Hybrid Pulse Power Characterization Test Research Articles

Physics-Informed Design of Hybrid Pulse Power Characterization Tests for Rechargeable Batteries

Industry-standard diagnostic methods for rechargeable batteries, such as hybrid pulse power characterization (HPPC) tests for hybrid electric vehicles, provide some indications of state of health (SoH), but lack a physical basis to guide protocol design and identify degradation mechanisms. We develop a physics-based theoretical framework for HPPC tests, which are able to accurately determine specific mechanisms for battery degradation in porous electrode simulations. We show that voltage pulses are generally preferable to current pulses, since voltage-resolved linearization more rapidly quantifies degradation without sacrificing accuracy or allowing significant state changes during the measurement. In addition, asymmetric amounts of information gain between charge /discharge pulses are found from differences in electrode kinetic scales. We demonstrate our approach of physics-informed HPPC on simulated Li-ion batteries with nickel-rich cathodes and graphite anodes. Multivariable optimization by physics-informed HPPC rapidly determines kinetic parameters that correlate with degradation phenomena at the anode, such as solid-electrolyte interphase (SEI) growth and lithium plating, as well as at the cathode, such as oxidation-induced cation disorder. If validated experimentally, standardized voltage protocols for HPPC tests could play a pivotal role in expediting battery SoH assessment and accelerating materials design by providing new electrochemical features for interpretable machine learning of battery degradation.

Development of dual polarization battery model with high accuracy for a lithium-ion battery cell under dynamic driving cycle conditions

Lithium-ion batteries are a key technology for electric vehicles. They are suitable for use in electric vehicles as they provide long range and long life. However, Lithium-ion batteries need to be controlled by a Battery Management System (BMS) to operate safely and efficiently. The BMS continuously controls parameters, such as current, voltage, temperature, state of charge (SoC), and state of health (SoH), and protects the battery against overcharging and discharging, imbalances between cells, and thermal runaways. The battery models and several prediction algorithms that the BMS uses to carry out these checks are essential to the system's performance. This research assesses the Dual Polarization (DP) model's ability to mimic actual battery performance in different dynamic driving conditions. In the study, a battery model for a Lithium–Nickel–Manganese–Cobalt-Oxide (Li-NMC) cell with a nominal capacity of 2 Ah is developed. A DP model was used in the study. Modeling and parameter estimation were performed in MATLAB Simulink/Simscape. Firstly, the model parameters are estimated depending on the SoC using the current and voltage data obtained from the Hybrid Pulse Power Characterization (HPPC) test. A further validation study of the model for low dynamic and high dynamic driving cycles is then presented. Dynamic Stress Test (DST), the US06 Supplemental Federal Test Procedure (SFTP) and Worldwide harmonized Light vehicles Test Procedure (WLTP) cycles were used for model validation. As a result of the study, the model's Root Mean Square (RMS) error values were obtained as 0.0053 V for DST, 0.0059 V for US06, and 0.008 V for WLTP. The obtained model is particularly successful for simulating a battery under dynamic current conditions and for use in control and prediction algorithms.

An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries

The State estimation and determination of time-varying model parameters are crucial for ensuring the safe management of lithium-ion batteries. This paper designs a limited memory recursive least square algorithm to improve the accuracy of online parameter identification. An adaptive radial basis correction-differential support vector machine model is constructed to correct the state of charge value by considering the dynamic characteristic parameters. It greatly reduces estimation error and noise, while monitoring the critical conditions for safe and reliable online battery operation. The estimation effects of the proposed model are verified under hybrid pulse power characterization and dynamic stress test working conditions. The maximum error values obtained are 0.037 % and 0.336 %, respectively, thus achieving high-precision estimation. The proposed method is adaptive to real-time battery management applications, laying a foundation for robust state estimation of lithium-ion batteries used in urban transportation electric vehicles.

