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

2.2.1.2 Data Fusion

As seen in Equation 4, in a real application, the calibrated NomAbsSoc is affected by the voltage detection accuracy, battery rest time and SOC-OCV table accuracy, which is especially critical on the LiFePO4 battery. The DeltaQ is influenced by the shunt resistor accuracy and ADC performance. This means the calibrated NomAbsSoc and the NomFullCap all include some errors. In the Gauge Level2, use a data fusion method, which has the same concept with the kalman filter. TI gives weight to all the error sources, including measured current, measure voltage, evaluated OCV and so forth. After that, users know the weight of the SOC generated through coulometer and the SOC generated through OCV calibration. At last, users can obtain a more accurate NomAbsSoc after combining these two SOCs together, as shown in Figure 2-5.

Data Fusion Figure 2-5 Data Fusion

Figure 2-6 shows the data fusion algorithm performance based on a LiFePO4 battery simulation model, with 3000 random DHG and CHG points, after considering the detection error. The AbsNomSoc error across the battery life is controlled within 3.5%, and the NomFullCap error is controlled within 4%. Remember that the result is only used to show the algorithm capability to detect NomAbsSoc with a limited condition and this does not make sure of the error range of the algorithm in a real application to detect CusRltSoc.

Algorithm Performance (By Simulation) Figure 2-6 Algorithm Performance (By Simulation)