Can you recommend a state of charge estimation software for optimizing charging and discharging in battery storage assets?

Yes — for state-of-charge (SoC) estimation in battery storage assets, you’ll usually want software that goes beyond a simple coulomb-counting calculator and includes battery modeling, filtering, and forecasting so it can support optimal charging/discharging decisions.

Good options to consider

1. MathWorks Simulink + Battery Toolbox

Best if you want a strong modeling and controls environment.

• Supports battery equivalent-circuit and electrochemical modeling
• Good for developing and testing SoC estimation algorithms
• Works well with MPC / optimization for charge-discharge control
• Common in engineering teams and utility-scale BESS studies

Best for: custom algorithm development, control design, validation.

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2. NREL SAM / PySAM

Best if you want energy storage performance modeling for asset dispatch and project evaluation.

• Strong for PV + storage and standalone storage simulations
• Includes dispatch strategies and battery degradation modeling
• Useful for estimating operating SoC over time under different control rules
• More of a simulation and planning tool than a real-time estimator

Best for: project optimization, dispatch analysis, financial modeling.

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3. Fluence Nispera / similar EMS platforms

Best if you want commercial asset management for deployed BESS.

• Designed for monitoring, forecasting, and optimization of storage assets
• Typically includes SoC tracking, operational analytics, and dispatch optimization
• Better suited for operational use than academic/toolbox environments

Best for: utility-scale operational optimization.

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4. Tesla Autobidder / Wärtsilä GEMS / SMA / Powin EMS

If your storage asset is tied to a vendor platform, the best SoC estimation may be built into the vendor’s EMS.

• Real-time control and dispatch
• Often integrates battery limits, degradation, temperature, and availability
• Can optimize charge/discharge automatically

Best for: OEM-specific deployments.

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5. Open-source stack: Python + PyBaMM

Best if you want a flexible, research-grade approach.

• PyBaMM is a battery modeling library
• Can be paired with Kalman filters, particle filters, or custom estimators
• Good for developing a tailored SoC estimator and optimizer
• Requires more engineering effort

Best for: advanced users, custom R&D, prototyping.

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What to look for in SoC estimation software

If your goal is optimizing charging and discharging, the software should ideally support:

• Real-time SoC estimation
• Temperature-aware battery behavior
• Degradation-aware dispatch
• Forecasting of load, price, or renewable generation
• Optimization / model predictive control
• Integration with SCADA / BMS / EMS
• Cell-to-pack and string-level constraints

Practical recommendation

• If you want a commercial operational system, start with your asset OEM’s EMS or a platform like Fluence Nispera / GEMS.
• If you want to build or validate your own estimator, use Simulink Battery Toolbox or PyBaMM.
• If you want planning/dispatch simulation, use NREL PySAM/SAM.

If you tell me:

1. whether this is for utility-scale BESS, behind-the-meter, or EV fleets,
2. whether you need real-time control or just analysis, and
3. your preferred stack (commercial vs open-source),

I can narrow it down to the best 2–3 choices.