High‐precision state of charge estimation of lithium‐ion batteries based on improved particle swarm optimization‐backpropagation neural network‐dual extended Kalman filtering

SummaryHigh‐precision state of charge (SOC) estimation is essential for battery management systems (BMSs). In this paper, a new SOC estimation method is proposed. The dual Kalman filter algorithm and backpropagation neural network (particle swarm optimization ‐ backpropagation neural network ‐ double extended Kalman filter [PSO‐BPNN‐DEKF]) are combined to estimate and correct the SOC of lithium‐ion batteries, in which the initial weight and threshold of the BPNN are optimized by particle swarm optimization algorithm. Based on the second‐order RC equivalent circuit model, parameter identification is carried out using the adaptive forgetting factor least squares (AFFRLS) method. Online parameter updates and SOC estimation are realized by DEKF algorithm. Then, the trained PSO‐BPNN is used to predict the SOC estimation error in real time, and the SOC estimation value is corrected by adding prediction errors. The SOC estimates before and after correction under Beijing Dynamic Stress Test (BBDST), dynamic stress test (DST), and hybrid pulse power characterization (HPPC) were compared. Under BBDST, DST, and HPPC tests, the maximum errors of the corrected SOC estimates are 0.0107, 0.0090, and 0.0147, respectively. The root mean square error (RMSE) of the corrected SOC estimates decreased by 94.02%, 83.18%, and 88.03%, respectively, compared with the extended Kalman filtering (EKF). The mean absolute error (MAE) of the corrected SOC estimates remained around 0.1% for all the BBDST dynamic operating conditions at different temperatures. The experimental results demonstrate the accuracy, effectiveness, and temperature adaptability of the proposed algorithm for SOC estimation under complex conditions of lithium‐ion batteries.

Using Reference Electrodes to Validate Parameterization of Individual Electrode Soh in the Single Particle Model with Electrolyte

Most model parametrization techniques for reduced order physics-based Lithium-ion battery models like the single particle model rely on minimizing the error in the measured and predicted cell terminal voltage. The battery terminal voltage is the difference between the voltages of the positive and negative electrode solid phase potentials. If the individual contribution of each electrode overpotential to the terminal voltage, is not measured, there could be multiple combinations of parameters that provide similar RMSE for voltage prediction, yielding non-unique solutions and local minima. Misattributing the parameters for kinetics and diffusion in each electrode could result in increased plating or over-conservative designs at higher operating currents.Adding a reference electrode to the battery allows us to measure the individual electrode potentials experimentally. Using this extra measurement, we can parametrize the kinetic and diffusion reactions in positive and negative electrodes with better confidence [1], and validate the electrode-specific state of health parameterization, specifically the increased resistances due to SEI growth and shift in the stoichiometric window as the cell ages [2,3]. In our experiments, we used a pouch cell manufactured in the U of M Battery Lab with Targray NMC 622 Single Crystal cathode powder, Superior SLC 1520-T anode powder, and a custom fabricated Lithium Iron Phosphate (LFP) reference electrode deposited on a porous electrode layer. Further details of the reference electrode are given in [4]. The model was parametrized using discharge data at different C-rates and a modified Hybrid Pulse Power Characterization test. Finally, the RMSE for both the modeled negative and positive electrode potentials are calculated for the validation data set which uses the US06 Drive cycle.

HPPC Test Methodology Using LFP Battery Cell Identification Tests as an Example

The aim of this research was to create an accurate simulation model of a lithium-ion battery cell, which will be used in the design process of the traction battery of a fully electric load-hull-dump vehicle. Discharge characteristics tests were used to estimate the actual cell capacity, and hybrid pulse power characterization (HPPC) tests were used to identify the Thevenin equivalent circuit parameters. A detailed description is provided of the methods used to develop the HPPC test results. Particular emphasis was placed on the applied filtration and optimization techniques as well as the assessment of the quality and the applicability of the acquired measurement data. As a result, a simulation model of the battery cell was created. The article gives the full set of parameter values needed to build a fully functional simulation model. Finally, a charge-depleting cycle test was performed to verify the created simulation model.

Determination of half-cell open-circuit potential curve of silicon-graphite in a physics-based model for lithium-ion batteries

