State Of Charge Estimation Techniques Research Articles

Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement

This paper explores the advancements and challenges in State of Charge (SOC) estimation techniques for lithium-ion batteries, particularly within the context of electric vehicles (EVs) and renewable energy systems. As the global demand for sustainable energy solutions grows, accurate SOC estimation becomes essential for optimizing battery performance, enhancing safety, and extending battery life. The paper categorizes various SOC estimation methods into three primary approaches: direct measurement techniques, model-based methods, and data-driven algorithms. Direct measurement techniques, such as Coulomb counting and Open Circuit Voltage (OCV), are straightforward but often lack precision under dynamic conditions. Model-based methods, including Equivalent Circuit Models (ECM) and electrochemical models, provide detailed insights into battery behavior but can be computationally intensive. In contrast, data-driven approaches utilizing machine learning algorithms exhibit promising adaptability and accuracy, albeit relying heavily on large datasets. Despite these advancements, several limitations persist. Current SOC estimation methods are hindered by their dependence on accurate battery models and quality data, which can degrade over time. Furthermore, hybrid methods, which combine strengths from various techniques, introduce complexity and demand substantial computational resources. The integration of SOC estimation techniques in EV applications poses additional challenges due to varying operational conditions, necessitating efficient processing of real-time data from multiple sensors. Emerging trends in Battery Management Systems (BMS) are also examined, highlighting the role of AI and machine learning in improving real-time SOC computations and predictive capabilities. Cloud-based BMS systems are identified as a significant development for remote monitoring and control, enhancing the overall reliability of battery systems. This paper aims to provide a comprehensive overview of SOC estimation techniques, identify ongoing challenges, and suggest future research directions to enhance the effectiveness of battery management strategies in the evolving landscape of energy systems. DOI: https://doi.org/10.52783/pst.906

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.

A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors

While State-of-Charge (SOC) estimation for supercapacitors has been extensively studied, most research focuses on single units. However, the recent introduction of reconfigurable circuits significantly alters system dynamics, rendering existing SOC estimation techniques inadequate. This paper addresses this challenge by employing a switching systems approach to estimate the SOC of supercapacitors with reconfigurable circuits. We first establish an RC model for the supercapacitor integrated with the reconfigurable circuit and thoroughly analyze the state continuity and observability of the resulting switched system. Subsequently, we propose a switching observer and evaluate its convergence properties by comparing its performance against other observer techniques. Experimental validation on a hardware platform demonstrates the superiority of our proposed observer for accurate SOC estimation in this context.

Deep learning-based state of charge estimation for electric vehicle batteries: Overcoming technological bottlenecks

This study presents a novel deep learning-based approach for the State of Charge (SOC) estimation of electric vehicle (EV) batteries, addressing critical challenges in battery management and enhancing EV efficiency. Unlike conventional methods, our research leverages a diverse dataset encompassing environmental factors (e.g., temperature, altitude), vehicle parameters (e.g., speed, throttle), and battery attributes (e.g., voltage, current, temperature) to train a sophisticated deep learning model. The key novelty of our approach lies in its integration of real-world driving data from a BMW i3 EV, enabling the model to capture the intricate dynamics affecting SOC with remarkable accuracy. We conducted 72 tests using actual driving trip data, which included 25 types of environmental variables, to validate the feasibility and effectiveness of our proposed model. The deep learning network, designed specifically for SOC estimation, outperformed traditional models by demonstrating superior accuracy and reliability in predicting SOC values. Our findings indicate a significant advancement in SOC estimation techniques, offering actionable insights for both policymakers and industry practitioners aimed at fostering energy conservation, carbon reduction, and the development of more efficient EVs. The study's major contribution is its demonstrated capability to improve SOC estimation accuracy by understanding the complex interrelationships among various influencing factors, thereby addressing a pivotal challenge in EV battery management. By employing cutting-edge deep learning techniques, this research not only marks a significant leap forward from traditional SOC estimation methods but also contributes to the broader goals of sustainable transportation and environmental protection.

