The increasing adoption of lithium-ion batteries is boosting growth in e-mobility, medical, and robotics markets. Moreover, current application requirements are driving the adoption of larger battery packs with an extended range, longer life, and superior power capabilities.
To guarantee excellent performance, it is vital to address the challenge of estimating a battery’s internal state in battery-based applications. This task is accomplished by the fuel gauge. Fuel gauges accurately estimate the battery’s internal states while providing key information about the battery, such as state-of-charge (SoC), state-of-health (SoH), and power limits. However, developing such complex algorithms requires an in-depth chemical understanding of Li-ion cells, expertise on nonlinear state estimation techniques and control theory, as well as significant resources and time.
This article introduces a novel, highly adaptable fuel gauge for high-voltage battery packs that enables a drastic time-to-market reduction while retaining high estimation accuracy. This article focuses on four key areas: advanced algorithm design, simple system integration, effortless fuel gauge configuration, and quick virtual validation.
Advanced Fuel Gauge Algorithm Design
Fuel gauges use measurable quantities such as current, voltage, and temperature to infer the battery’s internal states. Current-based estimation methods, such as Coulomb counting, monitor the charge going in and out of the battery, whereas voltage-based estimation methods rely on voltage look-up tables. These methods may provide poor estimates when used alone.
The alternative is to use model-based state estimation methods, which combine current-only and voltage-only methods. However, model-based methods rely on a cell mathematical model that captures the cell’s most important dynamics, such as open-circuit and diffusion voltages. Since these dynamics are heavily influenced by internal and environmental factors, their extraction depends on defined characterization tests across the complete cell operating range.
As an example, we’ll look at MPS’s MPF4279x series, a new fuel gauge family that uses an advanced model-based state estimation method to provide high-accuracy battery information, such as pack state-of-charge, remaining charge time, runtime, state-of-health, and power limits.
Table 1 shows an example of the battery pack’s state-of-charge performance under different operating conditions using the MPF42790. The metrics in Table 1 correspond to the state-of-charge root-mean-square error (and maximum state-of-charge error)
Table 1: MPF42790 State-of-Charge Performance
Figure 1 shows an example of the MPF42790 pack’s state-of-charge estimation performance, achieving 0.64 percent state-of-charge root-mean-square error and 1.46 percent maximum state-of-charge error. The test consists of a complete 1C constant-current and constant-voltage charge, which terminates when the charge current drops to 0.1C, followed by a 1C constant-current discharge on a multi-cell battery pack at 15°C (ambient temperature).
Figure 1:The MPF42790’sPerformance for a Complete 1C Charge/Discharge Cycle
MPS’s fuel gauge algorithm relies on high-fidelity electrical cell models generated from a proprietary characterization sequence, proprietary analysis, and optimization tools. This system allows users to easily load any one of these models into the fuel gauge (see Figure 2).
Figure 2: Cell Mathematical Model Generation
Simple Fuel Gauge System Integration