Lithium-ion batteries with silicon/graphite anodes have the potential to deliver high theoretical capacity. However, these electrodes exhibit significant hysteresis, which presents challenges in accurately estimating the open-circuit potentials (OCP) of the electrodes within a physics-based model. This paper proposes a method to establish the relationship between the electrode OCP and stoichiometry. Galvanostatic intermittent titration technique (GITT) tests are performed on half-cells to measure the charge and discharge OCP. To account for hysteresis, a hysteresis factor is defined to balance the lithiation and de-lithiation OCP. The estimated open-circuit voltage (OCV) of the full-cell is obtained by subtracting the anode OCP from the cathode OCP. The OCP and hysteresis factor are then optimized by minimizing the error between the measured OCV and the estimated OCV. Two different OCV test methods, namely the incremental method and C/30 galvanostatic method, are compared. The OCV estimation for fresh cells shows good agreement with experimental values, with root-mean-square errors (RMSEs) below 6.682 mV. To evaluate the effectiveness of the obtained OCPs in the full-cell model, the optimized OCPs are incorporated into the physics-based model. Under the Hybrid Pulse Power Characterization (HPPC) test, the electrochemical model utilizing the optimized OCP with the incremental OCV and C/30 galvanostatic OCV exhibits RMSEs of 10.587 mV and 11.016 mV, respectively, in predicting the cell voltage. Finally, the OCP identification method is assessed with cells at different aging states. The OCV predictions for degraded cells maintain RMSEs below 9.074 mV, thus validating the effectiveness of the developed OCP estimation method.

State of charge estimation for the vanadium redox flow battery based on the Sage–Husa adaptive extended Kalman filter

SummaryFocusing on improving the accuracy of vanadium redox flow battery (VRFB) state of charge (SOC) estimation, this paper will combine the extended Kalman filter (EKF) estimator with the Sage–Husa adaptive method, referred to as the SAEKF estimator. Firstly, a second‐order Equivalent Circuit Model (ECM) is established to provide the correct parameterization and degrees of freedom. Next, under a hybrid pulse power characterization (HPPC) text, the parameter estimator in MATLAB/Simulink is utilized to identify the ECM parameters by using nonlinear least squares method with Trust‐Region‐Reflective (TRR) iterative algorithm. Then, during the HPPC test, the estimators implemented on the ARM‐Cortex STM32F103 estimate the VRFB SOC under both the 0°C and 5°C conditions. Finally, the mean absolute error (MAE) of the IEKF estimator are 4.66% at the 0°C and 0.895% at the 5°C. In the meantime, the MAE of the SAEKF estimator are 4.15% at the 0°C and 0.703% at the 5°C. The evaluation factors of the 0°C and 5°C experiments in the SAEKF estimator are smaller than those of the IEKF and EKF estimators, indicating that the SAEKF estimator outperforms the IEKF estimator in VRFB SOC estimation.

Comparison and Analysis of SOC Estimation Based on First-Order and Second-Order Thevenin Battery Models Based on EKF

The accurate modeling and estimation of state-of-charge (SOC) of lithium-ion batteries are of great significance for their utilization efficiency, service life extension and battery management system (BMS) design. This paper aims to provide understanding and guidance for understanding and guidance on different battery models and their parameter selection by comparing and analyzing the performance differences of Extended Kalman Filter (EKF) algorithm based on the first-order Thevenin battery model and the second-order Thevenin battery model. The Battery module in Matlab/Simulink is used to simulate the HPPC (HybridPulse Power Characterization) test of real batteries, and the model is parameterically identified. By establishing Thevenin battery models of different orders and applying the EKF algorithm for SOC estimation, the differences in accuracy, robustness and computational complexity of the two models are compared and analyzed, and experimental verification is carried out under different working conditions.

Adaptive state of charge estimation for lithium‐ion batteries using feedback‐based extended Kalman filter

AbstractThe battery management system (BMS) is a crucial component of electric vehicles (EVs) owing to its sustainable operation. To ensure optimal performance of the BMS, state of charge (SOC) of the equipped battery is required to be effectively and accurately estimated. In this paper, the authors consider high‐order equivalent circuit model (ECM) to capture the dynamic characteristics of lithium‐ion batteries, which are connected in series with internal resistance by 2‐RC networks. The parameters of the RC network are determined by mathematically solving the working conditions of the two states. Moreover, the parameters of the battery can be derived by hybrid pulse power characterization (HPPC) tests. Then, based on the open‐circuit voltage, the proposed feedback‐based extended Kalman filtering (FEKF) algorithm is established. The parameters from the simulation have shown that the highest error is 0.0306 V, the optimal knowledge of which can improve the SOC estimation approach remarkably and can provide a reference value. Afterwards, the non‐linear predicting and corrective techniques are applied to the experiment in the extended calculation process. The original error is reduced by the FEKF algorithm, where the maximum and average errors are 0.0298 and 0.0240 V, respectively. Consequently, the established high‐order ECM utilizing the FEKF algorithm may provide SOC estimation with an error of 1.5% or less, resulting in superb performance from the lithium‐ion battery pack.

