Battery State Of Charge Research Articles

Comparison of different data-driven methods for estimating the state of charge of lithium-ion batteries

ABSTRACT Optimizing battery performance, range, and longevity in Electric Vehicles (EVs) can be achieved by utilizing a precise state of charge (SOC) estimation. Consequently, this enhances the overall efficiency and reliability of EVs. This study examines three data-driven methodologies: Feed-Forward Neural Network (FNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) for estimating SOC of lithium-ion batteries (LiBs). The evaluation uses real data collected from three distinct drive cycles namely Urban Dynamometer Driving Schedule (UDDS), New York City Cycle (NYCC), and Braunschweig City Driving Cycle (BCDC) at three different temperatures (15°C, 25°C, and 45ºC). Two scenarios are created to select input features based on the dataset’s properties. Hyperparameters are tuned to maximize accuracy, and models are assessed based on the parameters leading to the minimum validation error. The computational time of FNN is the least among all the methods under study. FNN also gives the best prediction of SOC estimation irrespective of drive cycles and temperatures as compared to other methods.

On-Demand Urban Air Mobility Scheduling with Operational Considerations

This paper introduces an on-demand sequencing and scheduling framework for Urban Air Mobility (UAM) with electric vertical takeoff and landing (eVTOL) aircraft. Safety and efficiency, considering factors such as battery state of charge and charging infrastructure, are critical factors for UAM operations. A new scheduling framework integrating considerations for battery consumption, parking and charging infrastructure, vertiport throughput, and fleet heterogeneity to maximize the operational efficiency of the eVTOL UAM fleet is proposed. A solution methodology utilizing a genetic algorithm and receding horizon scheduling achieves near-optimal solutions with an average optimality gap of 5.8% and a runtime of less than 1 min for dynamic scheduling. A case study based on the 2024 Paris Olympic air taxi operations demonstrates the efficacy of the proposed problem formulation and solution method.

Research on the Fast Charging Strategy of Power Lithium-Ion Batteries Based on the High Environmental Temperature in Southeast Asia

To address the problem of excessive charging time for electric vehicles (EVs) in the high ambient temperature regions of Southeast Asia, this article proposes a rapid charging strategy based on battery state of charge (SOC) and temperature adjustment. The maximum charging capacity of the cell is exerted within different SOCs and temperature ranges. Taking a power lithium-ion battery (LIB) with a capacity of 120 Ah as the research object, a rapid charging model of the battery module was established. The battery module was cooled by means of a liquid cooling system. The combination of the fast charging strategy and the cooling strategy was employed to comprehensively analyze the restrictions of the fast charging rate imposed by the battery SOC and temperature. The results indicate that when the coolant flow rate was 12 L/min and the inlet coolant temperature was 22 °C, the liquid cooling system possessed the optimal heat exchange capacity and the lowest energy consumption. The maximum temperature (Tmax) of the battery during the charging process was 50.04 °C, and the charging time was 2634 s. To lower the Tmax of the battery during the charging process, a charging rate limit was imposed on the temperature range above 48 °C based on the original fast charging strategy. The Tmax decreased by 0.85 °C when charging with the optimized fast charging strategy.

Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis

The article presents the development of a Rules-Based Energy Management System for a nanogrid that serves an electric vehicle charging station. This nanogrid is composed of photovoltaic generation, a wind turbine, a battery energy storage system, and a fast electric vehicle charger. The objective is to prioritize the use of renewable energy sources, reducing costs and promoting energy efficiency. The methodology includes forecasting models based on an Artificial Neural Network for photovoltaic generation, a parametric estimation for wind generation, and a Monte Carlo simulation to predict the energy consumption of electric vehicles. The developed algorithm makes decisions every 15 min, considering variables such as energy tariff, battery state of charge, renewable generation forecast, and energy consumption forecast. The results showed that the system adequately balances energy generation, consumption, and storage, even under forecasting uncertainties. The use of the Monte Carlo simulation was crucial for evaluating the financial impacts of forecast errors, enabling robust decision-making. This energy management system proved to be effective and sustainable for nanogrids dedicated to electric vehicle charging, with the potential to reduce operational costs and increase energy reliability and the use of renewable energy sources.

