State Of Charge Level Research Articles

A Novel Fitting Polynomial Approach for An Accurate Soc Estimation In Li-Ion Batteries Considering Temperature Hysteresis

Lithium-ion batteries are essential to modern technology, requiring accurate estimation of the state of charge (SOC) for optimal performance. Traditional methods such as Coulomb Counting (CC) are ineffective in the face of temperature variations, leading to inaccuracies in SOC estimation, which in turn cause obvious deformation of hysteresis curves. To address this, this paper introduces a novel method called Polynomial Fit State of Charge (FPSOC), for effective SOC estimation. This method incorporates a fifth-degree polynomial fitting that accounts for a wide range of temperature variations (from -10°C to +80°C), a feature that, according to the authors, has not been offered by all previously published methods. A series of simulation tests using the MATLAB/Simulink tool are conducted under various temperature profiles to evaluate the effectiveness of the FPSOC method. The results demonstrate the notable superiority of the FPSOC model compared to the CC method, with a significantly reduced RMSE of only 0.93% compared to 6.77% of the CC model. Particularly effective at low SOC levels (30%), the FPSOC model demonstrates precision up to six times higher compared to the CC model. Additionally, when evaluated against other recent SOC estimation techniques such as CM, RLSF, EKF, DST, BBDST, ASMO, LPM_H, LSTM-SA Group A and B, and baseline ECM-ID, The FPSOC method proves extremely accurate, with the lowest average error under different temperature conditions.

Generative learning assisted state-of-health estimation for sustainable battery recycling with random retirement conditions

Rapid and accurate state of health (SOH) estimation of retired batteries is a crucial pretreatment for reuse and recycling. However, data-driven methods require exhaustive data curation under random SOH and state of charge (SOC) retirement conditions. Here, we show that the generative learning-assisted SOH estimation is promising in alleviating data scarcity and heterogeneity challenges, validated through a pulse injection dataset of 2700 retired lithium-ion battery samples, covering 3 cathode material types, 3 physical formats, 4 capacity designs, and 4 historical usages with 10 SOC levels. Using generated data, a regressor realizes accurate SOH estimations, with mean absolute percentage errors below 6% under unseen SOC. We predict that assuming uniform deployment of the proposed technique, this would save 4.9 billion USD in electricity costs and 35.8 billion kg CO2 emissions by mitigating data curation costs for a 2030 worldwide battery retirement scenario. This paper highlights exploiting limited data for exploring extended data space using generative methods, given data can be time-consuming, expensive, and polluting to retrieve for many estimation and predictive tasks.

Investigation of battery safety states based on thermal propagation and expansion analysis: Experimental studies on different packaging forms

Lithium-ion batteries are highly susceptible to thermal runaway under harsh conditions, posing significant safety risks for electric vehicles. The differences in thermal runaway propagation across battery systems with varying manufacturing methods (including packaging and internal stacking) and the study of changes in expansion force within battery modules are crucial for improving safety and developing early warning systems for battery systems. This study investigates the thermal runaway and propagation behavior of lithium-ion batteries under various conditions, focusing on state of charge (SOC), internal manufacturing processes (electrode stacking and winding), and packaging types (pouch and prismatic) through a series of novel experiments. The results show that batteries at high SOC levels are at a greater risk of thermal runaway propagation, with winding pouch cells displaying more severe runaway behavior. In contrast, prismatic cells, despite experiencing intense explosions with an equivalent force of up to 42.3 g of TNT, exhibit relatively milder propagation. Furthermore, the study incorporates heat transfer analysis during thermal runaway propagation, identifying key phenomena such as the formation of negative pressure zones before explosions and changes in expansion forces at the module level. Based on these findings, a new safety evaluation method is proposed to assess the risk, hazards, and severity of thermal runaway, offering valuable insights for enhancing battery safety management and fire prevention strategies.

