Battery State Of Charge Research Articles

A totally coupled multi time-scale framework containing full parameters online identification and SOC real-time estimation of lithium-ion battery based on a fractional order model

Fractional order model (FOM) is one of the most promising choices for lithium-ion battery simulation and internal states estimation due to its high modeling accuracy. However, online identifying the order and parameters of FOM is extremely tough, which also makes it difficult to be applied in complex and variable operating conditions. To address this issue, this paper proposes a totally coupled multi time-scale framework that contains full parameters online identification of FOM and state-of-charge (SOC) real-time estimation of lithium-ion battery. Firstly, a novel global optimization algorithm named improved particle swarm optimization is designed to update the order of FOM on macro-time scale with low computational burden. On the basis of the determined order, the rest parameters of FOM are further identified by forgetting factor recursive least square algorithm on micro-time scale. With those online identified parameters, adaptive extended Kalman filter is finally adopted to track battery SOC in every instant. The estimation accuracy, adaptability to different operating temperatures and battery aging, and robustness ability against uncertain initialization of the developed method is verified under Federal Urban Driving Schedule tests. The corresponding results demonstrate that the proposed method can realize accurate full parameters online identification of FOM and precise SOC real-time estimation against different temperatures, battery aging states and initialization. Using the developed method, the mean absolute error (MAE) and root mean square error (RMSE) of SOC estimation of the fresh battery under temperature ranging from 0 °C to 50 °C can be limited below 0.9 % and 1.1 %, respectively. Even if the SOC-OCV relationship is not timely updated, those two indexes of the aged battery achieved by the proposed method can still be controlled within 3 % under temperature ranging from 0 °C to 50 °C. Moreover, the comparison with other typical FOM-based estimation methods is also conducted, whose results indicate that the developed method has apparently superior comprehensive performance on estimation accuracy and adaptation.

Platooning Cooperative Adaptive Cruise Control for Dynamic Performance and Energy Saving: A Comparative Study of Linear Quadratic and Reinforcement Learning-Based Controllers

In recent decades, the automotive industry has moved towards the development of advanced driver assistance systems to enhance the comfort, safety, and energy saving of road vehicles. The increasing connection and communication between vehicles (V2V) and infrastructure (V2I) enables further opportunities for their optimisation and allows for additional features. Among others, vehicle platooning is the coordinated control of a set of vehicles moving at a short distance, one behind the other, to minimise aerodynamic losses, and it represents a viable solution to reduce the energy consumption of freight transport. To achieve this aim, a new generation of adaptive cruise control is required, namely, cooperative adaptive cruise control (CACC). The present work aims to compare two CACC controllers applied to a platoon of heavy-duty electric trucks sharing the same linear spacing policy. A control technique based on reinforcement learning (RL) algorithm, with a deep deterministic policy gradient, and a classic linear quadratic control (LQC) are investigated. The comparative analysis of the two controllers evaluates the ability to track inter-vehicle distance and vehicle speed references during a standard driving cycle, the string stability, and the transient response when an unexpected obstacle occurs. Several performance indices (i.e., acceleration and jerk, battery state of charge, and energy consumption) are introduced as metrics to highlight the differences. By appropriately selecting the reward function of the RL algorithm, the analysed controllers achieve similar goals in terms of platoon dynamics, energy consumption, and string stability.

A Novel Method for Battery SOC Estimation Based on Slime Mould Algorithm Optimizing Neural Network under the Condition of Low Battery SOC Value

The State of Charge (SOC) is a crucial parameter in battery management systems, making accurate estimation of SOC essential for adjusting control strategies in automotive energy management and ensuring the performance of electric vehicles. In order to solve the problem that the estimation error of the traditional BP neural network increases sharply under complex conditions and low battery SOC values, a recurrent neural network estimation method based on slime mould algorithm optimization is proposed. Firstly, the data are serialized to include multiple discharge data. Secondly, the data are input into a recurrent neural network for SOC estimation, with a self-attention mechanism added to the network. Furthermore, it is found in the experiment that parameters have an impact on the estimation accuracy of the neural network, so the slime mould algorithm is introduced to optimize the parameters of the neural network. The experiment results show that the maximum error of the novel method is limited to within 5% under two conditions. It is worth noting that the SOC estimation error at low SOC value decreases instead of increasing, which shows the advantages of the novel method.