An Improved Comprehensive Learning - Particle Swarm Optimization - Extended Kalman Filtering Method for the Online High-Precision State of Charge and Model Parameter Co-Estimation of Lithium-Ion Batteries

The precise assessment of the state of charge (SOC) of lithium-ion batteries (LIBs) is critical in battery management systems. This work offers a comprehensive learning particle swarm optimization (CLPSO) and extended Kalman filter (EKF) technique to forecast the SOC of LIBs in order to obtain an accurate SOC estimate for power batteries. Firstly, to address the challenge of identifying various parameters of the battery model, the bilinear transformation technique is employed to determine the parameters of the second-order RC equivalent circuit model. Secondly, to improve the fitness values for the conventional PSO algorithm, which is prone to entering local optimality, a learning strategy () is added to the particle velocity update method. The optimized PSO and EKF algorithms are integrated to perform online prediction of the SOC of LIBs. The experimental results demonstrate that under the conditions of the Beijing Bus Dynamic Stress Test (BBDST), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization Test (HPPC), the parameter identification inaccuracy of CLPSO is restricted to 1%. After multi-metric evaluation, the maximum error and mean absolute error of the CLPSO-EKF algorithm in SOC estimation are 0.32% and 0.0652%, respectively, demonstrating a higher robustness and accuracy advantage over other versions.

PSO-Based Identification of the Li-Ion Battery Cell Parameters

The article describes the results of research aimed at identifying the parameters of the equivalent circuit of a lithium-ion battery cell, based on the results of HPPC (hybrid pulse power characterization) tests. The OCV (open circuit voltage) characteristic was determined, which was approximated using functions of various types, while making their comparison. The internal impedance of the cell was also identified in the form of a Thevenin RC circuit with one or two time constants. For this purpose, the HPPC pulse transients were approximated with a multi-exponential function. All of the mentioned approximations were carried out using an original method developed for this purpose, based on the PSO (particle swarm optimization) algorithm. As a result of the optimization experiments, the optimal configuration of the PSO algorithm was found. Three different cognition methods have been analyzed here: GB (global best), LB (local best), and FIPS (fully informed particle swarm). Three different swarm topologies were used: ring lattice, von Neumann, and FDR (fitness distance ratio). The choice of the cognition factor value was also analyzed, in order to provide a proper PSO convergence. The identified parameters of the cell model were used to build simulation models. Finally, the simulation results were compared with the results of the laboratory CDC (charge depleting cycle) test.

Co-estimation of state of charge and internal temperature of pouch lithium battery based on multi-parameter time-varying electrothermal coupling model

Accurate estimation of battery state of charge (SOC) and internal temperature can improve battery performance and safety. In this process, the accuracy of the battery's electrical model and thermal model and the applicability and robustness of the estimation algorithm are key. In this paper, a multi-parameter time-varying electrothermal coupling model for pouch batteries is established. The model takes into account the coupling relationship between the battery SOC and temperature changes, as well as the heat exchange between multiple surfaces of the battery and the external environment. And then through the improved hybrid pulse power characterization (HPPC) test to realize the identification of model parameters, and the changing external heat transfer conditions could be identified online. At last, an adaptive strong tracking square root cubature Kalman filter (AST-SRCKF) algorithm was designed to estimate SOC and internal temperature simultaneously. The results were verified by three dynamic conditions. The conclusion is that the AST-SRCKF has high robustness and can be quickly corrected when the initial values of SOC and temperature are unknown. And the estimation accuracy is higher than extended Kalman filter (EKF) and dual Kalman filter (DEKF). Furthermore, the simulation efficiency is improved by more than 6 % compared with EKF, and by more than 11 % compared with DEKF, which is more conducive to online real vehicle applications.

