RC Model Research Articles

Finding connection “while everything is going to crap”: experiences in Recovery Colleges during the COVID-19 pandemic

BackgroundRecovery Colleges (RCs) are mental health and well-being education centres where people come together and learn skills that support their wellness. Co-production, co-learning and transformative education are fundamental to RCs. People with lived experience are recognized as experts who partner with health professionals in the design and actualization of educational programming. The pandemic has changed how RCs operate by necessitating a shift from in-person to virtual offerings. Given the relational ethos of RCs, it is important to explore how the experiences of RC members and communities were impacted during this time. To date, there has been limited scholarship on this topic.MethodsIn this exploratory phase of a larger project, we used participatory action research to interview people who were accessing, volunteering and/or working in RCs across Canada. Semi-structured interviews were conducted with twenty-nine individuals who provided insights on what is important to them about RC programming.ResultsOur study was conducted amid the COVID-19 pandemic. Accordingly, participants elucidated how their involvement in RCs was impacted by pandemic related restrictions. The results of this study demonstrate that RC programming is most effective when it: (1) is inclusive; (2) has a “good vibe”; and (3) equips people to live a fuller life.ConclusionsThe pandemic, despite its challenges, has yielded insights into a possible evolution of the RC model that transcends the pandemic-context. In a time of great uncertainty, RCs served as safe spaces where people could redefine, pursue, maintain or recover wellness on their own terms.

Investigating the electrode-electrolyte interface modelling in cochlear implants

Objective. Proposing a good electrode-electrolyte interface (EEI) model and properly identifying relevant parameters may help designing safer and more optimized auditory nerve fiber stimulation and recording in cochlear implants (CI). However, in literature, EEI model parameter values exhibit large variability. We aim to explain some root causes of this variability using the Cole model and its simpler form, the Basic RC model. Approach. We use temporal and spectral methods and fit the models to stimulation pulse voltage response (SPVR) and electrochemical impedance spectroscopy (EIS) data. Main Results. Temporal fittings show that there are multiple sets of model parameters that provide a good fit to the SPVR data. Therefore, small methodological differences in literature may result in different model fits. While these models share similar characteristics at high frequencies >500 Hz, the SPVR fitting is blind to low frequencies, thus it cannot correctly estimate the Faradaic resistor. Similarly, the polarization capacitor and its fractional order are not estimated robustly (capacitor variations in the nano- to micro-farad range) due to limited observation of mid-range frequencies. EIS provides a good model fit down to ∼3Hz, and thus robust estimation for the polarization capacitor. At lower frequencies charge mechanisms may modify the EEI, requiring multi-compartment Cole model fitting to EIS to improve the estimation of Faradaic characteristics. Our EIS data measurements down to 0.05Hz show that a two-compartment Cole model is sufficient to explain the data. Significance. Our study describes the scope and limitation of SPVR and EIS fitting methods, by which literature variability is explained among CI EEI models. The estimation of mid-to-low-frequency characteristics of the CI EEI is not in the scope of the SPVR method. EIS provides a better fit; however, its results should not be extrapolated to unobserved frequencies where new charge transfer mechanisms may emerge at the EEI.

By reconstructing a multifunctional intensive microbiome, effective microorganisms (EM) improve the ecological environment of rice–crayfish cocropping

Integrated rice and aquaculture farming is a high-intensity agricultural model that efficiently utilizes soil and water resources. However, soil ecological issues caused by overexploitation of this model may jeopardize rice production and food security. Given the requirements of aquatic animal breeding circumstances, the application of biological agents has become the preferred choice for improving the environment in this model. In this study, the rice–crayfish cocropping (RC) model was used as the research object, and the widely used bio-fertilizer Effective Microorganisms (EM) was employed as a biological amendment. This study seeks to clarify the improvement effect of EM on the soil and water environment of the RC model as well as to explore its microbiological mechanism. The findings demonstrated that EM significantly improved the chemical properties of the model, for example, the dissolved oxygen in water increased by about 44%, and the redox potential in soil increased by about 150% over the entire rice growing period. Second, the use of EM reduced the soil microbial diversity while reconstructing a more compact and stable microbial cooccurrence network. The reconstructed community selectively enriched the functions related to photosynthesis and nitrogen transformation to cope with the environmental stresses of low dissolved oxygen or high nitrogen. Subsequently, 30 dominant-ASVs (ASV, Amplicon Sequence Variant) with high abundance, strong functionality, positive correlation with environmental factors, and good representation of the community were identified using correlation analysis, abundance comparison, and model prediction. This study not only verified the application effect of EM in a complex agricultural intensive system but also proved that the community induced by EM has a positive effect on ecosystem multifunctionality and identified the key dominant species. This provides strong evidence for us to understand the microbiological mechanisms of exogenous biological agents in environmental improvement and provides a reference for screening and optimizing functional microbial species in agricultural ecosystems.