Enhancing Estimating the Charge Level in Electric Vehicles: Leveraging Force Fluctuation and Regenerative Braking Data

Accurate determination of the state of charge is vital to optimize the performance and lifespan of electric vehicle batteries. Traditional methods which rely on battery models and direct measurements can be error-prone due to fluctuating operating conditions and battery degradation over time. Regenerative braking systems are crucial in electric and hybrid vehicles for improving energy efficiency by transforming kinetic energy into electrical energy during braking. However, force fluctuation is a challenge that can affect the performance and comfort of regenerative braking. It is known to us that electric motors and generators used in regenerative braking have non- linear torque characteristics, especially at low speeds, leading to inconsistent braking force. Variations in road conditions, such as wet or uneven surfaces, can affect the grip of the tires, leading to fluctuations in deceleration. Interactions of regenerative braking system with conventional friction brakes can cause force fluctuations, especially during the transition between the two systems. This study introduces an improved state of charge estimation technique based on force fluctuation and a regenerative braking system. This research shows that this approach significantly enhances state of charge accuracy compared to traditional methods, especially in urban driving conditions with frequent braking. The findings underscore the potential of using regenerative braking as well as force fluctuation condition data as a valuable input for state of charge estimation, ultimately leading to better battery management and an extended electric vehicle range.

State of charge estimation for lithium batteries based on multi-timescale FFRLS-UKF

Abstract State of charge (SOC) can represent the remaining battery capacity, and accurately estimating SOC is very useful for the drivers of electric vehicles. This study proposes a lithium battery SOC estimation technique based on multi-timescale forgetting factor recursive least squares as well as an unscented Kalman filter (MTS-FFRLS-UKF). The MTS-FFRLS-UKF uses a circuit with a 2-RC network as the battery model, whose parameters are continuously updated at a timescale that is different from SOC estimation. The experimental results under a dynamic test prove the availability and accuracy of MTS-FFRLS-UKF. Continuous parameter update enhances the accuracy of battery modelling and furthermore the SOC estimation accuracy. The mean absolute error (MAE) and root-mean square error (RMSE) of SOC estimation decrease to 0.58% and 0.62%, respectively.

Towards robust state estimation for LFP batteries: Model-in-the-loop analysis with hysteresis modelling and perspectives for other chemistries

The accurate estimation of a battery’s state of charge (SOC) is critical in battery management systems for various applications. Lithium Iron Phosphate (LFP) batteries, preferred for their long cycle life, cost efficiency, and enhanced safety, have emerged as favourable choices for stationary storage. Yet, they still face challenges in precise SOC estimation due to the flatness and hysteresis of their open circuit voltage. Addressing this, our study integrates a hysteresis model into a third-order battery model for BMS controlling a stationary storage system in frequency containment reserve (FCR) application. We analysed three advanced SOC estimation techniques — extended Kalman filter (EKF), dual unscented Kalman filter (DUKF), and particle filter (PF) — with the hysteresis model using a model-in-the-loop (MiL) toolchain. Performance testing under a 48-hour FCR load profile showed EKF with a 4% error, DUKF achieving the best result with a 1.1% error, and PF’s performance varying between 2.9% and 4% depending on particle count. Robustness tests against initialization and current sensor errors under an 8hr profile revealed DUKF maintained a 2% error boundary irrespective of the error introduced, highlighting the hysteresis model’s effectiveness. Broadening the scope, the study also explores extending the method to other lithium-ion chemistries.

An improved LKF based SOC estimation and a power management strategy to enhance the cycle life of BES in a microgrid

Nowadays, utility grid experiences significant challenges due to increased adoption of renewable energy resources (RES). The intermittency of RES is ridden by using battery energy storage (BES). The system control often aims at maximizing self-consumption, neglecting its effect on effective life of BES. A power management strategy (PMS) is proposed in this work that helps to increase the extraction of maximum energy without compromising on operational robustness of BES for grid applications. Maximum depth of discharge (DOD) is limited to a certain mean state of charge (SOC) to improve the cycle life of BES. A lifecycle model is derived for BES to compute a compensating term for estimating the DOD limit after each cycle. An enhancement to existing Linear Kalman filtering (LKF) technique is also presented in this work for SOC estimation of BES. A significant reduction in root mean square error (RMSE) is achieved using improved LKF-based SOC estimation technique. A second-order generalized integrator with a pre-filter based frequency locked loop (SOGI-WPF-FLL) controls microgrid under abnormal utility grid and load conditions. Resynchronization of microgrid with utility grid is demonstrated using SOGI-WPF-FLL filter without causing any maloperations. System is simulated under various operating conditions in MATLAB/Simulink environment and validated on a real-time OP5700-based test-bench.

