Research on battery SOC estimation method by combining optimization algorithm and multi-model Kalman filtering

Authors: zhi ming chen
chang qi zhu
lei  liu
Venue: IEEE_org_ICIST_2024_Conference
Published: 2024-08-15 14:16:54

Abstract: With the rapid growth of electric vehicles and energy storage systems, accurate state of charge (SOC) estimation has become a critical component of battery management systems (BMS), essential for preventing overcharging and over-discharging, enhancing operational safety, and extending battery life. This paper proposes a novel SOC estimation method based on an enhanced self-correcting (ESC) model incorporating a second-order RC circuit, enabling a more accurate simulation of battery response time and dynamic behavior. To improve model reliability, a genetic algorithm-particle swarm optimization (GA-PSO) approach is employed for parameter identification. Additionally, a multi-model adaptive extended Kalman filter (AEKF) algorithm is introduced to achieve precise SOC estimation. MATLAB simulations using constant current discharge and automotive driving cycle data demonstrate that the proposed method outperforms traditional AEKF algorithms, with faster convergence and higher estimation accuracy, particularly in scenarios with varying initial estimation accuracies. The results highlight the potential of this approach to significantly enhance SOC estimation in BMS, contributing to safer operation and prolonged battery life in electric vehicles and energy storage systems.

Main paper: 
 i. introduction: 
 As electric vehicles and energy storage systems continue to develop rapidly, the application of batteries as key energy storage devices has become increasingly widespread, highlighting the growing importance of battery management and control [1] . Within battery management systems (BMS), accurately estimating the state of charge (SOC) is a critical task [2] . Precise SOC estimation not only enables more reliable predictions of vehicle range but also improves battery utilization and helps prevent significant reductions in battery lifespan caused by overcharging or deep discharging [3] . However, the nonlinear characteristics, time-varying behavior, and electrochemical reactions of batteries make it impossible to measure their SOC directly with sensors [4] . Instead, SOC must be estimated using indirect measurements such as voltage, current, and temperature. Common SOC estimation methods include approaches based on open-circuit voltage, coulomb counting, data-driven techniques, and model-based estimation methods [5] . Each of these approaches presents distinct advantages and disadvantages [6] .Among these methods, model-based estimation achieves a reasonable balance between accuracy, real-time performance, and computational cost by integrating the battery equivalent circuit model (ECM) with state estimation algorithms. The ECM is a key component in this approach. Previous studies have advanced SOC estimation using various models and algorithms. Li et al. [7] utilized a second-order RC model with a stochastic gradient algorithm for parameter identification and developed a multi-innovation extended Kalman filter, validated experimentally. Shi et al. [8] employed Bayesian belief networks and adaptive extended Kalman particle filtering, demonstrating enhanced convergence and accuracy.However, these studies largely overlook the hysteresis effect in battery charging and discharging. Gregory L. Plett [9] addressed this by introducing an Enhanced Self-Correcting (ESC) model that incorporates hysteresis into the ECM. Sk Bittu et al. [10] simulated a first-order RC ESC model with an EKF algorithm for SOC estimation but found that the model struggles with complex polarization dynamics, and the EKF's performance deteriorates with significant measurement errors.Accurate SOC estimation requires precise circuit modeling and effective algorithms. This study incorporates the hysteresis phenomenon using an ESC model with second-order RC characteristics. The GA-PSO algorithm is applied for precise identification of battery model parameters via an optimized fitness function. Additionally, a multi-model AEKF is developed, integrating an adaptive factor into the EKF to refine the gain matrix, thereby improving the capture of the model's dynamic properties. This multi-model approach reduces estimation errors and enhances the robustness, accuracy, and stability of SOC estimation. The main contributions of this paper are as follows:1) Battery parameter estimation: An ESC model with second-order RC characteristics is used for accurate characterization, with GA-PSO employed for parameter identification, validated through model testing.2) Multi-model AEKF algorithm for SOC estimation: A multi-model AEKF algorithm is designed, combining adaptive parameters for process noise with a multi-model approach to improve SOC estimation accuracy.3) Simulation comparative analysis: SOC estimation is analyzed using constant current discharge and automotive driving cycle scenarios, comparing the multi-model AEKF with the traditional AEKF, demonstrating enhanced convergence and accuracy..This paper presents an ESC model based on a second-order RC equivalent circuit, incorporating the hysteresis phenomenon observed during battery charging and discharging. The model captures the battery's dynamic behavior, static characteristics, and hysteresis effects, as shown in Fig. 1 . To identify the unknown parameters in the ESC model, the GA-PSO algorithm, an integration of Genetic Algorithm and Particle Swarm Optimization, is utilized. The process initiates with GA generating an initial population of parameter sets, which are subsequently evaluated by comparing the model's predictions with experimental battery data. GA operations, including selection, crossover, and mutation, are employed to refine these parameters, while PSO dynamically adjusts their search direction. After several iterations, the algorithm converges on the optimal parameter set, facilitating precise SOC estimation.Building on the ESC model and parameter identification, a multi-model AEKF framework is developed to enhance SOC estimation. This framework employs multi-model fusion, integrating the estimates from several models to improve filter performance and robustness. The battery SOC is quantized into discrete sets, with n AEKF models constructed. The conditional probability of each SOC is calculated using Bayesian rules, and the SOC with the highest probability is selected for each time step. By using conditional probability as the switching rule, the multi-model AEKF adapts to varying operating conditions and improves SOC estimation accuracy and stability. The Bayesian rule used to compute these conditional probabilities is given by the following formula:1 1 1 1 1 ( , ) ( ) ( ) ( ) ( , ) ( ) ( ) k k i k i i i k N k k i k i i i p y Y s p Y s p s p s Y p y Y s p Y s p s - - - - = = ∑ (1)where ( ) i p s denotes the prior probability, reflecting the initial estimate of the state i s in the absence of any measurement information. The entire expression delineates the posterior probability of each potential state given all previous measurements 1 k Yand the current measurement i s . MATLAB simulations were conducted to model constant current discharge and automotive driving cycle discharge scenarios. A comparative experiment was set up between the traditional AEKF and the multi-model AEKF, focusing on evaluating their convergence performance and accuracy under conditions of unstable initial parameters and complex variations in discharge current.This paper focuses on the estimation performance of SOC in lithium-ion batteries. A second-order RC ESC model is considered, and the battery parameters are identified using the GA-PSO algorithm. Additionally, accurate estimation of battery SOC is achieved through the implementation of a multimodel Adaptive Kalman Filter. To validate the effectiveness of the proposed method, a series of simulation comparisons are conducted. The simulation results demonstrate that the proposed multi-model AEKF algorithm exhibits fast convergence and high estimation accuracy in predicting battery SOC, showcasing its superior performance in SOC estimation. 
 
