Battery health checks are a familiar concept for many of us, especially when it comes to consumer devices like smartphones with built-in indicators to determine when it’s time to service or replace batteries, or when performance is beginning to degrade.

The importance of battery health checks extends far beyond our personal gadgets, however. In industrial and large-scale contexts such as energy storage systems and e-mobility, battery health checks are crucial for ensuring the safety, efficiency, and longevity of these critical systems that represent a sizable investment for the companies that deploy them. In this article, we’ll look at three ways to evaluate and determine battery health.

Checking Battery Health for Deployed Batteries
In the realm of energy storage and e-mobility, battery health checks involve a series of diagnostic tests and monitoring techniques to assess the condition of deployed batteries. One common method is the open circuit voltage (OCV) test, which measures the voltage of a battery when it’s not connected to any load or charger¹. A significant deviation from the expected OCV can indicate underlying issues such as sulfation, internal shorts, or cell imbalance².

Another important aspect of battery health checks is monitoring battery aging. Over time, batteries degrade due to various factors such as temperature fluctuations, charge-discharge cycles, chemical reactions, and calendar aging³. In this context, it’s essential to have access to high resolution data that can detect shifts in battery performance, with a focus on early identification of problem areas to prevent disruptions and prolong life spans. Issues can include electrolyte oxidation, cathode dissolution, side reactions leading to the formation of gasses, and thermal degradation.

To consider one such issue, thermal degradation poses significant risks to batteries used in energy storage systems, impacting both performance and lifespan. Elevated temperatures accelerate chemical reactions within the battery, leading to the breakdown of electrode materials and electrolyte decomposition. This results in capacity fade, increased internal resistance, and reduced energy efficiency. For instance, a study on lithium-ion batteries revealed that operating at 45°C instead of 25°C can reduce battery lifespan by up to 50%⁷. Causes of thermal degradation include high ambient temperatures, excessive charging and discharging rates, and poor thermal management. Effective cooling systems and optimal charging practices are essential to mitigate these risks and ensure the longevity of energy storage batteries.

Solutions such as Peaxy Predict can provide insights into lithium ion battery health with state of health (SOH) comparisons between expected and actual, and degradation forecasting. 

Lithium plating is an aging phenomenon to monitor that can also affect battery health. This occurs when lithium ions deposit on the anode surface during charging, leading to reduced capacity and increased risk of short circuits³. Research indicates that lithium plating can reduce the capacity of lithium-ion batteries by up to 20% under certain conditions, such as fast charging or low-temperature environments⁶. This reduction in capacity can significantly impact the performance and lifespan of batteries, making it crucial to monitor and mitigate this phenomena to maintain the health and safety of lithium-ion batteries.

Lithium plating is a critical issue for lithium-ion batteries in the electric vehicle (EV) industry, and occurs predominantly during charging, particularly under high charging rates or low temperatures. This can lead to capacity loss, increased internal resistance, and potential safety hazards such as short circuits and thermal runaway. A study by Purdue University highlighted that lithium plating is exacerbated during fast charging, which is a common requirement for EVs. The research indicated that lithium plating could reduce the battery’s lifespan by up to 20% and increase the risk of catastrophic failures⁸. Another study from the University of Warwick quantified that fast charging could increase the loss of lithium inventory (LLI) and loss of active material (LAM) by 10% and 12%, respectively⁹. These findings underscore the importance of both optimizing charging protocols and improving battery management systems to mitigate the risks associated with lithium plating in EV batteries. Let’s take a closer look at some of the ways to address this issue and battery health overall.

Predicting Battery Health Using Analytics and Machine Learning
Advancements in analytics and machine learning have revolutionized the way we predict and manage battery health. By analyzing large datasets from battery operations, machine learning algorithms can identify patterns and predict future performance.

These predictive models take into account various factors such as electrochemical kinetics, thermal behavior, and charge-discharge profiles⁵. With these insights, operators can implement proactive maintenance strategies and optimize charging protocols to extend battery life and enhance performance. Real-time data from sensors, for example, enable detailed monitoring ranging from individual battery cells to fleet level views, regardless of the equipment manufacturer or supporting systems.

Peaxy Predict improves battery and system health with advanced analytics and machine learning. Anomaly detection identifies cells deviating significantly from the system mean with KPIs such as voltage, temperature and SOH. Runtime machine learning modules based on live metrics enable optimized battery augmentation schedules and improved dispatch decisions.

Predictive models offered as part of Peaxy Predict analyze the large volume of battery data at a high fidelity to forecast key battery health metrics, such as potential failure points, remaining useful life (RUL), and state of health (SOH). This allows for proactive alerts, enabling timely interventions before issues escalate, thus preventing costly downtimes and extending the battery lifespan through careful management. Predictive analytics can also help to improve the forecast and planning for system augmentation. A study by McKinsey found that predictive maintenance using machine learning can reduce maintenance costs by up to 25% and decrease unplanned outages by 50%¹⁰. These insights are crucial for optimizing performance, enhancing safety, and ensuring the reliability of energy storage systems across various applications.

Conclusion
Battery health checks are essential for ensuring the reliability and efficiency of energy storage systems and e-mobility applications. Peaxy Predict combines traditional diagnostic methods with advanced analytics and machine learning to better predict and manage battery health. Tools such as State of Health (SOH), State of Charge (SOC) and digital twins provide estimates that can ultimately lead to more sustainable and cost-effective energy solutions to protect your significant investments in clean energy.

¹https://www.batteryskills.com/battery-open-circuit-voltage-test/

²https://chargerblog.com/battery-open-circuit-voltage-test/

³https://onlinelibrary.wiley.com/doi/pdf/10.1002/est2.602

⁵https://eepower.com/tech-insights/simulation-model-predicts-sodium-ion-battery-health-and-longevity/

⁶https://www.mdpi.com/2313-0105/9/7/350

⁷https://www.mdpi.com/2313-0105/10/7/220

⁸Lithium plating: Purdue researchers tackle the toughest problem with …

⁹A Study on the Influence of Lithium Plating on Battery Degradation – MDPI

¹⁰Predictive Maintenance & Machine Learning: Models, Algorithms – Plat.AI