Measurement of thermophysical parameters and thermal modeling of 21,700 cylindrical battery

There is temperature unevenness inside the operating battery, and the internal temperature distribution of the battery has gradually attracted attention. To establish a thermal model of the 21,700 cylindrical battery that can reflect the internal temperature distribution, thermophysical parameters including anisotropic thermal conductivity and specific heat capacity are tested through experiments. The thermal model includes the heat generation part and heat transfer part. Thermophysical parameters are essential for the heat transfer part. Hybrid pulse power characterization (HPPC) tests are carried out to obtain battery internal resistance, and the entropy coefficient of the battery is also obtained by tests. Internal resistance and entropy coefficient data are used as inputs for the battery heat generation part. The core temperature and surface temperature of the operating battery are measured by the thermocouple embedded inside the battery and the thermocouple pasted on the battery surface. The calculated core and surface temperatures from the battery thermal model are both in good agreement with the measured temperature results. The maximum core temperature of 2C discharging battery is about 60 °C and the maximum temperature difference inside the 21,700 battery reaches 3 °C. The established simulation model can be used for extended research work. When heat dissipation is enhanced by increasing the convection heat transfer coefficient, the overall temperature of the battery decreases but the temperature non-uniformity inside the battery is aggravated.

A semiparametric clustering method for the screening of retired Li-ion batteries from electric vehicles

This study proposes a semiparametric clustering method (SPCM) to screen the batteries retired from electric vehicles (EVs) for echelon utilization. To quickly capture the static and dynamic battery characteristics, a hybrid test combining the low-current hybrid pulse power characterization (L-HPPC) test and China light-duty vehicle test cycle (CLTC) is designed. The pseudo-two-dimensional (P2D) model is combined with genetic algorithm (GA), and model parameters are identified by the hybrid test. Then, a clustering matrix containing sensitive model parameters is established and processed by principal component analysis (PCA). In SPCM, the Euclidean-distance-based clustering matrix is divided into high-density and low-density parts through a predetermined mixing proportion. For high-density data, hierarchical clustering (HC) is used to determine the initial cluster centers, and fuzzy C-means (FCM) method is utilized to determine the core clusters, while the low-density data is assigned to the nearest neighbours. Finally, 18 open-access datasets are employed to validate the effectiveness of the proposed SPCM. These results indicate that the proposed SPCM is superior to K-mean, K-medoid, and FCM in terms of normalized mutual information (NMI). Furthermore, the pulse testing and capacity testing of the re-grouped retired batteries further demonstrate the effectiveness of the SPCM in the screening of retired batteries.

Online Model Parameter and State of Charge Estimation of Li-Ion Battery Using Unscented Kalman Filter Considering Effects of Temperatures and C-Rates

This paper proposed a technique for online modeling and state of charge (SoC) estimation of Li-ion batteries considering the effects of operating temperatures and C-rates. The proposed battery model, which is updated in real-time, is incorporated with the unscented Kalman filter (UKF) for accurate and online SoC estimation. In the proposed technique, Thevenin-based equivalent circuit model of Li-ion battery is used. Each of the RC networks of the Thevenin model is represented as a first-order linear time-invariant (LTI) system. The offline RC parameters are extracted as the parameters of the first-order LTI system using the final value theorem. Then the offline RC parameters are converted to the online model parameters compensating the effects of operating temperatures and C-rates. Finally, the UKF technique is augmented with the proposed online battery model for higher accurate and online SoC estimation. An experimental setup has been developed in the LabVIEW platform to validate the proposed battery model and apply it for online SoC estimation. The proposed online battery model and UKF are developed in Matlab and then integrated with the LabVIEW program using the Matlab-LabVIEW interface called Math Script. The proposed technique has been rigorously validated in the laboratory under hybrid pulse power characterization (HPPC) and federal urban driving schedule (FUDS) tests under wide range of varying operating temperatures and C-rates. In the HPPC test, the SoC estimated using the proposed technique has a maximum absolute relative percentage error (ARPE) of less than 2%, whereas the battery model that does not account for temperature and C-rate effects has a maximum ARPE of 8.79 percent, which is 4.30 times higher than the proposed technique. The proposed approach yielded mean ARPE of 0.30%, 1.02%, and 0.81% in the FUDS test at 25 °C, 0 °C, and 40 °C, respectively, which is performed in dynamic identical C-rates. Furthermore, higher accuracy and effectiveness of the proposed technique have been validated under the presence of white and coloured measurement noises.

Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile

This paper investigates the model development of the state-of-power (SoP) estimation for a 43 Ah large-capacity prismatic nickel-manganese-cobalt oxide (NMC) based lithium-ion cell with a thorough aging investigation of the cells’ internal resistance increase. For a safe operation of the vehicle system, a battery management system (BMS) integrated with SoP estimation functions is crucial. In this study, the developed SoP model used for the estimation of power throughout the lifetime of the cell is coupled with a dual-polarization equivalent-circuit model (DP_ECM) for achieving the precise estimation of desired parameters. The SoP model is developed based on the pulse-trained internal resistance evolution approach, and hence the power is estimated by determining the rate of internal resistance increase. Hybrid pulse power characterization (HPPC) test results are used for extraction of the impedance parameters. In the DP_ECM, Coulomb counting and extended Kalman filter (EKF) state estimation methods are developed for the accurate estimation of the state of charge (SoC) of the cell. The SoP model validation is performed by using both dynamic Worldwide harmonized Light vehicles Test Cycles (WLTC) and static current profiles, achieving promising results with root-mean-square errors (RMSE) of 2% and 1%, respectively.

Parameter Identification of Lithium Battery Model Based on Chaotic Quantum Sparrow Search Algorithm

An accurate battery model is of great importance for battery state estimation. This study considers the parameter identification of a fractional-order model (FOM) of the battery, which can more realistically describe the reaction process of the cell and provide more precise predictions. Firstly, an improved sparrow search algorithm combined with the Tent chaotic mapping, quantum behavior strategy and Gaussian variation is proposed to regulate the early population quality, enhance its global search ability and avoid trapping into local optima. The effectiveness and superiority are verified by comparing the proposed chaotic quantum sparrow search algorithm (CQSSA) with the particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization algorithm (GWO), Dingo optimization algorithm (DOA) and sparrow search algorithm (SSA) on benchmark functions. Secondly, the parameters of the FOM battery model are identified using six algorithms under the hybrid pulse power characterization (HPPC) test. Compared with SSA, CQSSA has 4.3%, 5.9% and 11.5% improvement in mean absolute error (MAE), root mean square error (RMSE) and maximum absolute error (MaAE), respectively. Furthermore, these parameters are used in the pulsed discharge test (PULSE) and urban dynamometer driving schedule (UDDS) test to verify the adaptability of the proposed algorithm. Simulation results show that the model parameters identified by the CQSSA algorithm perform well in terms of the MAE, RMSE and MaAE of the terminal voltages under all three different tests, demonstrating the high accuracy and good adaptability of the proposed algorithm.

An improved smoothing factor-extended kalman filtering method for accurate online state-of-charge estimation of Lithium-ion battery

The state of charge of battery is an important index of battery management system. The Lithium-ion batteries are widely used in various industries, so they are full of uncertainty, which makes them difficult to estimate the state of charge of Lithium-ion batteries. To solve the problem of low accuracy and large variability in real-time estimation of Lithium-ion batteries, taking Lithium-ion batteries as the research object, the Thevenin model is used to simulate the working characteristics. On the basis of the extended Kalman filtering algorithm, through the influence of the covariance matrix and noise, a smoothing factor is introduced to increase the Kalman gain and improve flexibility. Experiments have proved that the smoothing factor-extended Kalman algorithm improves the flexibility of the algorithm, and at the same time reduces the non-linear error caused by the rapid charging and discharging changes of Lithium-ion batteries. In the Hybrid Pulse Power Characterization test, the maximum estimation error is 0.14%, and the average estimation error is 0.1%. It provides a new method for estimating the state of charge of Lithium-ion batteries.

A State-of-Charge Estimation Method Based on Multi-Algorithm Fusion

Lithium-ion power batteries are widely used in the electric vehicle (EV) industry due to their high working voltage, high energy density, long cycle life, low self-discharge rate, and environmental protection. A multi-algorithm fusion method is proposed in this paper to estimate the battery state of charge (SOC), establishing the Thevenin model and collecting the terminal voltage residuals when the extended Kalman filter (EKF), adaptive extended Kalman filter (AEKF), and H infinite filter (HIF) estimate the SOC separately. The residuals are fused by Bayesian probability and the weight is obtained, and then the SOC estimated value of the fusion algorithm is obtained from the weight. A comparative analysis of the estimation accuracy of a single algorithm and a fusion algorithm under two different working conditions is made. Experimental results show that the fusion algorithm is more robust in the whole process of SOC estimation, and its estimation accuracy is better than the EKF algorithm. The estimation result for the fusion algorithm under a Dynamic Stress Test (DST) is better than that under a Hybrid Pulse Power Characterization (HPPC) test. With the emergence of cloud batteries, the fusion algorithm is expected to realize real vehicle online application.