Impact of external heating and state of charge on discharge performance and thermal runaway risk in 21700 Li-ion batteries

This study investigates the combined effects of external heating and State of Charge (SOC) on the discharge performance and thermal runaway risk of 21700 Li-ion batteries. Experiments subject commercially available 21700 cylindrical cells to various external heating conditions at different SOC levels (20, 40, 60, 80, and 100 %) while measuring discharge performance and thermal behavior at different discharge rates (0.2, 0.5, 1, and 1.5C). The study emphasizes the significance of Li-ions' spatial arrangement and maintaining electrical charge in the electrolyte to evaluate battery performance. Numerical simulations are then conducted to complement the experimental data and provide deeper insights. The results reveal a significant degradation in discharge performance with increasing external heat, characterized by voltage drops and reduced capacity. Cells with higher SOC levels exhibit more severe exothermic reactions during external heating, leading to a higher likelihood of thermal runaway. Cells operated at a moderate SOC of 40 % demonstrate a significantly lower risk of thermal runaway under similar heating conditions. The rapid rise in temperature at 100 % SOC of batteries showed a sharp increase of over 20 °C per second. Temperature spikes were noted at specific time intervals corresponding to different SOC levels: 100, 80, 60, 40, and 20. At this critical point, temperatures ranged from 135 to 182 °C, indicating potential thermal issues from internal short circuits causing separator melting. These findings highlight the critical role of SOC management in ensuring the safe and reliable operation of 21700 Li-ion batteries, particularly in applications involving elevated temperatures.

State of Charge Estimation for Lithium-Ion Batteries Using Optimized Model Based on Optimal HPPC Conditions Created Using Taguchi Method and Multi-Objective Optimization

This study proposes a comprehensive methodology for accurate State of Charge (SOC) estimation in lithium-ion batteries by optimizing equivalent circuit model (ECM) parameters under varying temperature conditions using the Taguchi method. Analysis of Variance (ANOVA) was employed to evaluate the influence of these parameters on ECM accuracy. Experiments were conducted at −10 °C, 25 °C, and 40 °C to evaluate the effects of pulse time gap, discharge pulse time, and C-rate on SOC estimation accuracy. A genetic algorithm-based multi-objective optimization technique was employed to minimize RMSE in the extended Kalman filter (EKF) SOC estimation process. The results showed that temperature significantly impacts SOC prediction, with deviations most pronounced at low (−10 °C) and high (40 °C) temperatures. When assessments are conducted for different SOC levels (SOC90, SOC50, SOC30), the key results highlight the substantial influence of pulse time gap and discharge pulse time on model accuracy. Also, it was observed that there is a significant reduction in RMSE, indicating improved performance under optimized conditions. The findings are particularly relevant for real-time applications, such as electric vehicles, where accurate SOC estimation is crucial for battery management.

Modeling and Characterization of Li-Ion 18650 Nickel–Cobalt–Alumina Battery Jellyroll Subjected to Static and Dynamic Compression Loading

This study presents a comprehensive experimental investigation of the mechanical response of the jellyroll and complete Li-ion 18650 Nickel–Cobalt–Alumina (NCA) battery under axial compression, highlighting the effects of strain rate and state-of-charge (SOC). The jellyroll was subjected to both static (1 mm/min) and dynamic (10–30 m/s) axial compression using a Split-Hopkinson Pressure Bar (SHPB). A key innovation of this work is the investigation of the role of electrolytes under both static and dynamic conditions, revealing their significant impact on stress and strain behavior due to hydrostatic pressure. Additionally, the complete NCA battery was tested under various SOC levels (0–75%) using flat plate compression. The results demonstrate the jellyroll’s sensitivity to strain rate, with increased stress responses at higher loading speeds. Furthermore, the inclusion of electrolytes markedly amplified the stress and strain response. The Fu-Chang model was successfully employed to numerically replicate the observed static and dynamic behaviors. Critically, the full battery tests revealed a negative correlation between voltage cutoff and SOC, with the risk of fire and explosion increasing at higher SOC levels. This research provides novel insights into the safety and mechanical resilience of Li-ion batteries under compression.