A multi-scale SOC estimation method for lithium-ion batteries incorporating expansion force

Electric vehicles (EVs) have become a viable alternative to fuel vehicles, and accurate State of Charge (SOC) estimation of the lithium-ion battery is the key to guarantee EVs' safe operation. However, small data sampling interval like 1 s is usually required for exiting SOC estimation techniques, which cannot be achieved in practical EVs operation scenarios. This paper proposes a novel SOC evaluation method through the fusion of expansion force with voltage and current measurements. Specifically, long-term estimation (i.e., global trend) of SOC are established by extracting information from expansion force and voltage data with long short-term memory (LSTM) algorithm, while short-term estimation (i.e., local variation) of SOC are obtained by extracting information from current and voltage measurements with Support Vector Regression (SVR). After that, long-term and short-term (i.e. multi-scale) SOC estimations are fused to provide final SOC estimation. Various test data under different driving profiles are utilized to validate the proposed method, including NEDC, and results are compared to those from widely used SOC estimation techniques like LSTM. Results demonstrate that with sampling interval increases from 1 s to 10s, the multi-scale SOC estimation method can maintain maximum absolute error (ME) less than 2.84 %, while mean absolute error (MAE) changes from 0.32 % to 0.48 % and root mean squared error (RMSE) changes from 0.44 % to 0.64 %. It demonstrates that the proposed method can effectively alleviate the dependence on small data sampling interval, thus can provide accurate SOC estimation with various sampling intervals.

State of charge estimation techniques of Li-ion battery of electric vehicles

The Lithium-ion batteries are widely utilized in the electric car, bus, and two-wheeler industries because of their high energy density, low cost, extended lifespan, high power density, and stable voltage. One of the essential systems that must be present in any electric vehicle (EV) is the battery management system (BMS). One major input to BMS is state of charge to ensure the battery's durability, safety, and reliable operation. The state-of-charge (SoC) estimation of EV batteries plays a crucial role in optimizing their performance and extending their lifespan. As batteries are nonlinear and time-variant devices, estimating the state of charge or instantaneous remaining charge within a battery is a particularly challenging task. This paper covers a deep understanding of SoC estimation techniques for BMS. The two main approaches to explaining estimation of instantaneous remaining charge are model-based which relies on various battery models and their mathematical equations to explain the battery characteristics. The second approach is data-driven which studies large measured battery data sets to understand the behavior of running algorithms. Model-based approaches are based on series-parallel combinations of resistance and capacitance electrical circuits, while data-driven approaches are based on neural networks and machine learning algorithms. The review highlights the strengths and limitations of each technique, suggesting that hybrid approaches could yield more robust results. It emphasizes the importance of future research in integrating multiple information sources and developing standard evaluation procedures to enhance SoC estimation accuracy and its practical application in EVs.

State of charge estimation techniques for battery management system used in electric vehicles: a review

State of charge estimation techniques for battery management system used in electric vehicles: a review

A robust observer based on the nonlinear descriptor systems application to estimate the state of charge of lithium-ion batteries

A robust observer based on nonlinear descriptor systems is applied to estimate lithium-ion batteries’ state of charge (SOC). We creatively take the relationship between the battery’s open circuit voltage (OCV) and SOC as a nonlinear algebraic constraint, thereby modeling the battery as a nonlinear descriptor system. This model can effectively trade off accuracy and complexity. The robust convergence, even in the presence of uncertainties, is proven through a combination of the Lyapunov stability theorem and linear matrix inequality (LMI) techniques. Experimental results showcase the effectiveness of the proposed SOC estimation technique, offering enhanced convergence speed, estimation accuracy, and robustness compared to conventional sliding mode observers.