Research on SOC estimation method of hybrid electric vehicles battery based on the grey wolf optimized particle filter

In this paper, we propose a method called grey wolf optimized particle filter (GWO-PF) to estimate the state of charge (SOC) of the power battery in hybrid electric vehicles (HEVs). The GWO-PF method combines the particle distribution mechanism with grey wolf optimization to achieve accurate SOC estimation. To begin, we compare four different equivalent circuit models of power batteries and select the second-order RC model (RC2 model) as our research focus. Next, we identify the parameters of the RC2 model online using the recursive least square method with a forgetting factor. Finally, we utilize the identified parameters to implement the GWO-PF method for SOC estimation. We compare the performance of the GWO-PF method with two other commonly used methods: the unscented Kalman filter (UKF) and the particle filter (PF). The results demonstrate that the GWO-PF method achieves high precision in SOC estimation, with a controllable relative error within 3.5%.

Design of crosstalk aware energy harvesting system-on-chip

A crosstalk noise aware design methodology is developed, suitable for advanced energy harvesting systems-on-chip (SoCs). The proposed methodology enables accurate substrate crosstalk modeling via process characterization, Python based layout extraction and RC modeling, providing a reliable design flow for robust integrated circuits (IC) implementation. The SoC’s noise coupling performance degradation is accurately estimated, co-calculating the impact of the package and printed-circuit-board (PCB) design. The proposed design flow is seamlessly integrated into the standard design suite. An energy harvesting SoC is used as a product level vehicle. The system is developed in a 0.18 μm CMOS process. The noise aggressor is a digital Maximum Power Point Tracking (MPPT) block, while the noise victim is a DC-DC boost converter and the input voltage sensor. The proposed crosstalk design flow effectively captures the hardware efficiency values, with 14.6% maximum deviation from experimental results, while the standard methodology presents high deviation up to 51.08%.

The Effect of ECC Materials on Seismic Performance of Beam—Column Subassemblies with Slabs

The main objective of this investigation was to study the influence of an Engineered Cementitious Composite (ECC) on the seismic performances of beam–column–slab subassemblies. Tests and simulations were conducted on several models. The bearing capacity of the ECC model was 15% higher than that of the RC member, the deformability increased by 19%, and the energy dissipation capability increased by 34%. The use of an ECC in the slab could reduce the contribution of the reinforced bars in the slab to the flexural strength of the beam. At a drift of 2%, the range of the yielding bars in the slab of the RC models was 5h to 6h. However, the yield range of reinforcement in the slab of the ECC models was nearly 3h. As a result, the ECC subassemblies were prone to reach a “strong column and weak beam” yield mechanism.

State of Charge Estimation for Lithium-Ion Battery Based on Unscented Kalman Filter and Long Short-Term Memory Neural Network

State of charge (SOC) estimation is the core algorithm of the battery management system. However, the commonly used model-based, data-driven, or experiment-based methods struggle to independently achieve accurate SOC estimation under different working conditions and temperatures, which affects battery performance and safety. To this end, this paper proposes an online SOC estimation method that combines the model-driven and double-data-driven approaches. The unscented Kalman filter (UKF) based on the first-order RC model is used to achieve robust SOC estimation, while the data-driven long short-term memory (LSTM) neural network is used to achieve fast SOC estimation. The former model has an excellent dynamic performance and the latter has high steady-state accuracy. The SOC estimation results are input into the SOC estimation model of series LSTM so that the stable but inaccurate SOC values estimated by UKF in the first part and the accurate but fluctuating SOC values estimated by LSTM can be correlated and corrected, achieving a fast and accurate SOC estimation under various working conditions. The estimation results show that the above method has strong robustness and high accuracy, and effectively reduces model complexity and data redundancy. In addition, the root mean square error of SOC estimation under different working conditions is controlled within 1–2.3% at 0 °C, 25 °C, and 45 °C, which is better than the traditional single-SOC estimation method.