Modeling of battery energy storage systems for AGC performance analysis in wind power systems

Battery energy storage system (BESS) is being widely integrated with wind power systems to provide various ancillary services including automatic generation control (AGC) performance improvement. For AGC performance studies, it is crucial to accurately describe BESS’s power regulation behavior and provide a correct state of charge (SOC). In addition, BESS model is required to maintain a sufficiently high simulation speed, since it usually is involved in hour- or day-scale simulations. Different from existing models, this paper develops a new BESS model which considers DC-AC converter control, DC link circuit and switching loss, thus providing an accurate power regulation behavior. In addition, it also considers the nonlinear relationship between open-circuit voltage and SOC of battery so that it provides a correct SOC. As a whole, the BESS is simplified as a controlled current source with fundamental frequency (50/60 Hz) and described by algebraic phasor equations to reduce computational burden. This makes the proposed model capable of finishing hour- or day-scale simulations within a few minutes. Time-domain simulations are used to validate and compare the simulation accuracy with the classical first-order transfer function model and electromagnetic model. Based on the proposed model, this paper also introduces and verifies a new BESS-based strategy to improve the AGC performance of wind farms.

Extreme learning machine model with honey badger algorithm based state-of-charge estimation of lithium-ion battery

Accurate state-of-charge (SOC) detection was still a challenging task to complete due to complex battery dynamics and constantly changing external conditions. The formula for SOC was difficult to determine since external parameters including voltage, current, temperature, and battery arrangement were complex. Also the methods for estimating SOC that were already in use were not always appropriate for the same car operating in various road and climatic conditions. In all situations, the conventional methodologies did not deliver an accurate estimation performance. Here, a unique optimization-based Extreme Learning Machine (ELM) was created to accurately determine a battery's SOC and enhance the operation and safety of battery systems. A lithium ion battery was first created, and data on its current, voltage, SOC, capacity, duration, and discharge rate were gathered to produce a real-time dataset at several temperatures, including 00,250 and 450. The dataset underwent additional pre-processing to standardize the values and enhance the accuracy of the data. To determine the precise state of the battery, these pre-data were loaded into the ELM model. However, the performance of ELM was significantly influenced by the length of training and the number of neurons in a hidden layer. An advanced Honey Badger Optimization Algorithm (HBA) was used to choose the appropriate hidden neurons and increase the estimation accuracy in order to overcome this problem. The proposed SOC estimation model provides 97% accuracy in the FUDS drive cycle and 99% accuracy in the US06 drive cycle. The proposed model provides a well performance for estimating SOC in lithium-ion battery at various temperature, also the proposed model was fit for real time implementation.

A multi-mode coordinated operation control strategy for optical storage DC microgrid

The proposed microgrid provides a new way to explore and makes usage of available solar energy resources. In order to realize the energy management of microgrid, this paper describes a multi-mode coordinated operation control strategy with the main control objective of ensuring the DC bus voltage stability and the mode division depends on the battery charge state and the source-load power state inside the microgrid system. The simulation model is established in PSCAD/EMTDC, and the simulation results verify the rationality and effectiveness of the proposed coordinated control strategy.

