Ready to Deploy BMS Algorithms

For all battery applications

With our accurate state of charge (SoC) and state of health (SoH) estimations, our BMS algorithms increase battery life, improve safety and enable optimal charging.

WHY AMP BMS ALGORITHMS?

AMP BMS algorithms are off-the-shelf and can be configured to the specific needs of your battery application. With the ability to be optimized to work efficiently with your battery chemistry, our BMS algorithms help improve battery life while maximizing the power that can be safely utilized.

Our Algorithms are enabled with adaptive and self learning mechanisms that allow for high precision of SoC and SoH estimations.

With seamless transitions between coulomb counting and OCV corrections, our BMS algorithms offer optimum performance and efficiency, getting access to every single coulomb available in your battery pack.

300+ Years

Combined Experience

Series Production

Faster Time to Market

Hardware Agnostic

Fully Compatible

Self Learning

With High SoX Accuracy

Wide Range

Available from 24V to 1000V

Easy to Integrate

With Existing Architectures

PROVEN BMS ALGORITHMS

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Vehicles on Road

OUR ALGORITHMS

Impeccable cell balancing, with high SoC and SoH accuracy, has made our BMS algorithms #1 worldwide. AMP BMS algorithms are the most efficient, safe, and reliable.

State of Charge (SoC) Accuracy

Our BMS algorithms can estimate the SoC of the battery pack within a 3% accuracy range. We use a novel OCV Correction strategy to update SoC in seconds if there is any
unquestionable error.

Capacity State of Health (SoHC) Accuracy

AMP BMS algorithms can estimate SoHC of the battery pack with 2% accuracy at different cell configuration and temperatures. Our expertise in cell testing for SoH characterization ensures our algorithms to deliver a short term dynamic and long term robust estimation.

State of Energy (SoE)

The algorithm uses Joule Counting Method to estimate the module’s energy within 1Wh resolution to give you a range prediction that you can trust.

Power Prediction

Our power prediction model is aggressive to not leave power off the table. Our algorithms predict discharge and regeneration current from 0ºC to 65ºC. Power throughput is derated linearly based on temperature and SoH.

Resistance and Impedance

This algorithm use an underlying algorithm of the Kalman Filter to estimate resistance and cell impedance during operation. This helps determine power prediction and SoHR. The cell data is also used for redundancy checking.

Charge Control

The charge control algorithm allows you to quickly and safely charge all cells to a 100% SoC. This CC/CV algorithm considers the temperature and charge mode to estimate the time required to reach the target SoC.

Balancing

The algorithm balances cells based on coulombic imbalance for full charge. This allows us to achieve cell balancing at much smaller currents with reduced heat generation and higher balancing resolution.

RoTC - Rate of Temperature Change

The algorithm predicts the projected cell temperature using the rate of temperature change. This is to help achieve the AIS-156 amendment for thermal runaway detection.