Deployment of Public Charging Stations for BEVs Using an Agent-Based Modeling Approach

A low utilization rate of public chargers and unmatched deployment of public charging stations (CSs) are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation. This study proposes an optimization methodology for public CS deployment, considering real charging behavior and interactions between battery electric vehicle (BEV) users and CSs. Realistic charging choice behavior is modeled based on surveys, and a dynamic charging decision chain is simulated, allowing interactions between BEV users and CSs through an agent-based modeling (ABM) approach. The charging-related activities are triggered by state of charge (SOC) levels randomly generated from distributions derived from real BEV operating data, including the random SOC levels at the start of a trip, the SOC level that prompts the user to charge the BEV, and the SOC level at which the user stops charging the BEV. A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs, to determine the location and the capacity of new CSs. The objective is to minimize the total time cost per BEV user, including travel time, charging time and waiting time in the queue. An application is conducted, for the deployment of fast CSs in Washington State, USA. The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems. The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs and leads to a more balanced utilization among CSs. The proposed approach is expected to contribute to better planning and deployment of public CSs, satisfaction of the booming charging demand and increased utilization of public CSs.

Data-driven state-of-charge estimation of a lithium-ion battery pack in electric vehicles based on real-world driving data

State-of-charge (SOC) is one of the most important indicators in the battery management system (BMS) of electric vehicles (EVs). Accurate online estimation of battery pack SOC is essential for ensuring safety by preventing overcharging and overdischarging, as well as for facilitating proper operation and efficient energy utilization. This paper proposes a deep learning model, CNN-BiLSTM-Attention, to drive the battery pack SOC estimation by datasets collected from actual operating EVs. Multivariate time series (MVTS) signals such as vehicle speed, battery voltage, current and temperature are carefully selected as inputs to the model. Additionally, a three-parameter Weibull probability model is used to model the terminal voltage distribution of all cells within the investigated battery pack, and the evolution of cell terminal voltage inconsistency concerning depth of discharge (DOD) is described from the perspective of dispersion and symmetry. By adding these distribution features to the input dimension of the CNN-BiLSTM-Attention model, the estimation accuracy of battery pack SOC has been further improved. Experimental results indicate that when the window length (WL) is set to 25 sampling time steps and the window overlap rate is >72 %, the SOC estimation error is <3 % and the root mean square error (RMSE) is <1.12 % within the maximum DOD variation range experienced by the battery pack. Especially when the battery pack is in the discharge platform period of 30 %–100 % SOC, the SOC estimation error is reduced to <2 %. After adding the Weibull distribution features that reflect the cell inconsistency evolution into the model's input dimensions, the overall SOC estimation RMSE is reduced by about 31 %, especially by about 51 % at lower SOC levels (below 31 %).

Droop control based energy management of distributed batteries using hybrid approach

In this manuscript proposes a hybrid SO-CCG-DLNN approach for a droop control based Battery Storage System (BSS). The proposed hybrid approach is combination of both Cascade Correlation Growing Deep Learning Neural Network (CCG-DLNN) and Snake Optimizer (SO). Commonly it is named as SO-CCG-DLNN approach. Micro grid (MG) the use of hybrid renewable energy systems is an advanced and useful technology for the growth of sustainability that is environmentally beneficial. An MG mainly consists of Photovoltaic (PV), wind, and BSS. The primary goal of this study is to control the State of Charge (SoC) and improve the power efficiency of the battery. The droop manages balance and electricity from the batteries provided in the DC MG. The sources of renewable energy projection for electricity consumption and the uncertainty were combined. Obtain the optimal energy management solutions by using SO optimizer and CCG-DLNN to predict the SoC level and manage how the dispersed batteries are charged and discharged. The proposed model is implemented in MATLAB/Simulink platform and contrasted with several existing methods like Wild Horse Optimizer (WHO), Heap-Based Optimizer (HBO) and Particle Swam Algorithm (PSO).

Research on Interior Design of New Energy Vehicles under the Concept of Information Materialization

This research focuses on the interior designing of electric vehicles (EVs), considered as new energy vehicles (NEVs), within the information materialization framework. The idea of information materialization refers to the quantifiable demonstrations of given data within the vehicle interior space, enhancing user interactions and experiences. Considering the State of Charge (SoC) as the crucial designing parameter, the research investigates the several implications of SoC on NEV interior designing. SoC, a critical parameter that reflects the battery's charging level, profoundly influence the merging of SoC data into the vehicle's dashboard or infotainment systems, ensuring seamless accessibility and intuitive interpretations for the driver. Instant alerts and notifications are designed based on SoC levels to provide real-time feedback and guidance to end users, optimizing driving ranges and strategies for charging. Furthermore, the study emphasizes the significance of customization options in SoC displaying formats and accommodates varying users preferences and requirements of visibility. Analysis of empirical and feedback from users, the research confirms the effectiveness of SoC-driven designing intervention in enhancing user experiences, safety, and overall functionalities interiors of NEV. The findings underscore the pivotal role of SoC as a fundamental designing parameter in shaping the next generations of NEV interiors and highlight the need for complete consideration of energy management aspects in future vehicle design endeavors.