Real-time estimating method on rock strength via MWD of roofbolter and its application to in-situ grouting quality evaluation

Grouting reinforcement is an effective method for controlling fractured rock in the deeply buried roadways of coal mines. However, quantitatively evaluating the strength of grouted rock masses remains a challenge. The design of grouting reinforcement is primarily based on empirical knowledge. Consequently, this study proposes a real-time in-situ method for evaluating grouting quality using measure while drilling (MWD) based on a roofbolter. Initially, we validated the method through a numerical study using the discrete element method. Several 3D rock samples with varying crack densities were established and drilled numerically, demonstrating that the strength of fractured rock masses can be better estimated using the rock drillability index rather than drilling specific energy. Therefore, a model, known as the Pd–Rc model, was established to estimate rock strength based on the rock drillability index. Subsequently, an in-situ validation was conducted using a self-developed MWD testing system to assess the grouting quality in fractured rock masses. The results show that the strength of the rock mass increases by 38%, 56.0%, and 49.7%, respectively, after grouting, as observed in three boreholes. This method allows for quantitative estimation of grouting quality.

A Novel Fusion Method for State-of-Charge Estimation of Lithium-Ion Batteries Based on Improved Genetic Algorithm BP and Adaptive Extended Kalman Filter

The lithium-ion battery is the power source of an electric vehicle, so it is of great significance to estimate the state of charge (SOC) of lithium-ion batteries accurately to ensure vehicle safety. To improve the accuracy of the parameters of the equivalent circuit model for batteries, a second-order RC model for ternary Li-ion batteries is established, and the model parameters are identified online based on the forgetting factor recursive least squares (FFRLS) estimator. To improve the accuracy of SOC estimation, a novel fusion method, IGA-BP-AEKF, is proposed. Firstly, an adaptive extended Kalman filter (AEKF) is used to predict the SOC. Then, an optimization method for BP neural networks (BPNNs) based on an improved genetic algorithm (IGA) is proposed, in which pertinent parameters affecting AEKF estimation are utilized for BPNN training. Furthermore, a method with evaluation error compensation for AEKF based on such a trained BPNN is proposed to enhance SOC evaluation precision. The excellent accuracy and stability of the suggested method are confirmed by the experimental data under FUDS working conditions, which indicates that the proposed IGA-BP-EKF algorithm is superior, with the highest error of 0.0119, MAE of 0.0083, and RMSE of 0.0088.

Supercapacitor Constant-Current and Constant-Power Charging and Discharging Comparison under Equal Boundary Conditions for DC Microgrid Application

This work analyzes and compares the differences in efficiency between supercapacitor constant-current and constant-power charging under the same boundary conditions, i.e., charging from the same initial voltage to the same final voltage in equal charging times in both variants. Similarly, supercapacitor constant-current and constant-power discharging are compared under the same boundary conditions, i.e., discharging from the same initial voltage to the same final voltage in equal discharging times for both variants. The study included calculations and virtual simulations based on the most frequently used equivalent RC model of supercapacitors. As a result, theoretical calculations and simulations with the RC model confirmed that constant-current charging/discharging is more efficient than constant-power charging/discharging. The results show that this difference is usually not higher than 1%. Practical experiments confirm an almost equal efficiency of both strategies, but it was difficult to validate exactly such small, theoretically calculated differences. Overall, from a practical point of view, the two charging/discharging methods can be considered as close, and nearly equal in terms of efficiency.

A novel M-1 structured Bidirectional Long Short Term Memory-Rauch Tung Striebel Smoothing algorithm for the joint estimation State of Charge and multi-constrained sustained peak power of lithium-ion batteries

The estimation of State of Charge (SoC) and State of Power (SoP) of lithium-ion batteries is an important part of battery management system. For the purpose of obtaining high-precision SoC and SoP estimation results, this paper proposes a novel M-1 structured Bidirectional Long Short Term Memory-Rauch Tung Striebel Smoothing (BiLSTM-RTSS) algorithm, which can effectively fit current, voltage, temperature and SoC by using the improved sliding window M-1 technique. The RTSS algorithm is applied to update the SoC estimation of the BiLSTM to facilitate the accuracy and speed of SoC estimation. Subsequently, to better estimate the SoP with duration of 30 s, 2 min, and 4 min, three key constraints including SoC estimation results, second-order RC model and battery limits, are simultaneously used in SoP estimation. According to experiment, the mean absolute error of SoC is 0.602, 0.619 and 0.548, 0.469 respectively, under the conditions of HPPC and BBDST. When the duration is 30s, the peak charging and discharging currents up to 170 A and 380 A. In addition, the estimation trends of sustained peak power and peak current are in the same direction. The experimental results verify the rapidity and reliability of the proposed algorithm for SoC and SoP.

