data is the input that allows proactive replacement scheduling, avoiding both premature replacement (wasted asset value) and failure in service (operational disruption).

Monitor resistance: Internal resistance increase is both a consequence of aging and a cause of further degradation; higher resistance generates more heat during operation, accelerating the thermal aging mechanisms. SOH tracking through resistance measurement gives the BMS and EMS visibility into resistance trends that inform thermal management strategy and charge/discharge rate limits.

How SOC and SOH Work Together

SOC and SOH aren’t independent numbers how a battery is charged and discharged day to day (SOC behavior) directly shapes how fast it degrades over time (SOH), and a declining SOH in turn changes what SOC readings actually mean. Here’s how the two interact in practice, and why a BMS needs to manage them together rather than in isolation.

The Denominator Effect

SOH defines the denominator against which SOC is calculated. A battery at 80% SOH has a current maximum capacity of 80% of its original rated value. When this battery is fully charged, its SOC reads 100%  but the absolute energy available is only 80% of what 100% SOC meant when the battery was new. This interaction means that accurate SOC estimation requires accurate SOH knowledge; the BMS must continuously update its capacity reference as SOH declines to maintain SOC accuracy across the battery’s aging trajectory.

Cycle Tracking for Health

Every charge-discharge cycle contributes to SOH decline. The BMS tracks cumulative cycle count alongside depth of discharge per cycle, temperature exposure, and charge rate history, the variables that collectively determine the rate of SOH decline. This data is the input to predictive degradation models that project future SOH trajectory and estimate remaining useful life.

Range and Safety Predictions

The combination of real-time SOC and long-term SOH enables accurate range prediction and proactive safety management. Range = (SOC × Current Capacity) / Energy consumption rate  where Current Capacity = SOH × Rated Capacity. Both SOC accuracy and SOH accuracy are required for range prediction accuracy. For safety management, SOH tracking identifies batteries approaching the internal resistance thresholds where thermal management becomes more critical  allowing the EMS to adapt dispatch strategies before safety margins are compromised.

Why SOC and SOH Matter in Electric Vehicles

For EV owners and fleet operators alike, SOC and SOH aren’t just technical metrics they directly affect daily range confidence, resale value, warranty claims, and long-term cost of ownership. Here’s why these two numbers matter so much in practice.

Why State of Charge (SOC) Matters

Range Estimation: The range indicator that EV drivers rely on is a direct function of SOC accuracy. SOC estimation error of ±5% in a 400 km range vehicle translates to ±20 km of range uncertainty, enough to determine whether a driver charges at an intermediate stop or attempts to reach their destination. For fleet operators, SOC accuracy determines dispatch decisions across the full fleet every day.

Safety Protection: The BMS uses SOC to enforce the protection boundaries that prevent overcharge and deep discharge, the two most damaging events a lithium-ion cell can experience. A BMS with poor SOC accuracy either cuts off usable capacity prematurely (conservative protection threshold to compensate for SOC uncertainty) or allows cells to approach dangerous extremes (SOC appears safe when it isn’t). Accurate SOC is the prerequisite for both safety and usable capacity.

Why State of Health (SOH) Matters

Capacity Tracking: SOH quantifies the capacity the battery has lost to aging  information the driver experiences as reduced range and the fleet operator experiences as reduced vehicle productivity. Continuous SOH tracking translates invisible electrochemical degradation into a quantitative metric that operations teams can act on.

Resale Value: EV resale value is directly tied to battery SOH. A used EV with documented 90% SOH commands a meaningfully higher price than one with 75% SOH and uncertain history. Standardised BMS-generated SOH documentation  battery health reports  are increasingly used in used EV transactions and are the foundation of OEM battery certified pre-owned programmes.

Warranty Claims: Most EV battery warranties guarantee SOH above a threshold (typically 70% in India under CMVR regulations) for a defined period. The BMS’s SOH measurement is the instrument that determines whether a warranty claim is valid. Accurate, tamper-evident SOH logging is therefore both a consumer protection mechanism and an OEM liability management tool.

SOC and SOH are only two of the many parameters continuously monitored by a BMS. For a broader overview of how the BMS fits into an EV battery pack, see our guide to The Ultimate Guide to Electric Vehicle Components and Their Functions.

What Factors Affect SOC and SOH?