State of charge estimation of lithium-ion battery based on TSCSO-GRU-Attention

ABSTRACT The precise estimation of the State of Charge (SOC) for lithium-ion batteries is paramount for guaranteeing their secure operation. To augment the precision of SOC estimation, this paper introduces a novel TSCSO-GRU-Attention model. This model amalgamates the Sand Cat Swarm Optimization (SCSO) algorithm, which is optimized by a triangular traversal strategy, and the Gated Re-current Unit (GRU) neural network, equipped with an attention mechanism. This model harnesses the robustness and accuracy of GRU and attention for SOC estimation, enhances the local search proficiency of the SCSO algorithm via the triangular traversal strategy, and employs the refined SCSO algorithm to optimize the three hyperparameters: the number of neurons, learning rate, and batch size prior to feeding into the GRU-Attention network model, thereby enhancing the precision of SOC estimation. This paper proposes a model that accurately predicts the SOC of lithium batteries. Experimental results demonstrate that when compared to the SCSO-GRU-Attention model, the proposed model achieves reductions in MAE, RMSE, and MAPE by 42.84%, 41.94%, and 2.25% points respectively, thus rendering the SOC estimation results more precise.

Simple Energy Model for Hydrogen Fuel Cell Vehicles: Model Development and Testing

Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model’s forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model’s effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models.

Research on Energy Management Strategies Based on Bargaining Game for Range-Extended Electric Vehicle Considering Battery Life

Effective energy management techniques are essential for the full utilization of energy in the field of extended-range electric vehicle research, with the goals of lowering energy consumption and exhaust emissions, enhancing driving comfort, and extending battery life. To achieve optimal comprehensive usage costs, this article uses bargaining game theory to design an adaptive energy management strategy (EMSad-bg) that focuses on battery life. In the study, a power system model was first built based on AVL/Cruise software and MATLAB/Simulink software. The impact of discount factors on strategy results was analyzed through simulation experiments. The results showed that the discount factor for auxiliary power unit (APU) focused more on energy optimization, while the discount factor for battery focused more on optimizing the degradation of battery life. When the initial state of charge (SoC) is high, the specific value of the discount factor also has a significant impact on the battery SoC value at the end of the trip. To improve the strategy’s adaptability to various initial SoC values, a fuzzy controller was created that can adaptively modify the discount factor based on the battery SoC. The results of the simulation experiment demonstrate that the bargaining game strategy taking SoC into account has more pronounced advantages in terms of overall usage cost when compared to the strategy of the fixed discount factor. The creation of an EMSad-bg that takes battery life into account based on a bargaining game can serve as a helpful model for the creation of a clever EMS that lowers the total cost of operating a vehicle.

Optimized operation of AC–DC microgrid cluster with modified PLL and socket protocol

AbstractMicrogrids integrating inverter based resources face challenges like stability issues during voltage fluctuations and reliance on diesel generators in islanded mode. This paper presents a novel control strategy for photovoltaic and wind‐integrated microgrids to enhance reliability and efficiency. The strategy utilizes a modified phase‐locked loop with droop control for seamless synchronization in grid‐connected and islanded modes. Central to this approach is the localized autonomous controller with a socket protocol interfaced communication layer, dynamically managing operations based on real‐time conditions such as generation, load, and battery state of charge. By integrating battery storage systems, the strategy reduces diesel generator dependency during grid outages. Comprehensive simulations and controller hardware‐in‐the‐loop testing validate its effectiveness, demonstrating improved stability and decreased reliance on diesel generators. The socket protocol enhances real‐time communication, monitoring, and optimization of microgrid operations, ensuring optimal battery storage system utilization and minimal diesel usage. The proposed strategy addresses critical microgrid challenges, offering significant advancements in enhancing system resilience.

Performance analysis of CKF algorithm for battery SoC estimation with its aging effect

The penetration of electric vehicle (EV) in automobile market is very much dependent on the battery technology. Its size, weight, and cost are issues of concern. To effectively utilize the battery expertise, precise estimate of state of charge (SoC) is vital which greatly depends on the battery model. Current models lack consideration for variations in battery capacity over their lifespan. This paper develops a battery model which depicts the depletion of battery capacity with its life. Subsequently, this model has been utilized for estimation using advanced Kalman filtering (KF) algorithms. For the developed model, the design and effectiveness of the cubature Kalman filter (CKF) is applied as a proposed robust state-estimator for this problem. Moreover, a comparative analysis was undertaken with existing non-linear KFs based on performance metrics. The optimal choice of estimator is identified, through the results obtained from the Octave/MATLAB simulation. The outcomes show CKF algorithm based SoC estimator is superior to others in ensuring high accuracy, strong robustness even under changes in initial conditions (i.e., initial SoC, process and sensor noise levels), system's ability to converge quickly while ensuring that the maximum error in state of charge (SoC) estimation remains within 1% after convergence.