An improved coulomb counting method based on non-destructive charge and discharge differentiation for the SOC estimation of NCM lithium-ion battery

Accurate state of charge (SOC) estimation plays a crucial role in the safe and efficient operation of batteries. The coulomb counting (CC) approach has been used extensively in industrial production as a relatively easy-to-use and reliable algorithm for estimating battery SOC. However, the accuracy of standard CC method is constrained and it cannot satisfy some complex operating environment requirements. In this paper, a CC approach based on non-destructive charge and discharge differentiation is proposed on NCM battery. This method corrects the battery's initial SOC value using the open-circuit voltage method. It also corrects the actual capacity of the battery based on temperature, discharge rate and aging. Finally, it corrects the CC method's cumulative error using non-destructive capacity differentiation (dQ/dV) and voltage differentiation (dV/dQ). Experimental results show the maximum estimation error of the proposed approach is 3.33 % in the temperature range of −10 °C to 45 °C which is 15.57 % lower than the traditional OCV - CC method at −10 °C.

Multi-objective control and optimization of a stand-alone photovoltaic power conversion system with battery storage energy management

This paper addresses the problem of controlling a stand-alone photovoltaic (PV) energy conversion system integrated with a battery energy storage system. The study focuses on a series association of PV panels, a DC/AC converter, a Li-ion battery, and a DC load. The intermittent nature of PV power and frequent variations in load demand decrease battery lifetime and its charging performance. To mitigate these issues, an efficient battery charge controller is proposed to instantaneously balance the PV power flow delivered to the DC load and the battery, ensuring optimal utilization of PV power and appropriate battery charging. Based on available solar power, battery state of charge (SOC), and DC load demand The controller adapts to three charging modes, namely, maximum power point tracking (MPPT) charging mode, constant current (CC) charging mode, and constant voltage (CV) charging mode. Additionally, a novel energy management algorithm is designed to ensure battery safety and determine the system’s mode of operation, considering weather conditions and load demand variations. Interestingly, no solar irradiation or battery SOC sensors are required for the implementation of the control system. Nonlinear and robust controllers are developed to provide the necessary control input laws for the management algorithm. The robustness and stability of the system are verified using the Lyapunov theory. Furthermore, the paper quantifies the performance of the proposed strategy through a comparative analysis using integral of absolute error (IAE) indices against two conventional control approaches. Simulation results validate the effectiveness of the proposed controller strategy, demonstrating its high performance and ability to meet the specified objectives. This work presents an innovative approach to enhance the efficiency and reliability of stand-alone PV energy conversion systems with battery storage, offering promising prospects for sustainable energy applications.

Techno-economic Optimization of an Offshore Hybrid Power System: Argentine Basin Case Study

Ocean observation and monitoring relies on data gathered from multiple sources including gliders, cabled observatories, underwater vehicles, and moored buoys. Moored buoys, augmented by resident autonomous underwater vehicles, are potentially well-suited to long-term oceanographic data collection with the resolution and scope of data collection increased by utilizing in-situ power generation. While power needs could be provided by a single generation source (e.g., diesel generator, solar photovoltaics, or wave energy converter) with battery backup, a hybrid power system can potentially smooth seasonal variations that would otherwise require large generation and storage capacities, and be tailored to a locations’ resource availability. By reducing system size through hybridization, we have the potential to reduce overall system cost. To this end, we developed an optimization model that defines the generator and storage capacities of the cost-optimal hybrid power system for a given location and load profile. The model considers generation by wave energy converters, wind turbines, solar photovoltaics, diesel generators, and current turbines with a battery for energy storage. The model uses the defined load profile and location specific time series of resource availability for a time-domain simulation of the power generated and battery state of charge. Based on the power system size, mooring, and mission specific maintenance schedule, the model calculates capital and operating costs for each potential combination of generation and storage capacities. The optimization model searches the 6-dimensional design space to find the lowest cost system that can satisfy the load requirements. In this paper, we focus on a case study of a hybrid power system serving an ocean observation buoy with a resident autonomous underwater vehicle located at the Mid-Atlantic Shelf Break. Diagnostic metrics such as the cost-breakdown and generator capacity factors are evaluated to understand cost drivers and draw comparisons to the costs of single generation systems. Additionally, the model sensitivity to key assumptions such as battery life, maintenance vessel cost, survival loads, and required persistence are discussed.