An Internal Heating Strategy for Lithium-Ion Batteries Without Lithium Plating Based on Self-Adaptive Alternating Current Pulse

AC pulse heating is a promising preheating method for lithium-ion batteries due to its low energy cost and high efficiency. To avoid the lithium plating in the AC heating, upper bound of heating current (UBHC) should be obtained. In this paper, the dual RC model is developed, and coupled with the thermal model to predict the battery temperature and potential of negative electrode (PNE). PNE=0 V is used as the lithium plating criteria. If the PNE falls to 0 V during the heating process, the lithium plating is triggered, and the corresponding pulse amplitude is the UBHC. Moreover, because the UBHC increases with the battery temperature, the pulse amplitude should be automatically raised to increase the heating efficiency. When the battery temperature is too low, the UBHC is so small that the AC pulse heating is inapplicable. Through the self-adaptive AC pulse based on the UBHC, the heat strategy is built, and the heating time is calculated. The total heating time reached by the proposed strategy increases with the increment of the initial SOC, and it falls with the decrease of the initial battery temperature. Moreover, the heating time has a temperature limit. If the battery temperature is lower than the limit, the battery cannot be heated to the target temperature in the required time. The proposed strategy is applied to heating the battery at the different SOCs, and its performance is compared to the constant amplitude pulse. In the same heating time, the battery temperature achieved by the proposed strategy is at least 7.6 °C higher than the constant amplitude pulse. Moreover, the UBHC is enlarged by 10% to prove the original UBHC can reduce the degradation. The cyclic heating experiment shows that the 110% UBHC reaches a capacity loss of 0.49 Ah after 60 heating cycles and that of the original UBHC is only 0.1 Ah.

Research Progress of Battery Life Prediction Methods Based on Physical Model

Remaining useful life prediction is of great significance for battery safety and maintenance. The remaining useful life prediction method, based on a physical model, has wide applicability and high prediction accuracy, which is the research hotspot of the next generation battery life prediction method. In this study, the prediction methods of battery life were compared and analyzed, and the prediction methods based on the physical model were summarized. The prediction methods were classified according to their different characteristics including the electrochemical model, equivalent circuit model, and empirical model. By analyzing the emphasis of electrochemical process simplification, different electrochemical models were classified including the P2D model, SP model, and electrochemical fusion model. The equivalent circuit model was divided into the Rint model, Thevenin model, PNGV model, and RC model for the change of electronic components in the model. According to the different mathematical expressions of constructing the empirical model, it can be divided into the exponential model, polynomial model, exponential and polynomial mixed model, and capacity degradation model. Through the collocation of different filtering methods, the different efficiency of the models is described in detail. The research progress of various prediction methods as well as the changes and characteristics of traditional models were compared and analyzed, and the future development of battery life prediction methods was prospected.

Critical view of the results of the 2nd international round-robin test on EIS measurements of organic coatings

Although electrochemical impedance spectroscopy (EIS) is a well-established and powerful technique to investigate protective properties of organic coatings, there is no comparative study to show how reliable and reproducible are the results of this technique between different laboratories. One of the tools to fill this gap is international round robin tests (RRT). Following up the conclusions of 1st international RRT, presented in 11th workshop on AETOC Valencia (Spain), this paper will present the results of 2nd international RRT. 30 laboratories have participated this RRT using various equipment (24 different electrochemical systems from 8 manufacturers). To reduce the complexity of the testing in the 2nd RRT only one well defined E-coating material on a single kind of substrate (steel) was used as sample for all participating labs.Results were analyzed in terms of estimated capacitance (RC model, at single frequencies at 1 kHz and 10 kHz, respectively) and magnitude of impedance at low frequencies (100 mHz and 10 mHz). The provided data has been evaluated in a balanced approach using equal statistical weighting for each laboratory. The general conclusion drawn from the tests performed with the coated panel is that the results in terms of repeatability and reproducibility are scattered (high variance). To understand this issue, an additional RRT was organized with 11 participating laboratories (which showed the best results in the E-coating test), using a dummy cell as a test subject. Scattered EIS Bode plots for E-coting as well as for the dummy cell were observed and will be discussed.Although the careful collection and evaluation of the EIS raw data is crucial, it became apparent that this is not always being done. The observed sources of error and measurement artifacts will be presented and discussed. Recommendations for best lab and best evaluation practices will be presented.