Advanced integration of bidirectional long short-term memory neural networks and innovative extended Kalman filter for state of charge estimation of lithium-ion battery

The state of charge (SoC) of a battery is a crucial monitoring indicator for battery management systems and it helps to assess how much further an electric vehicle can travel. This work proposes a novel approach for predicting battery SoC by developing a closed-loop system that integrates a bidirectional long short-term memory neural network with an innovative algorithm-extended Kalman filter. A second-order equivalent circuit model is selected, and its parameters are computed using the variational and logistic map cuckoo search approach. Further, an Extended Kalman filter is combined with an innovation algorithm to update process noise in real-time, and a bidirectional long short-term memory neural network takes the input from the Extended Kalman filter and gives the compensated error value for the final SoC estimation. 75% of dynamic stress test data from the Extended Kalman filter is used for training purposes, remaining data sets are used for testing purposes. The addressed algorithm is validated by evaluating its performance in comparison to individual algorithms and various combined approaches. Empirical analysis demonstrates that the proposed model achieves a root mean square error of 0.11 % and mean absolute error of 0.1 % positioning it as a valuable tool for battery management systems.

State of Charge Prediction of Mine-Used LiFePO4 Battery Based on PSO-Catboost

The accurate prediction of battery state of charge (SOC) is one of the critical technologies for the safe operation of a power battery. Aiming at the problem of mine power battery SOC prediction, based on the comparative experiments and analysis of particle swarm optimization (PSO) and Categorical Boosting (Catboost) characteristics, the PSO-Catboost model is proposed to predict the SOC of a power lithium iron phosphate battery. Firstly, the classification model based on Catboost is constructed, and then the particle swarm algorithm is used to optimize the Catboost hyperparameters to build the optimal model. The experiment and comparison show that the optimized model’s prediction accuracy and average precision are superior to other comparative models. Compared with the Catboost model, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of the PSO-Catboost model decreased by 12.4% and 25.4% during charging and decreased by 5.5% and 12.2% during discharging. Finally, the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models, both ensemble learning models, are selected and compared with PSO-Catboost after being optimized via PSO. The experimental results show that the proposed model has a better performance. In this paper, experiments show that the optimization model can select parameters more intelligently, reduce the error caused by artificial experience to adjust parameters, and have a better theoretical value and practical significance.

Grid-following and grid-forming control modes of the rotor and grid sides converters for seamless and universal operation of the hybrid DFIG-wind/battery energy storage system in grid-connected and stand-alone conditions

The system examined in this paper is a hybrid doubly-fed induction generator wind-turbine (DFIG-WT) combined with a battery energy storage system (BESS). It operates in both stand-alone and grid-connected modes, with the capability for seamless transitions between these states, and manages the state of charge (SOC) of the battery under all conditions. In this paper, by modifying control structures of the rotor-side and grid-side converters (RSC and GSC), the RSC is controlled in the grid-following (GFL) mode and the GSC in the grid-forming mode using the virtual synchronous generator technique. The proposed control scheme with detailed considerations for both the RSC and GSC simultaneously addresses the following aspects: (a) seamless operation in both grid-connected and stand-alone conditions, (b) synchronization and smooth connection of the DFIG during initial moments of grid-connection without using a phase-locked loop (PLL), (c) enabling the converters control systems to allow the grid-connected DFIG to operate not only in MPPT mode but also in power dispatch mode based on the power grid requirements, and (d) SOC adjustment of the battery in both grid-connected and islanded modes. In the grid-connected state, the battery SOC is managed by the GSC, and for SOC < 90 %, the battery is charged by the GSC according to the SOC level, and for SOC > 95 %, the GSC limits the SOC below the upper limit. In the stand-alone state, the SOC upper limits are mainly managed by the RSC, and if the SOC exceeds the value of 85 %, the RSC works in the power curtailment mode and reduces the stator power.

Real‐Time Energy Management System for a Hybrid Renewable Microgrid System

ABSTRACTThis paper gives a detailed study for the design and implementation of an energy management system (EMS) for a hybrid renewable microgrid system using real‐time software. Microgrids, with their ability to integrate renewable energy sources, face challenges in maintaining stability and reliability. The implemented EMS aimed to maximize the renewable energy sources utilization, including PV and wind power, in conjunction with a battery energy storage system. The objectives of this research included the implementation of an EMS that ensures a reliable and stable operation between the microgrid system and the main grid including the control of charge and discharge of the battery using Typhoon Hardware‐in‐the‐Loop (HIL) software. The simulation results and case studies demonstrated the effectiveness and performance of the developed EMS in managing a hybrid renewable microgrid system. The results also demonstrated that the time of charging was maximized by utilizing a higher power. By doing so, the battery was fully charged in a shorter timeframe. The battery state of charge (SOC) was maintained between the fixed values (20% and 100%) as stated by the algorithm.