A key-term separation based least square method for Hammerstein SOC estimation model

In this paper, a Hammerstein state of charge (SOC) estimation model for lithium-ion batteries with two inputs and single output is constructed to map the relation between battery SOC and its discharge current/voltage, and a key-term separation based least square (KT-LS) algorithm is studied to identify the model parameters so as to identify the battery SOC. The thought is, by adopting the key-term separation principle, to decompose the key-term from the linear element and to substitute the expression of the nonlinear element only into the decomposed key-term, thus to express the model output as a linear auto-regression form about parameters. Then the least square algorithm is performed to optimize parameters in the reformed auto-regression form of the Hammerstein SOC system. The advantages of the explored the KT-LS algorithm contains the least parameters required, thus the calculation burden of the KT-LS algorithm is small compared with the over-parameterized model based algorithm. In the simulation experiment part, the performance of the KT-LS algorithm under different conditions are analyzed under UDDS and LA92 conditions.; the performance comparison between the KT-LS algorithm and the key-term separation based stochastic gradient (KT-SG) algorithm is carried out; the performance of the KT-LS algorithm based Hammerstein model method is compared with that of the back propagation (BP) neural network, the Long Short Term Memory (LSTM) neural network and the Extended Kalman filter (EKF) method. The simulation results show that the KT-LS algorithm based Hammerstein SOC model can effectively estimate the SOC of lithium-ion batteries.

Reducing the Fuel Consumption of an Hybrid Electric Vehicle With the Use of Model Predictive Control-Case Study

The article presents operational aspects related <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">to energy management strategy (EMS) - model predictive control (MPC). The mathematical model showing the synergy of working machines of the hybrid electric vehicle (HEV) was presented. Then the results of road tests have been shown. They were based on the factory control of the above-mentioned system. The results of the operating parameters of the system according to the factory control and the results of the operating parameters according to the MPC with different linear quadratic tracking (LQT) curves were compared. Application of thirteen reference LQT trajectories results in lower fuel consumption of the HEV. It led to changes in the power and the battery state of charge (SOC): for factory control - from 50.1% (the beginning) to 56.1% (the end of course) and for MPC from 50.1% (the beginning) to 59.9% (the end of the course). The applied MPC with 13 reference trajectories (LQT) of power machines of the series-parallel HEV allowed for fuel savings on the level of 4%.

Regenerative braking energy recovery control strategy for electric vehicles with battery temperature measurement

Aiming at the problems of short mileage and overheating and easy damage of the battery in electric vehicles, the fuzzy control regenerative braking strategy with vehicle speed, braking intensity, battery state of charge (SOC), and the introduction of battery temperature as fuzzy control input and regenerative braking distribution percentage as output are proposed; secondly, the ideal braking force distribution curve and ECE regulations are used as the boundary to determine the braking force distribution range of pure electric vehicles. Finally, the control strategy designed in this paper is simulated under selected working conditions by ADVISOR software, and the effectiveness and superiority of this control strategy are verified. The strategy can not only reduce the damage to the battery caused by high temperatures but also obtain more braking energy.

A switchable indicator for active balance of the lithium-ion battery pack using a bypass equalizer

Balance techniques are critical for the Battery Management System (BMS) of a battery pack. If not well balanced, the performance of the battery pack will always be limited by the weakest cell. Battery State of Charge (SOC) is naturally an effective indicator for balancing, yet the SOC estimation cannot always be accurate, which may further induce uncertainties to the balance performance. Aiming to alleviate this issue, this paper proposes a switchable indicator for balancing a series-connected battery pack using a bypass equalizer with a compact topological structure, high efficiency and fault tolerance ability. The proposed switchable indicator enables automatically selecting the balance indicators between voltage and SOC, and a new balancing strategy is thus designed to utilize this switchable indicator. The proposed method is proven by both simulation and experiment on a lithium-ion battery pack.

