Inaccurate SoC Costs You Money
In the first part of this series, we talked about the challenges of accurate SoC estimation (read it here). But how do you know you’re missing SoC readings and leaving money on the table? Here are three telltale signs:
1. You use large safety margins when making operational decisions.
Inaccuracy in SoC is a significant cause of operational inefficiencies for electrified fleet operators. Suppose you’re an enterprise operator of an EV fleet or energy storage system (ESS) assets. For you, SoC estimation is critical to understanding what revenue you can earn, and what you might leave on the table when there is a risk of underperformance.
It’s a simple calculation for customers operating ESS assets: their EMS and optimizers must operate conservatively to avoid costly penalties for overbidding capacity or energy across their value stack. Today, owner-operators around the country scratch their heads at the volatile and imprecise SoC estimates from their assets. In an EV fleet operation, these inaccurate readings lead technicians to retrieve the battery or vehicle for charging when it could still earn more revenue before incurring the service cost.
Imagine the implications for aerospace, like constellations of low earth orbit (LEO) satellites. Just like on Earth, batteries age in space. Constellation operators use SoC estimates and power plus thermal margin calculations to ensure their satellites are reliable. But most BMS systems today have wide confidence intervals on these estimates, meaning that satellites cannot operate near their full potential. A satellite isn’t like a car; you can’t take it to the garage every few months for a tune-up. Satellite operators need guaranteed performance on their SoC algorithms for the life of the asset for robust and reliable constellation operation.
The worst-case scenarios we see in the field today are reasons for deep concern. Degraded SoC estimation leads to stranded vehicles, missing battery backup power from ESS, and other failures that frustrate, and even injure, end customers. The bottom line is that battery systems are safer and more profitable when they are operated with precise, accurate SoC algorithms.
2. You’re oversizing batteries—or buying oversized batteries (esp. LFP) to compensate for poor SoC accuracy and future degradation.
Battery oversizing is one of the worst-kept secrets in the industry. If you aren’t intentionally specifying oversized assets, your manufacturer may already be oversizing them without telling you. EV and ESS battery makers report a “nameplate” capacity for packs. With nameplate capacity, a 1 MWh ESS asset may contain 1.2MWh of cells (20% oversized) to compensate for uncertainty about SoC algorithm performance, especially for LFP assets, and to mitigate problems with capacity and power degradation over time.
If you aren’t intentionally specifying oversized assets, your manufacturer may already be oversizing them without telling you.
Whether you’re designing and deploying your packs from the cells up or procuring them through a supplier, oversizing your system cuts into your margins or makes your prices less competitive. And, if your operational strategy and BMS parameters don’t account for oversizing well (for example, assets are frequently charged to 100% SoC) you could still be hurting the lifespan of your assets.
Ultimately, BMS algorithm observability is the key to avoiding capital, margin, and operating cost inefficiency. Zitara can help. Zitara Live provides accurate SoC, fixing the problem at its root. It also gives operators a clear picture of the capacity and power degradation across their fleets. By understanding the capabilities of each system, it becomes possible to determine what use cases they can serve, and ultimately what they are still worth.
3. You’re paying for fuel gauge chips that don’t guarantee performance.
Fuel gauge chips are commonly found in battery-powered applications across consumer electronics, e-bikes, drones, satellites, EVs, and ESS installations. They typically cost $1-3 per module, depending on volume. With increasing competition in the battery chip supply chain, you often must order them in lifetime-buy quantities with 12 or more months of lead time.
Fuel gauge chip marketing purposely avoids making claims about SoC performance.
But fuel gauges have a fundamental flaw when it comes to delivering accurate SoC estimates. Under the hood, they are low-power microprocessors with minimal space for memory or computation. They are designed to minimize die cost and keep idle current draw very low. Fuel gauge vendors usually provide standardized libraries to support different chemistries. For large customers they may even create customized tunings if you provide them with sample batteries, but it is typically limited to simple look-up-tables for OCV and impedance versus temperature and SoC—not nearly enough to deliver the performance today’s applications require.
Even worse, fuel gauge chip marketing is designed to avoid making claims about SoC performance. Instead, customers are encouraged to focus on raw analog accuracy, such as voltage resolution and coulomb counting, without explaining that customers must perform a broad matrix of tests to characterize the accuracy of the estimate in a specific application. To understand what they’re buying, fuel gauge customers must run:
- Tests across operating temperature range
- Tests under the full range of loading and charging conditions the cell will experience
- Tests where the cell is not discharged fully to 0% or charged fully to 100% for many cycles in a row, which is particularly important for LFP
- Tests on cells that have aged under different conditions. Zitara recommends validating algorithm performance on a full matrix of possible temperature, depth of cycle, and loading conditions at a relevant density of time intervals.
Chip vendors don’t have the cloud software capabilities to monitor performance across the various conditions in which their products are used.
With fuel gauge chips, this form of system-level testing is left up to the system integrator or operator of the asset. When you inevitably start to experience significant errors during use, you’ll quickly find the limits to what the vendor can or will do to improve performance. They are heavily constrained by underpowered hardware and a lack of software expertise. Another facet of the problem is that chip vendors don’t have the cloud software capabilities to monitor performance across the many varied conditions in which their products are used. Without knowing just how inaccurate the SoC estimates become as systems degrade, they lack the incentive to change. It is telling that vendors of these solutions don’t stand behind the performance of their software with a robust performance guarantees or service level agreements (SLA).
You Need Guarantees for SoC Accuracy
The bottom line is that manufacturers just don’t give you sufficient information about the performance of their SoC algorithms. They don’t provide testing and tuning for algorithm performance tailored to your system. They don’t monitor real-time performance in the field to ensure accurate SoC estimation across time and use case throughout the life of your deployment.
Zitara Live provides the guarantees you need—including rigorous lab testing and continuous online monitoring of algorithm performance. Our embedded and cloud-based algorithms for SoC, SoH, energy and thermal lookahead, and safety can be delivered with one-time validation benchmark or configured to include a lifetime SLA.
Contact us to learn more about Zitara Live and Zitara Studio.