PyBaMM Charging Conundrum: Unlocking 100% SoC for Accurate Battery Simulations

The PyBaMM Charging Conundrum: Why Isn't My Battery Reaching 100%?

A common point of confusion for engineers and developers working with battery simulation software like PyBaMM often arises when trying to achieve a full 100% State of Charge (SoC). This challenge, highlighted in a recent GitHub Community discussion, underscores the importance of understanding both real-world battery behavior and the nuances of simulation protocols. For many, accurately modeling a battery's full charge cycle is a critical software development kpi, indicating the reliability and realism of their simulations and the underlying product.

The Problem Unveiled: Unexpected SoC Limits During 1C Charge

User Saharphd initiated a discussion after observing that their PyBaMM experiments, using commands like "Charging at 1C for 1 hour" or "Charging at 1C until 4.2V", consistently resulted in the battery's SoC reaching only around 0.6, despite starting at 0.2. Their calculation, soc = initial_soc + (throughput_capacity / nominal_capacity), seemed logically sound, leading to the question: what was causing the unexpected ceiling?

Understanding the Discrepancy: Voltage Cutoffs and Real-World Batteries

The core of the issue lies in the fundamental behavior of lithium-ion batteries and how they are safely charged. As contributors tmilost and healer0805 explained, a simple constant current (CC) charge protocol, even if specified for a full hour or until a certain voltage, rarely results in a 100% SoC in a realistic model:

• Voltage Cutoff is King: Most batteries have a strict upper voltage limit (e.g., 4.2V for many Li-ion cells). During a constant current charge, the cell's terminal voltage will rise. Long before the theoretical '1C for 1 hour' capacity is delivered, the voltage limit is typically reached. Once this limit is hit, the charging process must stop or switch modes to prevent damage and ensure safety.
• Capacity Mismatch: If charging starts at a low SoC (like 0.2), the battery will often hit its voltage limit before receiving the full 80% (0.8) of its stated capacity needed to reach 100% SoC. The 'throughput capacity' recorded by the simulation will thus be less than what's theoretically expected to bring the SoC to 1.0.
• Model Constraints: Even if you specify '1 hour', the PyBaMM solver will respect the model's inherent constraints, such as voltage cutoffs. The charging current will effectively drop, or the step will end early, once these limits are reached.

This means your formula, while conceptually correct, is applied to a 'throughput capacity' that is constrained by real-world (and thus, model-world) physics, not an idealized constant current delivery.

The Solution: Embracing the CC-CV Protocol for Full Charge

To accurately simulate a battery reaching full charge (SoC = 1.0), you need to mimic real-world charging strategies. The industry standard is a two-stage Constant Current-Constant Voltage (CC-CV) protocol. This is how you achieve full charge safely and efficiently in actual battery management systems, and it's how you should model it in PyBaMM.

Here's how it works and how to implement it in PyBaMM:

1. Constant Current (CC) Stage: The battery is charged at a constant current (e.g., 1C) until its terminal voltage reaches the upper voltage limit (e.g., 4.2V).
2. Constant Voltage (CV) Stage: Once the voltage limit is reached, the charger holds the voltage constant at that limit (e.g., 4.2V), while the current gradually tapers down. Charging continues until the current drops to a very low threshold (e.g., C/50 or C/20). This tapering current allows the battery to absorb the remaining capacity without overvoltage.

In PyBaMM, this translates to a simple yet powerful experiment definition:

experiment = pybamm.Experiment([
    "Charge at 1C until 4.2V",
    "Hold at 4.2V until C/50"
])

By using this two-stage protocol, your simulated battery will realistically approach an SoC of 1.0 by the end of the constant voltage stage.

Beyond the Protocol: Other Considerations for Accurate Modeling

While the CC-CV protocol is the primary fix, healer0805 also highlighted other crucial points for technical leaders and developers:

• Throughput Capacity Variables: Be mindful of which PyBaMM variable you're using for 'throughput capacity'. Some variables might track total absolute charge, including discharge, or cumulative over the entire simulation. For SoC during a specific charge step, look for variables like "Charge capacity [A.h]" or the integral of current over time for that specific step.
• Nominal Capacity Matching: Ensure your 'nominal capacity' in the SoC formula accurately reflects the 'usable capacity' defined within your PyBaMM model's parameter set. Discrepancies here can lead to under or over-estimation of SoC changes.
• Solver Cutoffs: Understand that PyBaMM models have various internal cutoffs (e.g., lower voltage limits, concentration limits) that can also end a step prematurely, even if not explicitly stated in your experiment string. Familiarity with your chosen model's parameters is key.

Impact on Productivity, Delivery, and Technical Leadership

For dev team members, product/project managers, and CTOs, understanding these nuances in battery simulation is more than just a technical detail—it's a strategic imperative. Accurate battery modeling directly impacts:

• Product Reliability: Realistic simulations lead to more robust product designs, reducing the risk of field failures and costly recalls.
• Faster Iteration & Delivery: When simulations accurately predict real-world behavior, development teams can iterate faster, reducing physical prototyping cycles and accelerating time to market. This directly contributes to positive software developer statistics related to efficiency and feature delivery.
• Informed Decision-Making: Technical leaders can make better decisions regarding battery chemistry, sizing, and charging strategies when armed with reliable simulation data. This insight can even inform discussions in your next agile methodology retrospective meeting, highlighting areas for tooling improvement or model refinement.
• Resource Optimization: Precise models help optimize battery usage, extending lifespan and improving overall system efficiency, which is a key software development kpi for sustainable products.

The 'error' Saharphd encountered wasn't in their formula's logic, but in the assumption that a simplified charging protocol would yield a full charge in a realistic battery model. By adopting the industry-standard CC-CV protocol, you empower your PyBaMM simulations to reflect actual battery behavior, leading to more reliable products, faster development cycles, and stronger technical leadership.

TL;DR — The Solution for 100% SoC in PyBaMM

• The Problem: Simple constant current (1C) charging protocols hit voltage cutoffs (e.g., 4.2V) before reaching 100% SoC.
• The Fix: Use a two-stage Constant Current-Constant Voltage (CC-CV) protocol.
• PyBaMM Implementation: experiment = pybamm.Experiment(["Charge at 1C until 4.2V", "Hold at 4.2V until C/50"])
• Why it Matters: Accurate simulations improve product reliability, accelerate delivery, and provide critical data for technical leadership and software development kpis.