Acculon Energy

Unlocking Peak Performance: State-of-Charge (SOC) Estimation for Commercial and Industrial Equipment Battery Packs

Today we will be exploring the vital role SOC plays in optimizing performance and extending the lifespan of your battery packs. 

Contact: Betsy Barry
Communication Manager

State of Charge (SOC) refers to the amount of energy currently stored in a battery as a percentage of its maximum capacity. In other words, SOC is the ratio of the available capacity and the maximum possible charge that can be stored in a battery. SOC indicates how much energy is available to power a device or a system. It is important to know the SOC of the battery pack in order to decide the operational usage of the battery pack, including the charge/discharge strategy to avoid overcharging and over-discharging.

Estimating the SOC of a battery pack is a challenging endeavor. As the demand for electric equipment in commercial and industrial settings continues to grow, it is vital that advanced SOC estimation strategies are part of the solutions offered. Poor SOC estimation has many negative impacts, including shorter battery lifespan, reduced performance, and difficulties with fleet management. Incorporating appropriate SOC strategies will help propel the growing commercial and industrial market segment.

One of the most important factors to consider when estimating SOC is the type of battery chemistry used in the pack. There are different types of lithium-ion battery chemistries, each with different discharge profiles. For example, LFP batteries have a very flat curve, while NMC batteries have more nonlinearities. Estimation algorithms must be tailored to each battery chemistry to provide accurate and reliable results. 

Another crucial factor is the accuracy of the voltage and current sensors used in the battery pack. These sensors provide critical data that is used to calculate SOC, so appropriate tradeoffs between sensor accuracy, cost, and size must be considered. Inaccurate sensor readings can be partially corrected with corrective estimation algorithms, but only to a certain extent.

Computational power is another vital consideration for SOC estimation strategies. Battery packs can be comprised of thousands of individual battery cells, with each cell having its own cell-level SOC. Running thousands of SOC estimations at once would take a very powerful microchip, so simplification strategies must be implemented. The complexity of the SOC estimation equations also contributes to computational demand, so a balance between accuracy and estimation sophistication must be made.

It is also important to consider the application when estimating SOC. Different applications have different power requirements, and the SOC estimation must take these factors into account. For example, a forklift that operates at maximum power for long periods day after day will have a different SOC estimation than an electric lawnmower that operates at a lower power level once every few weeks.

Accurate SOC estimations are highly dependent on customized approaches for different battery chemistries, sensors, computational power, & application-specific adaptations.

The expanding domain of electrification calls for meticulous attention to SOC estimation in battery-powered equipment. Customized approaches for different battery chemistries, sensors, computational power, and application-specific adaptations collectively contribute to accurate SOC estimations, ultimately enhancing the efficiency, performance, and longevity of battery packs powering a range of industrial applications.