SLAAEF5 March   2024 MSPM0G1505 , MSPM0G1505 , MSPM0G1506 , MSPM0G1506 , MSPM0G1507 , MSPM0G1507 , MSPM0L1303 , MSPM0L1303 , MSPM0L1304 , MSPM0L1304 , MSPM0L1304-Q1 , MSPM0L1304-Q1 , MSPM0L1305 , MSPM0L1305 , MSPM0L1305-Q1 , MSPM0L1305-Q1 , MSPM0L1306 , MSPM0L1306 , MSPM0L1306-Q1 , 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 Equations
      1. 2.2.1 NomAbsSoc Calculation
        1. 2.2.1.1 Coulometer With OCV Calibration
        2. 2.2.1.2 Battery Model Filter
      2. 2.2.2 CusRltSoc Calculation
      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 Step1: Hardware Preparation
    2. 4.2 Step2: Get Battery Model
      1. 4.2.1 Battery Test Pattern
      2. 4.2.2 Battery Model Generation
    3. 4.3 Step3: Input Customized Configuration
    4. 4.4 Step4: Evaluation
      1. 4.4.1 Detection Data Input Mode
      2. 4.4.2 Communication Data Input Mode
  8. 5MSPM0 Gauge Solutions
    1. 5.1 MSPM0L1306 + 1 LiCO2 Battery
      1. 5.1.1 Hardware Setup Introduction
      2. 5.1.2 Software and Evaluation Introduction
      3. 5.1.3 Battery Testcases
        1. 5.1.3.1 Performance Test
        2. 5.1.3.2 Current Consumption Test
    2. 5.2 MSPM0G3507 + BQ76952 + 4 LiFePO4 Batteries
      1. 5.2.1 Hardware Setup Introduction
      2. 5.2.2 Software and Evaluation Introduction
      3. 5.2.3 Battery Testcases
        1. 5.2.3.1 Performance Test1 (Pulse Discharge)
        2. 5.2.3.2 Performance Test2 (Load Change)
  9. 6References

2.3 Algorithm Overview

This section provides you with an overview of the introduced gauge algorithm, shown in Figure 2-9. This is only for the battery cell algorithm. For battery pack algorithms, it is just a combination of battery cell algorithms.

This algorithm is based on the coulometer, paired with other methods described before to solve its limitation. It is combined of four parts. The Capacity Learn part is used to detect the battery rest, do OCV calibration and calculate SOH. The VGauge part is used to output the related parameters from the saved class-one battery model. IGauge is a coulometer, used to accumulate the capacity. Mixing part is used to calculate and output NomAbsSoc, CusRltSoc and SmoothRltSoc to customers.

GUID-BDC9AB9D-7D9A-47AC-ABF6-89D09DD11146-low.png Figure 2-9 Algorithm Overview

Figure 2-10 shows the algorithm performance based on a LiFePO4 battery simulation model, with 3000 random DHG/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 it does not ensure the error range of the algorithm in a real application to detect CusRltSoc.

GUID-97F02E83-8072-4195-9019-29F3BEF9E94F-low.png Figure 2-10 Algorithm Performance (By Simulation)

In the following section, a description for every algorithm part and its key parameters outputted to GUI or other functions is provided.