A fuzzy robust two-stage unscented Kalman filter method for uncertainty and state of charge estimation of lithium-ion batteries

For the practical state of charge (SoC) estimation of lithium-ion batteries, the unknown battery model uncertainties should be considered. Conventionally, the robust SoC estimation method uses a bounded matrix to model battery uncertainties. However, since the upper bound of the uncertainty is unknown, the robust estimation would be conservative and inaccurate. To accurately estimate the SoC of lithium-ion batteries, this paper proposes a novel fuzzy robust two-stage unscented Kalman filter (FRTSUKF) method to model the battery uncertainty with unknown statistical characteristics. The proposed estimator is able to estimate the model uncertainties and does not require the statistical characteristics of the uncertainties. Using the estimated uncertainties, the SoC estimation is corrected, and the destructive effect of the uncertainties on the estimation accuracy is eliminated correctly. To make the estimation more accurate and practical during experiments, the covariance matrix of the measurement noise is recursively adjusted using a fuzzy system. In addition, as the battery aging increases the model uncertainty, the proposed observer is able to adjust the SoC estimation at each battery life period using the estimated uncertainties. In the first stage, the state variables of the battery model are estimated. The covariance matrix of the measurement noise is recursively adjusted using a fuzzy system, which makes the estimation more accurate and practical during experiments. In the second stage, the unknown model uncertainties are estimated. Based on the estimated uncertainties, state variables, including SoC, are adjusted to compensate for the adverse effects of the model inaccuracy on the state estimation. The experimental results show that in addition to the ability for the battery model uncertainty estimation, the proposed estimator is more accurate, as much as 1 % and more robust than other Kalman-based methods for aged batteries with more uncertain models.

An RNN With Small Sequence Trained by Multi-Level Optimization for SOC Estimation in Li-Ion Battery Applications

To ensure safe operation, the high-precision estimation of the state of charge (SOC) in the battery management system (BMS) is relied on. The classical recurrent neural network (RNN) has a gradient and poor accuracy problem, and the RNN with additional gates is complex and hard to apply in engineering. To address these issues, the RNN with small sequence trained by multi-level optimization is proposed in this paper to improve the accuracy and the gradient problem of the SOC estimation. First, the small sequence is introduced to improve the gradient problem and running speed of RNN and the SOC post filter is used to increase the continuity of SOC estimation. Then, the particle swarm optimization (PSO) is used to pre-train the RNN to obtain the optimal weight and threshold value, and the hybrid optimizer of Adam and stochastic gradient descent (SGD) is utilized to improve the accuracy of SOC estimation. The experimental data of the charging and discharging test, and the performances of the proposed method, the nonlinear autoregressive with exogeneous inputs neural network (NARX), RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) for estimating the SOC of lithium batteries are compared. The results show that the estimation error of the RNN is within 3.66% and that of NARX is within 6%, and the proposed method, LSTM, and GRU reduce the error to 0.47%, 1.04%, and 0.48%, respectively. Moreover, the calculation time of the proposed method is 1/3 and 1/9 of that of LSTM and GRU. Besides, the proposed method is robust to different vehicle cycles, measurement noises, temperature variations, and battery aging.

