Updated December 4, 2025

Understanding a battery’s condition in the field is essential for predicting performance, preventing failures, and planning maintenance. Operators increasingly rely on battery analytics for state of charge and state of health to move beyond traditional estimates and gain deeper visibility into how assets behave under real-world conditions. As energy storage systems grow more complex and widely deployed, the need for precise, data-driven insights into battery behavior only becomes more pressing.

While conventional methods offer a snapshot of a battery’s condition, advanced analytics provide a fuller and more dynamic picture—one that reflects not just what a battery is doing now, but how it has been performing over time and what is likely to happen next.

Why Traditional Methods Aren’t Enough

Conventional approaches to estimating State of Charge (SoC) and State of Health (SoH)—such as open-circuit voltage checks, Coulomb counting, or impedance measurements—each have intrinsic limitations. They often depend on idealized conditions or assumptions that don’t always hold up in day-to-day operation. As systems cycle, age, or encounter fluctuating temperatures and loads, these methods can drift or lose accuracy, especially when used in isolation.

Battery analytics helps bridge these gaps by integrating multiple signals, contextual information, and historical data into a more cohesive interpretation. Instead of relying on one method, operators benefit from a combined analytical approach that continuously adapts to changing real-world behavior.

Peaxy Battery Analytics measures battery performance by ingesting a variety of data including configuration and telemetry data, BMS registers, and real-time computations to report on anomalous temperatures, voltage, degradation and SOH as shown in the examples above. More advanced computational models and machine learning algorithms ensure a higher quality result vs. a BMS alone.

Using Battery Analytics for State of Charge and State of Health

By layering physics-based models with machine learning, operators can uncover trends that traditional calculations might miss. For State of Charge, analytics can incorporate both instantaneous measurements and long-term usage patterns, improving accuracy even during rapid load shifts. For State of Health, analytics can reveal deeper indicators of degradation, including subtle changes in internal resistance, charge acceptance, voltage curves, and thermal response.

In addition to SOH and SOC estimators, some of the common metrics used in battery monitoring include voltage, current, and temperature. A robust battery analytics platform can ingest, normalize and parameterize a multitude of data from various sources into one common source of trust, using either physical sensors or existing API’s that preclude deployment of additional hardware. Battery monitoring tools that can take advantage of this data help to measure and track the key battery metrics that affect the performance and lifespan of the battery. For example, voltage sensors can detect overcharging or undercharging conditions, current sensors can measure the power output and input of the battery, temperature sensors can monitor the thermal stability and efficiency of the battery, and SOH and SOC estimators can calculate the remaining capacity and health of the battery based on various factors. 

Using Battery Analytics for State of Charge and State of Health

By using the tools and charts generated by an automated system that can easily traverse enormous amounts of data, a battery monitoring system can provide a more sophisticated and yet cost effective way to optimize the charging and discharging cycles, prevent damage or degradation, and extend the life and improve the performance of the battery. With the significant investment that a BESS requires to manufacture, install and operate, an effective battery analytics solution just makes economic sense.

A key advantage of advanced battery analytics is the ability to generate a more meaningful and transparent State of Health score. Instead of relying on a single metric or opaque OEM method, analytics derive SoH from multiple contributing factors—capacity fade, resistance growth, temperature behavior, cycle count, and deviations from expected voltage profiles. By weighting these factors appropriately, the resulting SoH score reflects real operating conditions rather than theoretical assumptions. This gives operators a clearer, more actionable understanding of how a battery is aging and what kind of performance they should expect going forward.

Over time, these combined insights allow SoC and SoH estimates to grow more stable, more predictive, and more resilient to the uncertainties inherent in field operation.

Context Matters: How Operating Conditions Influence Battery Behavior

A battery’s apparent health and charge level can vary significantly depending on ambient temperature, recent duty cycles, chemistry, and even how individual cells within a pack interact. Battery analytics helps operators interpret measurements correctly by comparing observed behavior against historical baselines and peer units in the same system.