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Reviews: review: title: title: My Comments for Improvement
main_review: main_review: The paper presents an intriguing methodology combining genetic algorithm-particle swarm optimization (GA-PSO) and multi-model adaptive extended Kalman filter (AEKF) for estimating the state of charge (SOC) of batteries. Here are some suggestions for improvement:

1.	Results from MATLAB simulations are mentioned, demonstrating the effectiveness of the proposed method over traditional AEKF. However, statistical analysis is missing.

2.	It is suggested to add content in the conclusion for future research or potential improvements in the methodology.
title: title: This paper introduces an advanced SOC estimation technique for BMS in electric vehicles and energy storage, combining an ESC model with a second-order RC circuit for more precise battery dynamics simulation. It uses GA-PSO for parameter optimization and an AEKF for accurate SOC estimation, enhancing system safety and battery lifespan. MATLAB simulations show the method's effectiveness, especially with varying initial accuracies. However, the research motivation and innovative effectiveness need more robust support from experiments and theory.
main_review: main_review: This paper presents a novel State of Charge (SOC) estimation approach for battery management systems (BMS) in electric vehicles and energy storage systems, which is pivotal for preventing overcharge and discharge, enhancing safety, and prolonging battery life. The proposed method integrates an Enhanced Self-Correcting (ESC) model with a second-order RC circuit to more accurately simulate the battery's response time and dynamic behavior, mitigating singularity issues without the stringent requirement for n-order differentiability of the model  .To bolster the model's reliability, a hybrid Genetic Algorithm - Particle Swarm Optimization (GA-PSO) is employed for parameter identification, ensuring the model's precision and robustness. Additionally, a multi-model adaptive Extended Kalman Filter (AEKF) is introduced for precise SOC estimation, which is instrumental in achieving the prescribed-time stability for all signals in nonlinear systems .MATLAB simulations, conducted under constant current discharge and vehicle driving cycles, demonstrate the proposed method's superiority over traditional AEKF algorithms. It showcases faster convergence rates and higher estimation accuracy, particularly when the initial estimation accuracy varies, thereby emphasizing the method's potential to significantly elevate the SOC assessment capabilities of BMS .
       However, the motivation for research needs to be further strengthened.
       The effectiveness of innovative points is not supported by experimental results and theoretical research.
title: title: The paper lacks experimental and data support.
main_review: main_review: The paper is too brief, and I haven't seen the experiments and data that support the arguments presented in the paper.