State of Charge (SOC) Estimation for Lithium Batteries

The system studied the key technologies for estimating the SOC of lithium batteries. By optimizing equivalent circuit models or improving estimation algorithms, the accuracy, robustness, and real-time performance of SOC estimation under different operating conditions were enhanced. The impact of factors such as temperature, noise, and aging on estimation accuracy was clarified, providing technical support and methodological references for the engineering applications of Battery Management Systems (BMS).

Innovations

  1. Optimized traditional equivalent circuit models (such as improved RC network structures and the introduction of dynamic parameter correction mechanisms) to more accurately describe the nonlinear characteristics and dynamic responses of lithium batteries, thereby improving the foundational accuracy of the model.
  2. Improved classic SOC estimation algorithms (such as Kalman filtering series and particle filtering) or integrated machine learning methods to reduce the impact of interference factors like temperature, noise, and aging on estimation accuracy, enhancing adaptability under complex conditions.
  3. Focused on low SOC ranges and dynamic charging and discharging in extreme scenarios, proposing targeted estimation strategies to address the accuracy decline of traditional methods under extreme conditions, thus broadening the applicability of the methods.

State of Charge (SOC) Estimation for Lithium BatteriesState of Charge (SOC) Estimation for Lithium BatteriesState of Charge (SOC) Estimation for Lithium BatteriesState of Charge (SOC) Estimation for Lithium BatteriesState of Charge (SOC) Estimation for Lithium BatteriesState of Charge (SOC) Estimation for Lithium BatteriesState of Charge (SOC) Estimation for Lithium Batteries

FuturePossible Directions for Improvement:

  1. Strengthen the analysis of multi-factor coupling effects by establishing a coupling model between temperature, aging degree, charge and discharge rates, and SOC estimation to enhance estimation stability across wide temperature ranges and throughout the entire lifecycle.
  2. Explore low-complexity, high real-time estimation algorithms through lightweight algorithm design (such as model order reduction and parameter simplification) to adapt to the hardware resource limitations of in-vehicle embedded platforms, promoting engineering implementation.
  3. Conduct long-term real vehicle experiments to accumulate measured data from different battery types (such as ternary lithium and lithium iron phosphate) and various usage scenarios, optimizing algorithm parameters and model structures to enhance the generality and reliability of the methods.

Zhang Shuai. State of Charge (SOC) Estimation for Lithium Batteries [D]. Nanjing University of Posts and Telecommunications, 2021.

DOI:10.27251/d.cnki.gnjdc.2021.001076.

2025State of Charge (SOC) Estimation for Lithium BatteriesEmpowering with IntelligenceThe Lyonzine Group is committed to building world-class technological strength. As a comprehensive provider of technical solutions, the company encompasses software development, algorithm design, and technical consulting, among other services. Its primary products include a MES system for smart factories, intelligent product customization solutions, industrial big data mining, and more. When reprinting, please indicate the source.

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