For example, what looks like reduced SoH may simply be temporary cold-weather performance. Conversely, a pack that appears normal might hide early signs of imbalance or accelerated wear. Analytics brings clarity to these nuances by integrating context rather than treating each measurement in isolation.

Peaxy Battery Analytics improves existing lithium iron phosphate (LFP) SOC and SOH estimating and degradation forecasting with machine learning. Combining measurements from Voltage Open Circuit (VOC) and Coulomb Counting (CC), Peaxy’s Machine Learning Manager updates standard degradation curves based on actual dispatch, adjusts curves based on stressful dispatch and alerts for high c-rate, calendar aging, temperature variation, etc.

Improving Reliability and Predicting Failures Before They Occur

SOH scoring has been a boon to several customers who adopted our solution during development of their grid connected storage solutions.  In one case, SOH trending was used to dramatically aid the analysis of design of experiments, specifically while optimizing electrolyte recipes over a range of temperature domains.  In a second, SOH scoring during commissioning identified outlier modules in a fashion that was both automated – the customer needed no analysis other than what PLI provided – and customer configurable.

With a more accurate understanding of SoC and SoH, operators can make informed decisions about dispatch, maintenance scheduling, and long-term planning. Analytics can detect early indicators of failure—voltage depression, resistance growth, thermal irregularities—before they impact system availability.

Across large fleets, this capability transforms operations from reactive to predictive, helping teams allocate resources where they’re needed most. Accurate analytics-driven SoC and SoH assessments help detect unsafe trends—such as thermal runaway precursors or uneven cell behavior—early enough to intervene. This improves safety margins and reduces the risk of cascading failures.

Detailed historical data also supports warranty claims, regulatory documentation, and audit requirements by demonstrating that batteries were used and maintained within specification.

The Takeaway

Peaxy’s battery analytics platform can help BESS owners and operators achieve financial and operational benefits such as:

– Increased energy output and revenue by optimizing charge/discharge cycles and maximizing depth of discharge (DoD), minimizing voltage rebalancing requirements, and improving accuracy in energy trading BESS capacity.
– Reduced operational costs and risks by minimizing chances of over-charging or over-discharging, identifying unusual battery behavior, extending battery life, and preventing safety incidents.
– Enhanced asset management and planning by forecasting RUL and RV, identifying optimal augmentation and end of life timing, and enabling secondary market transactions.

Battery monitoring within a comprehensive battery analytics solution is a game changer for energy storage applications, as it can provide accurate estimation of SOC and SOH, which are essential parameters for optimizing performance, reliability, safety, and value. Our solution backed by battery knowledge experts can provide significant benefits with energy output, operational costs, asset management, and planning.

Frequently Asked Questions (FAQ)

Why are traditional methods insufficient for estimating State of Charge and State of Health?

Traditional methods such as voltage checks, Coulomb counting, or impedance measurements often rely on assumptions or idealized conditions. They may drift over time or lose accuracy during real-world operation. Battery analytics integrates multiple data sources to create more robust and reliable SoC and SoH estimates.

How do battery analytics improve State of Charge estimates?

Battery analytics strengthen State of Charge estimates by combining real-time measurements with historical patterns, fleet behavior, and machine learning models that adapt to temperature shifts, load changes, and aging effects that traditional methods often miss.

How does analytics create a more accurate State of Health score?

Analytics-derived SoH scoring uses multiple contributors—capacity fade, resistance growth, temperature behavior, cycle count, voltage curves, and other indicators—to produce a more meaningful, transparent, and actionable assessment of battery aging compared to OEM-provided single-metric scores.

Can battery analytics help predict failures?

Yes. Battery analytics can detect early indicators of failure such as rising internal resistance, unbalanced cell behavior, thermal irregularities, or abnormal voltage response. This allows teams to act before issues escalate into downtime or safety incidents.

How do analytics support battery safety and warranty compliance?

Analytics improve safety by identifying thermal anomalies and unsafe trends early. They also maintain detailed operational histories that help operators demonstrate proper system use, support warranty claims, and comply with audit or regulatory requirements.

* https://www.altenergymag.com/article/2017/09/understanding-battery-degradation/27102