Dual-Fuzzy Regenerative Braking Control Strategy Based on Braking Intention Recognition

Regenerative braking energy recovery is of critical importance for electric vehicles due to their range limitations. To further enhance regenerative braking energy recovery, a dual-fuzzy regenerative braking control strategy based on braking intention recognition is proposed. Firstly, the distribution strategy for braking force is devised by considering classical curves like ideal braking force allocation and ECE regulations; secondly, taking the brake pedal opening and its opening change rate as inputs, the braking intention recognition fuzzy controller is designed for outputting braking strength. Based on the recognized braking strength, and considering the battery charging state and the speed of the vehicle as inputs, a regenerative braking duty ratio fuzzy controller is developed for regenerative braking force regulation to improve energy recovery. Furthermore, a control experiment is established to evaluate and compare the four models and their respective nine braking modes, aiming to define the dual fuzzy logic controller model. Ultimately, simulation validation is conducted using Matlab/Simulink R2019b and CRUISE 2019. The results show that the strategy in this paper has higher energy savings compared to the single fuzzy control and parallel control methods, with energy recovery improved by 26.26 kJ and 96.13 kJ under a single New European Driving Cycle (NEDC), respectively.

Advanced Design of Naval Ship Propulsion Systems Utilizing Battery-Diesel Generator Hybrid Electric Propulsion Systems

As advanced sensors and weapons require high power, naval vessels have increasingly adopted electric propulsion systems. This study aims to enhance the efficiency and operability of electric propulsion systems over traditional mechanical propulsion systems by analyzing the operational profiles of modern naval vessels. Consequently, a battery-integrated generator-based electric propulsion system was selected. Considering the purpose of the vessel, a specification selection procedure was developed, leading to the design of a hybrid electric propulsion system (comprising one battery and four generators). The power management control technique of the proposed propulsion system sets the operating modes (depending on the specific fuel oil consumption of the generators) to minimize fuel consumption based on the operating load. Additionally, load distribution control rules for the generators were designed to reduce energy consumption based on the load and battery state of charge. MATLAB/Simulink was used to evaluate the proposed system, with simulation results demonstrating that it maintained the same propulsion performance as existing systems while achieving a 12-ton (22%) reduction in fuel consumption. This improvement results in cost savings and reduced carbon dioxide emissions. These findings suggest that an efficient load-sharing controller can be implemented for various vessels equipped with electric propulsion systems, tailored to their operational profiles.

Impact of driving timing and road types on the operating regions of electric two-wheeler powertrain components under indian road conditions

Among the different classes of electric vehicles (EVs), electric two-wheelers (E2Ws) are securing more enticement towards the commutator for regular usage due to their higher suitability in the heterogeneous traffic conditions of India. However, the driving range of the E2Ws is the major concern in increasing its penetration rate in road transportation. To eradicate the range anxiety of commutators by redesigning the current E2 W powertrain configuration, this study strives to insights into the operational limits of powertrain elements such as the battery and motor in E2 W under multiple operational circumstances such as driving style, pavement, and ambient conditions. Regarding this, the E2 W with data acquisition system (DAC) connected with an onboard diagnosis (OBD) port is utilized to acquire the data related to powertrain components and vehicles for four driving trips (trips 1–4) under three major road segments (Rural, expressway, and urban) of India. The operating and computed performance metrics, comprising battery voltage and current, state-of-charge (SOC), energy consumption, motor current, speed, efficiency, and so on, are then compared throughout driving trips and road segments. The mean velocity of E2 W for trips 1 to 4 is 31.91, 33.68, 39.03, and 35.58 km/h, respectively. The results disclosed that total energy consumption and energy consumption per km. are higher during trip 3 due to higher average vehicle speed. Also, it has a higher depth-of-discharge (DOD) of 83.91 % than other driving trips. Further, a higher rate of SOC drop, energy consumption and battery temperature rise are found at highway road segments.