Assessing the Limits of Equivalent Circuit Models and Kalman Filters for Estimating the State of Charge: Case of Agricultural Robots

The battery State of Charge (SoC) is critical information to overcome agricultural robots’ limitations related to battery and energy management. Although several SoC estimation methods have been proposed in the literature, the performance of these methods has not been validated for different battery chemistries in agricultural mobile robot applications. Compared to previous work, this paper evaluates the limits of the SoC estimation using the RC model and the Thevenin model for a Lithium Iron Phosphate (LFP) battery and a Sealed Lead Acid (SLA) battery. This evaluation used a custom agricultural robot in a controlled indoor environment. Consequently, this work assessed the limitations of two ECM-based SoC estimation methods using battery packs, low-cost sensors and discharge cycles typically used in agricultural robot applications. Finally, the results indicate that the RC model is not suitable for SoC estimation for LFP battery; however, it achieved a mean absolute error (MAE) of 2.2% for the SLA battery. On the other hand, the Thevenin model performed properly for both chemistries, achieving MAE lower than 1%.

Seismic performance of indoor substation RC frames with combined base isolation techniques

In order to investigate the seismic performance of indoor RC substations with combined base isolation technique under far-field earthquakes and near-fault earthquakes, a 1/4 scaled three-story RC frame model was prepared for shaking table tests. Three different model configurations were considered, namely the non-isolated model (control model), the mono-isolated model (model with lead rubber bearings), and the combined isolated model (model with lead rubber bearings and viscous dampers). Scaled transformers and GISs were also installed on the model floors to consider the influence of the electrical equipment. Typical far field and near fault seismic waves, in this study the El Centro wave and the ChiChi wave, respectively, up to a PGA of 0.44 g were used as excitations. It was found that LRBs worked desirably for the RC model under the El Centro wave while less effective under the ChiChi wave. The acceleration responses (roof), the inter-story shear force (1F) and the inter-story drift (2F) were reduced by use of the LRBs at most by 21%, 51% and 26%, respectively, with respect to the non-isolated model under the El Centro wave. However, under the ChiChi wave irregular torsional behavior was observed and the isolation layer displacement reached 88% of the limit value. Combined use of LRBs and VDs effectively reduced the seismic responses of the RC model under the ChiChi wave. In particular, the isolation layer displacement was reduced by 15% and the torsional displacement ratio was reduced to less than 1.15, with respect to the model with LRBs only. Considerable equipment-structure interaction was found in the RC indoor substations and increased the acceleration and the displacement responses by up to 21% and 22%, respectively, given the position and mass of the equipment in this study. Besides, the LRBs and VDs were found to be effective to reduce the equipment-structure interaction.

State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage analysis method

State of health (SOH) and remaining useful life (RUL) prediction are crucial for battery management systems (BMS). However, accurate SOH and RUL prediction still need to be improved due to the complicated battery aging mechanism. This work combines incremental capacity analysis (ICA) and differential voltage analysis (DVA) based on the second-order RC model with an improved Bidirectional Gated Recurrent Unit (BiGRU) to develop SOH and RUL prediction framework. Firstly, the voltage is reconstructed through the second-order RC model to obtain the incremental capacity (IC) and differential voltage (DV) curves to avoid the influence of measurement noise and the complex parameter adjustment process in the filtering method on the IC and DV curves. Then, a new set of battery aging features are extracted from the reshaped IC and DV curves to improve SOH and RUL prediction accuracy and robustness. Next, the BiGRU method with attention mechanism (BiGRU-AM) is used to build the prediction models for battery aging features, SOH, and RUL. To reduce the impact of the capacity regeneration phenomenon, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is used to decompose the SOH prediction results, and the decomposed residual is used as the input to improve the prediction accuracy of RUL. The uncertainty of RUL prediction results is analyzed by Monte Carlo (MC) simulation. Finally, the proposed method is verified by experimental battery data from Center for Advanced Life Cycle Engineering (CALCE) and Sandia National Laboratory. Experimental results show that the voltage reconstruction results based on the second-order RC model are applied to ICA and DVA analysis, effectively avoiding the influence of noise. The RMSE of voltage reconstruction is within 0.0006, and the Pearson correlation coefficient between the four aging features extracted from the reconstructed IC/DV curve and SOH is above 0.9. Moreover, this method has good robustness to the cell inconsistency, temperature uncertainty, and a satisfied generalization ability to different battery chemistries, which the maximum RUL predicted AE of CALCE and Sandia battery is within 10 and 5, respectively.

