SLAAEF5B March   2024  – June 2025 MSPM0G1505 , MSPM0G1506 , MSPM0G1507 , MSPM0G3506 , MSPM0G3507 , MSPM0H3216 , MSPM0L1303 , MSPM0L1304 , MSPM0L1304-Q1 , MSPM0L1305 , MSPM0L1305-Q1 , MSPM0L1306 , MSPM0L1306-Q1

 

  1.   1
  2.   Abstract
  3.   Trademarks
  4. 1Introduction
  5. 2Algorithm Introduction
    1. 2.1 Battery Basic Knowledge Introduction
    2. 2.2 Different SOCs and Used Technologies
      1. 2.2.1 NomAbsSoc Calculation
        1. 2.2.1.1 Coulometer With OCV Calibration
        2. 2.2.1.2 Data Fusion
        3. 2.2.1.3 Battery Model Filter
      2. 2.2.2 CusRltSoc Calculation
        1. 2.2.2.1 EmptySoc and FullSoc
        2. 2.2.2.2 Core Temperature Evaluation
      3. 2.2.3 SmoothRltSoc Calculation
    3. 2.3 Algorithm Overview
      1. 2.3.1 Voltage Gauge Introduction
      2. 2.3.2 Current Gauge Introduction
      3. 2.3.3 Capacity Learn Introduction
      4. 2.3.4 Mixing Introduction
  6. 3Gauge GUI Introduction
    1. 3.1 MCU COM Tool
    2. 3.2 SM COM Tool
    3. 3.3 Data Analysis Tool
  7. 4MSPM0 Gauge Evaluation Steps
    1. 4.1 Step 1: Hardware Preparation
    2. 4.2 Step 2: Get a Battery Model
      1. 4.2.1 Battery Test Pattern
      2. 4.2.2 Battery Model Generation
    3. 4.3 Step 3: Input Customized Configuration
    4. 4.4 Step 4: Evaluation
      1. 4.4.1 Detection Data Input Mode
      2. 4.4.2 Communication Data Input Mode
    5. 4.5 Step 5: Gauge Performance Check
      1. 4.5.1 Learning Cycles
      2. 4.5.2 SOC and SOH Accuracy Evaluation
  8. 5MSPM0 Gauge Solutions
    1. 5.1 MSPM0L1306 and 1 LiCO2 Battery
      1. 5.1.1 Hardware Setup Introduction
      2. 5.1.2 Software and Evaluation Introduction
      3. 5.1.3 Battery Test Cases
        1. 5.1.3.1 Performance Test
        2. 5.1.3.2 Current Consumption Test
    2. 5.2 MSPM0G3507, BQ76952 and 4 LiFePO4 Batteries
      1. 5.2.1 Hardware Setup Introduction
      2. 5.2.2 Software and Evaluation Introduction
      3. 5.2.3 Battery Test Cases
        1. 5.2.3.1 Performance Test 1 (Pulse Discharge)
        2. 5.2.3.2 Performance Test 2 (Load Change)
    3. 5.3 MSPM0L1306 and BQ76905
  9. 6Summary
  10. 7References
  11. 8Revision History

2.2.1.3 Battery Model Filter

In Section 2.2.1.1, NomAbsSoc is evaluated based on purely one battery parameter (voltage or current accumulation). One method is to use the relationship between OCV (positive and negative electrodes balance) and NomAbsSoc (different lithium ion concentrations). Another method is to use the relationship between coulometer (electron numbers) and capacity (movable lithium ion numbers). This strategy requires less computing resources, as only a coulometer is needed to work in every cycle. However, this needs to wait until the NomFullCap is generated for 1-2 battery cycles.

Another strategy is used to create a model or a filter to evaluate the NomAbsSoc or even the CusRltSoc based on all the input parameters, like current, voltage, time and temperature. The common methods are equivalent-circuit model, Kalman-filter, neural network and so on. The accuracy of the SOC output mostly lies on the model accuracy. However, more complex model means more MCU computing resources.

An economic method is to use a simplest equivalent-circuit model (a first order RC model), shown in Figure 2-7, to output NomAbsSoc with only voltage input.

Battery Model Figure 2-7 Battery Model

Figure 2-8 shows the software flow chart of VGauge. However, in this design, the SOC lookup table function only needs to be used to search the wanted parameters in this battery model. The SOC part comes out from IGauge.

VGauge Software Flow Figure 2-8 VGauge Software Flow

For more about the NomAbsSoc accuracy outputted from VGauge, see MSPM0 Gauge L1 Solution Guide, application note.