Relating state-of-charge to impedance and equivalent circuit parameters in lithium-ion cells: An experimental study

The wide-band impedance of Lithium-ion (Li-ion) batteries has become a focal point for researchers interested in State-of-Charge (SOC) estimation and battery modeling. However, the interplay between SOC, wide-band impedance, and model parameters has not been extensively explored. This paper addresses this gap by investigating how SOC impacts the impedance and Equivalent Circuit Model (ECM) parameters of a Li-ion battery cell through empirical measurements. The study utilizes discharge tests to vary the SOC and employs the Coulomb Counting Method (CCM) to estimate the approximate SOC from the response signals. A novel measurement technique is introduced and applied to determine a cell’s wide-band impedance at different SOC levels. This innovative approach uses a Pseudo-random Impulse Sequence (PRIS) perturbation signal to measure wide-band cell impedance. The collected impedance data is then used to estimate the ECM parameters. Experimental findings reveal an inverse relationship between impedance and SOC. Additionally, the ECM parameters exhibit significant correlation with SOC, particularly in mid- to low-frequency regions. This research highlights that SOC profoundly influences ECM parameters, offering valuable insights into the performance of Li-ion batteries under various conditions.

Novel Ordinary Differential Equation for State-of-Charge Simulation of Rechargeable Lithium-Ion Battery

Lithium-ion battery energy storage systems are rapidly gaining widespread adoption in power systems across the globe. This trend is primarily driven by their recognition as a key enabler for reducing carbon emissions, advancing digitalization, and making electricity grids more accessible to a broader population. In the present study, we investigated the dynamic behavior of lithium-ion batteries during the charging and discharging processes, with a focus on the impact of terminal voltages and rate parameters on the state of charge (SOC). Through modeling and simulations, the results show that higher terminal charging voltages lead to a faster SOC increase, making them advantageous for applications requiring rapid charging. However, large values of voltage-sensitive coefficients and energy transfer coefficients were found to have drawbacks, including increased battery degradation, overheating, and wasted energy. Moreover, practical considerations highlighted the trade-off between fast charging and time efficiency, with charging times ranging from 8 to 16 min for different rates and SOC levels. On the discharging side, we found that varying the terminal discharging voltage allowed for controlled discharging rates and adjustments to SOC levels. Lower sensitivity coefficients resulted in more stable voltage during discharging, which is beneficial for applications requiring a steady power supply. However, high discharging rates and sensitivity coefficients led to over-discharging, reducing battery life and causing damage. These new findings could provide valuable insights for optimizing the performance of lithium-ion batteries in various applications.

Self-Diagnostic Opportunities for Battery Systems in Electric and Hybrid Vehicles

The number of battery systems is also growing significantly along with the rise in electric and hybrid car sales. Different vehicles use different types and numbers of batteries. Furthermore, the layout and operation of the control and protection electronics units may also differ. The research aims to develop an approach that can autonomously detect and localize the weakest cells. The method was validated by testing the battery systems of three different VW e-Golf electric vehicles. A wide-range discharge test was performed to examine the condition assessment and select the appropriate state of charge (SoC) for all three vehicles. On the one hand, the analysis investigated the cell voltage deviations from the average; the tests cover deviations of 0 mV, 12 mV, 60 mV, 120 mV, and 240 mV. On the other hand, the mean value calculation was used to filter out possible erroneous values. Another important aspect was examining the relationship between the state of charges (SoC) and the deviations. Therefore, the 10% step changes were tested to see which SoC level exhibited more significant voltage deviations. Based on the results, it was observed that there are differences between the cases, and the critical range is not necessarily at the lowest SoC level. Furthermore, the load rate (current) and time of its occurrence play an important role in the search for a faulty cell. An additional advantage of this approach is that the process currently being tested on the VW e-Golf can be relatively simply transferred to other types of vehicles. It can also be a very useful addition for autonomous vehicles, as it can self-test the cells in the system at low power consumption.