SOC and SOH don’t decline in a vacuum a handful of real-world factors, from charging habits to temperature exposure, drive how fast a battery loses capacity and how reliable its readings stay. Here’s a look at the key variables that influence both.

Factors Affecting SOC (State of Charge)

Discharge Rate: Higher discharge rates (high C-rate) cause greater voltage depression due to ohmic losses and concentration polarisation  making voltage-based SOC estimation less accurate at high currents. Coulomb counting accuracy is not affected by rate directly, but the heat generated at high rates introduces secondary effects.

Temperature: Cold temperatures increase internal resistance and reduce the battery’s ability to deliver its full rated capacity  meaning the effective SOC at a given absolute charge state is lower in cold conditions than at room temperature. The BMS must apply temperature correction to SOC estimates to maintain accuracy across the operating temperature range.

Self-Discharge: Lithium-ion batteries lose approximately 1–2% of charge per month during storage without cycling. Self-discharge is not tracked by Coulomb counting (no current flow to measure) and causes growing SOC error during extended storage periods. The BMS must recalibrate SOC at known reference points (full charge, full discharge) to correct for accumulated self-discharge error.

Aging and Wear: As the battery ages and SOH declines, the relationship between voltage, current, and SOC changes  the electrochemical model the BMS uses for SOC estimation drifts from reality. Adaptive SOC algorithms that update their model parameters as the battery ages maintain accuracy; fixed-model algorithms degrade in accuracy over the battery’s life.

Factors Affecting SOH (State of Health)

Cycle Count: Each charge-discharge cycle causes incremental degradation through lithium inventory loss, SEI layer growth, and active material fatigue. Cycle life varies dramatically by chemistry  LFP at 3,000–6,000 cycles versus NMC at 1,000–2,000 cycles  and by operating conditions within each chemistry.

Temperature Extremes: Heat is the primary accelerant of lithium-ion aging. Calendar aging (degradation from time alone) roughly doubles with every 10°C increase in storage temperature. Cycling at elevated temperatures accelerates both capacity loss and resistance increase. Cold temperatures cause lithium plating during charging  physical deposition of metallic lithium on the anode that permanently reduces capacity and creates internal short circuit risk.

Depth of Discharge (DoD): Deeper discharge cycles cause more degradation per cycle than shallow cycles. A battery cycled from 100% to 0% SOC daily degrades significantly faster than one cycled from 80% to 20%. Operating within a partial SOC window  the “longevity zone” typically 20–80% for NMC, 10–90% for LFP  extends cycle life by reducing the electrochemical stress per cycle.

Fast Charging: High charge rates accelerate lithium plating on the anode  particularly at low temperatures and at high SOC. Repeated DC fast charging as the primary charging method measurably shortens cycle life compared to slower AC charging. The BMS mitigates this through temperature-dependent charge rate limits and multi-stage fast charge protocols that reduce rate as SOC approaches 80–100%.

Challenges in Monitoring SOC and SOH

Measuring SOC and SOH accurately is harder than it looks; both depend on indirect estimation rather than direct measurement, and a range of real-world factors can throw those estimates off. Here’s what makes accurate monitoring difficult, and why even well-designed BMS algorithms have to account for these variables.

State of Charge (SOC) Challenges

Error accumulation: Coulomb counting  the backbone of SOC estimation  accumulates current sensor error over time. A current sensor with ±0.1% accuracy introduces 0.1% SOC error per full cycle, compounding to meaningful drift over hundreds of cycles without recalibration. The BMS must implement error correction at known reference points to prevent drift from degrading SOC accuracy over time.

Flat voltage curves: LFP’s discharge voltage is nearly flat across 20–80% SOC  making it almost impossible to infer SOC from voltage alone in this range. A 0.02V measurement change corresponds to a 40%+ SOC change in the flat region. This forces LFP BMS implementations to rely almost entirely on Coulomb counting with algorithm correction, with correspondingly higher sensitivity to current sensor accuracy and drift.

Load and temperature swings: Dynamic operating conditions  rapidly varying current during aggressive driving or regenerative braking, wide temperature swings between winter morning and summer afternoon  create rapidly changing electrochemical conditions that challenge fixed-model SOC estimators. Adaptive algorithms that update their internal model in real time are the engineering response, but add computational complexity.