Design and Validation of a State-Dependent Riccati Equation Filter for State of Charge Estimation in a Latent Thermal Storage Device

Abstract Latent thermal energy storage (TES) devices could enable advances in many thermal management applications, including peak load shifting for reducing energy demand and cost of HVAC or providing supplemental heat rejection in transient thermal management systems. However, real-time feedback control of such devices is currently limited by the absence of suitable state of charge estimation techniques, given the nonlinearities associated with phase change dynamics. In this paper, we design and experimentally validate a state-dependent Riccati equation (SDRE) filter for state of charge estimation in a phase change material (PCM)-based TES device integrated into a single-phase thermal-fluid loop. The advantage of the SDRE filter is that it does not require linearization of the nonlinear finite volume model; instead, it uses a linear parameter-varying system model which can be quickly derived using graph-based methods. We leverage graph-based methods to prove that the system model is uniformly detectable, guaranteeing that the state estimates are bounded. Using measurements from five thermocouples embedded in the PCM of the TES and two thermocouples measuring the fluid temperature at the inlet and outlet of the device, the state estimator uses a reduced-order finite volume model to determine the temperature distribution inside the PCM and in turn, the state of charge of the device. We demonstrate the state estimator in simulation and on experimental data collected from a thermal management system testbed to show that the state estimation error converges near zero and remains bounded.

A new state of charge estimation technique of lithium-ion battery using adaptive extended Kalman filter and artificial neural network

Due to rapid rise in climate change, world is seeing a major technology shift from conventional vehicles to electric vehicles (EVs). Battery’s state of charge (SOC) is a critical parameter in EVs whose accuracy affects not only the battery management system but can also influence the driving range estimation for an EV. In this paper, a simulation-based hybrid technique is presented, that combines both adaptive extended Kalman filter (AEKF) and artificial neural network (ANN) for SOC estimation of lithium-ion battery. The proposed hybrid technique is validated using five different EV driving cycles, that is, LA92, US06, urban dynamometer driving schedule (UDDS), highway fuel economy test (HWFET) and a mixed driving cycle at four different temperature values, that is, 0°C, 10°C, 25°C and 40°C. To facilitate the potential EV users, a real-time SOC estimator is also implemented. The real-time SOC estimated through open circuit voltage method and Coulomb counting method is further used for reservation of a charging slot in a nearby charging station. Moreover, to monitor the charging status of a potential EV from a remote location, a cloud-based Internet of things (IoT) platform, that is, ThingSpeak, is used. This results into the reduction of waiting time for an EV user at a charging station. In addition to this, a comparative analysis is carried out between proposed hybrid method and existing methods. It is demonstrated that our hybrid technique achieves lower root mean square error and higher accuracy as compared to existing methods at different operating conditions.

A real-time hybrid battery state of charge and state of health estimation technique in renewable energy integrated microgrid applications

Abstract This paper presents a novel real-time hybrid battery state of charge (SoC) and state of health (SoH) estimation technique with less computational effort for optimal operation in renewable energy integrated microgrid applications. The proposed SoC estimation technique utilizes battery terminal voltage and current information along with stress factors like battery charge–discharge rates and temperature effects to accurately estimate the SoC. In addition, it considers the open-circuit voltage (OCV) and SoC relation to dynamically recalibrate the SoC during idle conditions. The proposed SoH estimation technique uses a modified coulomb counting method and variation of battery capacity at different charge–discharge rates to precisely estimate the SoH of the battery. Simulation studies are carried out by considering the aging factor, temperature effect, and charge–discharge rates to analyze the performance of the proposed techniques under various dynamic conditions. A LabVIEW-based application is developed, and experimental verification in terms of estimation accuracy, real-time monitoring is carried out to verify the efficacy of the proposed technique. A comparative analysis with state-of-the-art estimation techniques is presented for validating the effectiveness and usefulness in real-time applications.