Revealing the Nanoscopic Corrosive Degradation Mechanism of Nickel-Rich Layered Oxide Cathodes at Low State-of-Charge Levels: Corrosion Cracking and Pitting.

Ni-rich layered oxides have received significant attention as promising cathode materials for Li-ion batteries due to their high reversible capacity. However, intergranular and intragranular cracks form at high state-of-charge (SOC) levels exceeding 4.2 V (vs. Li/Li+), representing a prominent failure mechanism of Ni-rich layered oxides. The nanoscale crack formation at high SOC levels is attributed to a significant volume change resulting from a phase transition between the H2 and H3 phases. Herein, in contrast to the electrochemical crack formation at high SOC levels, another mechanism of chemical crack and pit formation on a nanoscale is directly evidenced in fully lithiated Ni-rich layered oxides (low SOC levels). This mechanism is associated with intergranular stress corrosion cracking, driven by chemical corrosion at elevated temperatures. The nanoscopic chemical corrosion behavior of Ni-rich layered oxides during aging at elevated temperatures is investigated using high-resolution transmission electron microscopy, revealing that microcracks can develop through two distinct mechanisms: electrochemical cycling and chemical corrosion. Notably, chemical corrosion cracks can occur even in a fully discharged state (low SOC levels), whereas electrochemical cracks are observed only at high SOC levels. This finding provides a comprehensive understanding of the complex failure mechanisms of Ni-rich layered oxides and provides an opportunity to improve their electrochemical performance.

Adaptive Virtual Inertial Control and Virtual Droop Control Coordinated Control Strategy for Hybrid Energy Storage Taking into Account State of Charge Optimization

For energy-storage-assisting conventional units to participate in the primary frequency regulation of a power system, firstly, based on the frequency regulation mechanism of virtual inertial control (VIC) and virtual droop control (VDC) of energy storage, we analyze the effect of the action timing of energy storage on the frequency deviation of the grid under two control methods and put forward a reasonable combination of the two control methods; on this basis, we also put forward hybrid energy storage adaptive VIC and VDC based on the demand of VIC and VDC on the power and capacity of energy storage. On this basis, based on the demand of VIC and VDC on the power and capacity of energy storage, a hybrid energy storage adaptive VIC and VDC coordinated control strategy based on supercapacitor–lithium batteries is proposed, whereby a high-power storage supercapacitor responds to inertial control signals to rapidly suppress a drop in frequency, and the high-capacity lithium battery responds to droop control signals to perform long-time droop control. The high-capacity lithium battery responds to the sagging control signal and is used to perform a long-time sagging power response; finally, in order to avoid the state of charge (SOC) of energy storage falling into a low/high working condition and losing the subsequent frequency regulation ability, an adaptive power control strategy of energy storage based on the improved logistic function is proposed. The simulation results show that under typical load disturbance, the SOC level of the proposed strategy increases by 19.17% and 30.16%, respectively, compared with that of the single-lithium strategy and no energy storage, and the SOC level of the supercapacitor and lithium battery increases by 29.4% and 2.1%, respectively, compared with that of logistic optimization.

An improved equalization technique for fast charging of Electric vehicles

This paper describes a lithium-ion battery (LIB) hybrid equalization (HE) structure. There are two primary strategies for voltage equalization (EQ): passive and active. Active equalization (AE) moves energy between the LIBs, whereas passive equalization (PE) wastes energy. PE strategy has the benefit of being easy to put into action, but at the same time, it requires more time to equalize and results in a loss of energy. Although it is difficult to implement, AE has a shorter EQ time and less energy loss. Combining these two methods simultaneously controls a pack of LIBs and equalizes voltage. This article offers recommendations on a cell level (CL) and between stacks. Some LIBs were outfitted with varying state of charge (SOC) levels to simulate a cell imbalance. Different topologies were tested, including combined AE and PE at the pack level and PE and AE topologies at the CL. Response times and SOC levels are used to compare the results. Compared to strictly AE or PE strategies, the HE showed encouraging results regarding EQ time and autonomy. This combination offers a solution that enables one to achieve minimal HE time while maintaining appropriate SOC.