A Deep Learning Dependent Controller for Advanced Ultracapacitor SoC Concept to Increase Battery Life Span of Electric Vehicles

ABSTRACTOn a global scale, significant progress is being made in the field of battery technology for Electric Vehicle (EV) applications, driven by the need to combat carbon emissions and mitigate the effects of global warming. Accurately determining critical parameters, making sure battery storage system diagnosis, and functioning are correct are critical to the feasibility of EVs. However, insufficient supervision and safety measures for these storage systems may lead to serious problems like a thermal runaway, overcharging, overheating, cell imbalances, and fire hazards. To tackle these challenges, the presence of an efficient battery management system becomes paramount. By facilitating accurate monitoring, managing heat dissipation, regulating charging‐discharging procedures, guaranteeing battery safety, and offering protection measures, this system is essential to maximizing battery performance. The key intention of this innovative approach is to improve the longevity of EV batteries during extended periods of operation. By assessing vehicle velocity, remaining battery energy, and State of Charge (SoC), the proposed method effectively manages SoC in both the battery and ultracapacitor. This control is accomplished through a two‐stage convolutional neural network‐based system known as the Charge Sustain‐CNN Controller and the Charge Deplete‐CNN Controller. These controllers are fine‐tuned using the Fractional Latrans‐Hunt optimization (FLHO) algorithm to optimize the performance. The evaluation criteria encompass the battery and ultracapacitor's energy efficiency, as well as vehicle velocity. This novel approach significantly improves the energy storage system in EVs, leading to enhanced energy efficiency and prolonged battery life. Ultimately, experimental results validate the practicality and effectiveness of this developed method. Specifically, the proposed approach attained the Battery's SoC of 72.47%, 91.99%, and 82.88% for the different drive cycles including the FTP75, J1015, and UDDS, respectively.

State of charge estimation for lithium-ion batteries based on Gated Recurrent Unit neural network and an Adaptive Unscented Kalman Filter

Abstract For battery management systems, the accurate estimation of the state of charge (SOC) of lithium-ion batteries is crucial, yet it still poses challenges. Traditional model-based filtering methods typically require accurate battery models, but parameter uncertainties arise from various factors such as battery aging and temperature variations, leading to parameter uncertainties. In contrast, data-driven approaches can effectively capture SOC variations under different operating conditions and temperatures, but often exhibit significant prediction fluctuations. To address these challenges, a combined SOC estimation approach known as GRU-AUKF is presented in this paper. This method utilizes Gated Recurrent Unit (GRU) networks based on temperature, current, and voltage to estimate SOC, followed by filtering the output using an Adaptive Unscented Kalman Filter (AUKF) to reduce estimating errors. Experimental results demonstrate that when estimating battery SOC across different temperatures ranging from 0°C to 50°C, the RMSE is less than 1.68%, and the MAE is less than 1.44%. Compared to methods solely employing GRU models, the proposed GRU-AUKF method exhibits superior performance in enhancing estimation accuracy, thus validating its effectiveness.

Lithium battery SOC estimation based on improved sparrow search algorithm and backpropagation neural network

Accurate state-of-charge (SOC) estimation is crucial for optimal battery management. This paper proposes a novel method, the Improved Sparrow Search Algorithm-Backpropagation (ISSA-BP) neural network, to address the issue of low estimation accuracy encountered with a single BP neural network. ISSA is used to optimize the initial weights and thresholds of the BP neural network, effectively overcoming its tendency to get stuck in local minima. Compared to the single BP neural network, ISSA-BP demonstrates significantly improved accuracy under two conditions (DST and BJDST), with reductions in root mean square error by 64.0% and 50.9% and mean absolute error by 69.8% and 51.1%, respectively. These results highlight the superior robustness and accuracy of the ISSA-BP algorithm for SOC estimation in lithium batteries.

An Adaptive Combined Method for Lithium‐Ion Battery State of Charge Estimation Using Long Short‐Term Memory Network and Unscented Kalman Filter Considering Battery Aging

AbstractThe accurate estimation of battery state of charge (SOC) enables the reliable and safe operation of lithium‐ion batteries. Data‐driven SOC estimation is considered an emerging and effective solution. However, existing data‐driven SOC estimation methods typically involve direct estimation and lack effective feedback correction. Moreover, battery degradation poses additional challenges to accurate SOC estimation. Therefore, this study proposes an adaptive combined method for battery SOC estimation based on a long short‐term memory (LSTM) network and unscented Kalman filter (UKF) algorithm considering battery aging status. First, an LSTM model is constructed to characterize the battery's dynamic performance instead of traditional battery models. Then, the UKF algorithm is employed to perform SOC estimation through the feedback of terminal voltage prediction. To enhance estimation accuracy under different aging statuses, a proportional‐integral‐derivative controller is employed to correct the capacity fading during the SOC estimation process. Validation results indicate that the terminal voltage prediction model demonstrates exceptional robustness against interference from current and voltage noise. Compared to the traditional estimation method combining the deep learning model and Kalman filter algorithm, the proposed method demonstrates superior estimation accuracy under various complex operating conditions. Furthermore, the proposed method outperforms the traditional method in estimation performance during battery aging.