A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

A Comprehensive Review of State of Charge Estimation Techniques for Battery Energy Storagesystems in Power Grid

A Comprehensive Review of State of Charge Estimation Techniques for Battery Energy Storagesystems in Power Grid

Adaptive Integral Correction-Based State of Charge Estimation Strategy for Lithium-Ion Cells

Lithium-ion (Li-ion) battery systems are critical elements of future energy systems and electric vehicles. Accurate prediction of the state of charge (SoC) is necessary for the safe and reliable functioning of Li-ion battery systems. Achieving a precise SOC estimate is challenging due to the nonlinear characteristics and variations of model parameters caused over the cell lifetime. This paper introduces an adaptive estimation strategy that can compensate for the effect of cell degradation for achieving high accuracy SoC estimation. The proposed method uses an integral correction-based SoC estimation loop utilizing a Li-ion cell model. The effect of model parameter variation is corrected by introducing two additional correction factors, the cell model resistance, and capacity correction factor. These correction factors are employed to update the Li-ion cell model, resulting in an adaptive integral correction-based SoC estimation technique that can compensate for the influence of cyclic degradation-induced parameter change. The proposed method is validated through extensive simulations in the Matlab-Simulink environment, and its output is compared to the existing unscented Kalman filter-based SoC estimation method. The proposed estimation strategy can adapt to the cell circumstances and correct for model uncertainties. The results indicate that the proposed adaptive SoC estimation strategy provides more precise and accurate SoC estimates for the entire lifespan of the Li-ion cell.

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review

Extension of driving range and battery run time optimization are necessary key points in the modeling of Electric Vehicle (EV). In this view, Battery Management System (BMS) plays a major role to ensure a safe and trustworthy battery operation, especially when using Lithium-ion (Li-ion) batteries in an electric vehicle. Key function of BMS is State of Charge (SoC) estimation. A well-parameterized battery model is required for accurate state estimation. Consequently, the major factors to be considered in battery modeling are the SoC estimation and charging methodology of an effective BMS development. By focusing on these features, in this paper, the well-known battery models such as the electrochemical model, equivalent circuit model, and data-driven model are comprehensively reviewed along with their strengths and weaknesses. Further, the SoC estimation of a battery is also discussed by using standard methodologies such as direct estimation methods and model-based estimation methods. The comparisons of the three most distinct battery models and the classification of SoC estimation techniques to develop a proper BMS for EV with the focus on accuracy, configuration effort, computational complexity, ease of implementation, and real-time applications are systematically reviewed. In addition to this, convenient battery charging approaches with the consideration of some constraints such as charging time, charging efficiency, state of charge, state of health, charging voltage threshold, capacity fade, power fade, aging effect, capacity utilization, impedance rise, and temperature rise of the battery in EV are presented. Finally, the perspectives of the existing work and the recommended future research work of BMS are summarized.

State of Charge Estimation Techniques for Lithium-ION Batteries Used in Electric Vehicle Applications

Abstract: Lithium-ion battery packs constitute an important part of Electric vehicles. The usage of Lithium-ion based chemistries as the source of energy has various advantages like high efficiency, high energy density, high specific energy, longevity among others. However, the management of lithium-ion battery packs require a Battery Management System (BMS). The BMS deals with functions like safety, prevention of abusive usage of battery pack, overcharging & over-discharging protection, cell balancing and others. One of the prominent features of the BMS is the estimation of State of charge (SOC). SOC is like a fuel gauge in automobile, it indicates how much more the battery can be used before charging it again. SOC is also required for other functions of BMS like State of Health (SOH) tracking, Range calculation, power & energy availability calculations. However, there is no means of measuring it directly (at least not on-board a vehicle) or estimating it easily. Various techniques should be used to estimate SOC indirectly. This paper starts from classical techniques that have existed since long time and reviews some of the modern & developing methods for SOC estimation. It contains a brief review about most of these SOC estimation methods, thus highlighting the methodology, advantages & disadvantages of each of these techniques. A brief review of other developing SOC estimation techniques is also provided. Keywords: State of Charge, SOC, Lithium-ion battery packs, Electric vehicles, Kalman Filter.