Assessing spatial non-uniformities in lithium-ion battery state of charge using ultrasound immersion testing

Enhancing the performance, safety and reliability of battery management systems is crucial for advancing the state of the art in battery electric vehicles. Current research explores the potential of ultrasound to monitor state of charge (SoC) changes in individual cells. Understanding spatial variations in SoC is essential, as non-uniformities could lead to sub-optimal performance, premature ageing, and possible safety risks. This study uses ultrasound immersion C-scans to map wave speed and attenuation at different SoC levels during battery cycling. Results indicate non-uniform wave speed and attenuation suggestive of SoC spatial variations within single cells, emphasising the importance of addressing this issue. Acoustic measurements under various C-rates and relaxation periods are discussed, providing insights into lithium-ion rearrangement in graphite particles. Potential causes of structure and manufacturing variations of the cell are discussed, highlighting the need to address these issues to prevent overcharging or overdischarging in specific battery areas.

Discrete control for state of charge balance in DC microgrids considering the disturbance of photovoltaics

This study examines State of Charge (SoC) balancing control in DC microgrids subject to photovoltaic (PV) fluctuations, aiming to optimize power distribution in energy storage systems influenced by PV disturbances. The proposed approach enhances both the lifespan of storage systems and microgrid stability. To mitigate voltage variation due to PV perturbation, the paper introduces an adjustment in droop control offset. Additionally, it presents a novel discrete Sliding Mode Controller (SMC) characterized by reduced parameter sensitivity, thus enhancing control responsiveness. A SoC balancing control strategy employing sliding mode control is developed to equalize SoC levels across Battery Energy Storage Systems during both charge and discharge cycles. The stability of this strategy is substantiated through the construction of a Lyapunov function. Simulations conducted in a distributed DC microgrid environment using Simulink/SimPower Systems demonstrate the efficacy of the discrete SMC and the SoC balancing algorithm, achieving uniform SoC in energy storage nodes during operation, with improved robustness against PV perturbations.

Smart Energy Management Control Based on Fuzzy Logic Controller in a Standalone Photovoltaic/Wind System with Battery Storage

Energy management control (EMC) becomes imperative in hybrid energy systems that involve multiple sources. The main objective of this study is to concentrate on the effective control and utilization of energy in a hybrid system combining photovoltaic solar and wind sources, together with a battery bank. A sophisticated intelligent supervisor, employing a fuzzy logic controller (FLC), adeptly balances the power generated by the proposed system in response to the load demand. Simulation results demonstrate that, despite the initial maximum battery state of charge value being set at 90%, the battery’s state of charge does not exceed 60% during charging. Additionally, during discharge, the battery state of charge remains above 27%, surpassing the initially set minimum value of 25%. This achievement is attributed to the exchange of energy flow facilitated by the implemented intelligent management. The notable advantage of this intelligent management lies in its ability to precisely control the state of charge (SOC) of the battery across five distinct states, a feat not achievable with traditional management practices that rely on only two SOC levels (SOCmin–SOCmax).

Voltage measurement-based recursive adaptive method for internal short circuit fault diagnosis in lithium-ion battery packs

Internal short circuit (ISC) has been identified as a major cause of thermal runaway in lithium-ion (Li-ion) battery systems, making the investigation of ISC fault diagnosis a focal research topic in electric vehicles and battery energy storage systems. Recently, several studies have applied multivariate analysis techniques to analyze abnormal behavior in cell voltage measurements and achieved an accurate diagnosis of ISC faults. However, model parameter variations under different operating conditions and state of charge (SOC), which results in unacceptable false alarm rates, is yet to be fully considered. To effectively address this problem, this study introduces a recursive approach on the basis of principal component analysis (PCA) to update the monitoring model and adapt to parameter changes. Moreover, an adaptive threshold with fusion factors is devised to reduce fault false alarm rates. In particular, this study employs sliding window techniques to amplify weak fault features to tackle the challenge of early-stage detection difficulty for ISC faults and provides an optimal window width determination algorithm based on fault-free data. Experimental results on a real Li-ion battery pack test platform under various operating conditions and SOC levels demonstrate that the proposed method maintains extremely high fault detection rates for ISC faults of different intensities. Furthermore, it is established that the adaptive updating of the model in the absence of faults greatly reduces false alarm rates.