A three-time-scale dual extended Kalman filtering for parameter and state estimation of Li-ion battery

Estimation of battery state and parameters play an important role in electric vehicle battery management system (BMS). Second-order RC model is applied, the initial parameters of battery model are determined by experiments. Data points of open circuit voltage and state of charge (OCV-SOC) are determined by experiment. Different function forms are used to fit the OCV-SOC discrete points, and the function form with great fitting effect is selected as the OCV-SOC fitting form. Dual extended Kalman filter which is divided into Parameter filter and state filter is applied. Battery state in state filter is a fast-time-varying parameter, The battery model parameters in parameter filter are divided into two parts. the battery model parameters are classified according to the influence of each parameter on the terminal voltage. A longer sampling time is applied to the parameters that have a strong impact on the terminal voltage, and a longest sampling time is applied to the parameters that have a weak impact on the terminal voltage. The time-scale classification method is validated both quantitatively and qualitatively. Compared with the previous methods, the three-time-scale classification method can reduce the number of parameter updates.

Simulation of substrate coupling for mobile communications SoC – A 20 GHz VCO case study

An innovative substrate coupling simulation methodology with on-the-fly extraction and validation of CMOS substrate parasitic elements, combining boundary element method (BEM) with a computational python based physical layout extraction and RC modeling method, is presented in this work. The proposed methodology is seamlessly integrated into the standard virtuoso based custom circuit design flow and allows the designer to extract fast and accurate a substrate RC mesh, valid from DC to GHz, enabling crosstalk impact estimation and gaining insight on substrate coupling effects from the early stage of the design process until the prototype phase. The proposed substrate crosstalk modeling flow is validated using a 20 GHz VCO circuit as a crosstalk victim. The VCO vehicle is designed using a 65 nm RFCMOS process and the respective RF devices of the specific PDK are used. The PCB transmission line and the package parasitics must be considered as to achieve more accurate and realistic simulation results. The simulation scheme is set using a layout footprint test case and two separate signal injection tap points, each at a certain distance from the VCO victim. This simulation scheme can provide various substrate signal crosstalk scenarios by injecting signals that consist of multiple frequencies into the substrate. Three simulation analysis types, Transient, QPSS and ENVLP, are performed as to be able to designate which analysis is the most suitable for crosstalk simulation in terms of simulation time, accuracy and compatibility. Finally, as a sanity check and methodology validation, crosstalk isolation trends with respect to the aggressor distance and the substrate signal frequency are extracted and compared with the State-of-the-Art results available in literature.

Model parameter identification for lithium-ion batteries using adaptive multi-context cooperatively co-evolutionary parallel differential evolution algorithm

Lithium-ion battery (LIB) has a polarization phenomenon during charging and discharging processes, and the internal chemistry of LIB is characterized by severe time-varying nonlinearity. Therefore, the equivalent circuit model (ECM) is usually used for LIB research. To address the problems of low identification accuracy and local optimization in the offline identification of battery parameters, this paper proposes a novel adaptive multi-context cooperatively co-evolutionary parallel differential evolution (AMCC-PDE) algorithm to identify parameters of LIB. Firstly, the data segment to be identified is divided into a plurality of segments according to the state of charge (SOC), and each segment is called a unit data segment (UDS). In addition, the UDS has a parameter group (PG) for the first-order RC model. Secondly, according to the differential equation of the first-order RC model, the PG (R0+,R0−, D1, R1, U1, init) of each UDS and the open-circuit voltage (UOCV) at each sampling point are considered as variables to be optimized. Then, such an optimization problem is transformed into a large-scale global optimization (LSGO) problem. In addition, to trade off the relationship between population diversity and convergence speed, a novel parallel mutation strategy is proposed. Finally, an AMCC-PDE algorithm is proposed to solve the above LSGO parameters identification model. In both of the DST and FUDS datasets, the identification error at each point obtained by AMCC-PDE is lower than 2 mV, which shows the effectiveness of AMCC-PDE and indicates that the algorithm can avoid destroying the variable-coupling relationship.