State of Health (SOH) Challenges

No direct sensor: Unlike SOC, which can be estimated from measurable electrical quantities, SOH has no direct analogue sensor. It must be inferred from capacity measurements, resistance tracking, and pattern recognition in operational data  all of which are indirect and subject to measurement uncertainty.

Coupled variables: Battery aging mechanisms are coupled  capacity loss, resistance increase, and lithium inventory loss all affect each other and are all affected by the same operating variables (temperature, rate, DoD). Separating these contributions to estimate total SOH degradation accurately is a complex estimation problem that even sophisticated algorithms do not solve perfectly.

High compute cost: Advanced SOH estimation methods  electrochemical impedance spectroscopy, incremental capacity analysis, physics-based degradation models  require significant computational resources and controlled operating conditions that are difficult to implement in production BMS hardware with real-time constraints. Simplified methods sacrifice accuracy; accurate methods sacrifice computational feasibility. The balance point is an active area of BMS engineering development.

SOC and SOH Comparison Across EV Applications

EV Application	Core Chemistry Focus	SOC Operational Window	SOH Retirement Threshold	Primary BMS Challenge
Passenger EVs (Sedans, SUVs)	NMC / LFP	10–100% (daily); 20–80% (longevity mode)	70–75% SOH	Range accuracy at low SOC; user-facing SOC precision
Commercial Fleets (Buses, Delivery Trucks)	LFP	20–80% (managed DoD for cycle life)	75–80% SOH (operational capacity requirement)	High cycle count accuracy; SOH fleet management across hundreds of units
Two/Three-Wheelers (E-Bikes, Rickshaws)	LFP	10–100%	70% SOH	Cost-effective accuracy on simple hardware; flat LFP curve SOC estimation
Heavy Industrial/Mining EVs	NMC / LFP	20–80% (conservative for reliability)	80% SOH (high operational availability requirement)	Extreme temperature SOC correction; resistance tracking for high-current applications

Conclusion

SOC and SOH answer different questions on different timescales, and both matter for safe, efficient battery operation. SOC is the real-time fuel gauge governing charge and discharge decisions; SOH is the long-term health record that determines value, warranty status, and replacement timing. A BMS that tracks both accurately and continuously is what separates a battery that performs to spec from one that fails early.

Frequently Asked Questions

What does 80% SOC mean? 

A battery at 80% SOC has 80% of its current maximum capacity available for a 400 km EV, that’s roughly 320 km remaining. The absolute energy this represents also depends on the battery’s SOH, since a degraded battery holds less energy at the same SOC.

What is SOC for a battery? 

SOC (State of Charge) is the real-time percentage of usable energy remaining relative to current maximum capacity, from 0% to 100%. The BMS calculates it continuously using current integration, voltage measurement, and adaptive algorithms.

What is 30% SOC? 

A battery at 30% SOC has 30% of its current capacity remaining. Most manufacturers recommend charging before dropping below 20%, and fleets typically use 30% as a dispatch alert threshold.

What is 70% SoH? 

A battery at 70% SOH retains 70% of its original rated capacity; a 60 kWh pack would store only 42 kWh. In India, CMVR regulations require replacement if a battery falls below 70% SOH within its warranty period.applications, 70% SOH is the practical end-of-life threshold for the primary EV use case  though the battery may still be valuable for second-life stationary storage applications.

Does SOH affect charging speed? 

Yes, indirectly. As SOH declines, internal resistance rises, generating more heat, so the BMS may derate charge rate to protect cells, an effect that becomes noticeable below roughly 80% SOH.

What tools measure SOC accurately? 

Production BMS units combine precision current sensors, cell voltage measurement, and algorithms like the Extended Kalman Filter. Diagnostic tools such as CAN analysers or OEM software can read SOC directly from a deployed vehicle’s BMS.

Is SOH the same as battery life? 

Not exactly SOH is a current capacity measurement, while battery life is a forward projection of when SOH will reach its end-of-life threshold. A battery at 90% SOH degrading slowly may outlast one at 85% SOH degrading fast.

Is SOC more important than SOH? 

Neither is more important; they serve different purposes at different timescales. SOC matters moment-to-moment for drivers, while SOH matters strategically for fleet and warranty planning.

What causes a rapid drop in SOH? 

Repeated deep discharge, lithium plating from fast charging in cold conditions, sustained high temperatures, and physical cell damage all accelerate SOH loss. A quality BMS prevents most of these by enforcing voltage, temperature, and current limits..

“” “”