A Dual Kalman Filtering Algorithm for Estimating the SOC of Lithium-Ion Batteries with LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 Cathode Based on Multi-Innovation and Schmidt Orthogonal Transformation

Accurately estimating the state of charge (SOC) is imperative for ensuring safe and dependable battery utilization. However, accurately calculating SOC for LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 (LMFP/NCM) batteries can be challenging due to their two flat voltage platforms and significant temperature dependence. To improve estimation accuracy, a battery SOC estimation method based on a dual Kalman filter (DKF) was proposed. The adaptive unscented Kalman filter (AUKF) process starts with the introduction of Schmidt orthogonal transform, which is subsequently employed in the algorithm’s sampling point selection procedure to mitigate computational complexity. Moreover, the utilization of the multi-innovation theory serves to enhance the accuracy of algorithmic estimation. The extended Kalman filter is used to identify the parameters of the equivalent circuit model online while simultaneously carrying out battery SOC estimation. This approach mitigates the impact of variations in battery model parameters during charging and discharging processes. Under complex conditions, the algorithm’s average error is less than 0.53%, demonstrating its effectiveness in improving SOC estimation accuracy as evidenced by comparison between experiment and simulation results. It has reference significance for optimizing LMFP/NCM battery SOC estimation.

Techno-Economic Framework for Electric Vehicle Battery Swapping Stations

Electric vehicle (EV) battery swapping stations (BSWS) are an important aid in rapid transport electrification, especially in developing countries where per capita income is low and expensive battery prices discourage EV penetration. Consequently, a BSWS model, where the BSWS owns the battery, can help in EV penetration and rapid transport electrification. This paper presents a new EV BSWS model to obtain a suitable trade-off between swapping time and the quality of battery health indicators. The state of charge (SOC), battery abuse, and battery degradation are measured/estimated online, while the state of health (SOH) is determined offline. The cost of the swapping cycle is determined by considering multiple cost components and penalties. The modeling approach not only enables determining a fair and affordable price for each swapping cycle for EVs but also helps to keep a check on the battery’s SOH and remaining useful life (RUL) during swapping without significantly increasing the battery swapping times compared to the average refueling time of internal combustion engine (ICE) vehicles. The swapping cost of the BSWS model is also compared with home and commercial charging station (HCS & CCS) models under different assumptions. The analysis suggests that the battery can be swapped in 6 minutes for light vehicles, comparable to the complete tank refueling of ICE vehicles. Moreover, with appropriate consideration of electric vehicle opportunity profit (EVOP), the BSWS remains feasible compared to HCS and CCS even at a high-profit margin of 750%. Also, battery degradation is considered using the economic model’s degradation cost component, and its link with different SOC strategies is explored. Again, results suggest that SOC strategies can be a helpful way for BSWS to maximize battery life and increase its profits. Moreover, considering EVOP along with battery degradation and SOC strategies further highlights the feasibility of BSWS.

Comparison of the SOC Estimation Method for Lithium Battery Based on Simulink

SOC (State Of Charge, SOC) represents the state of charge of a battery and is one of the important indicators to measure the energy usage of the battery. In the field of automotive energy, accurate battery SOC estimation is of great significance for improving the range, service life, and safety of electric vehicles. It is an important part of battery management and energy management. The current SOC estimation methods are diverse, and the ampere-hour integration method has the advantages of high accuracy and simple implementation. The EKF (Extended Kalman Filter) algorithm provides solutions for nonlinear problems such as SOC estimation of batteries. Therefore, these two methods are the preferred choice for some vehicle manufacturers and BMS enterprises. This study selects a first-order RC equivalent circuit model for parameter identification, builds a circuit model on Simulink, and compares and verifies the error of the AH integration method and EKF algorithm.

Fuzzy-based optimal energy management strategy of series hybrid-electric propulsion system for UAVs

Hybrid-electric propulsion system (HEPS) has gained significant attention in unmanned aerial vehicles (UAVs) due to its potential to significantly reduce fuel consumption and pollutant emissions. Effective energy management strategies are crucial in ensuring the efficient operation of HEPS in UAVs, as they involve multiple power and energy sources. While conventional equivalent consumption minimum strategy (ECMS) is effective in distributing engine and battery output power, it struggles to maintain the battery’s state of charge (SOC) within desired levels in case of engine failure. The present study introduced the fuzzy logic control-equivalent consumption minimum strategy (FLC-ECMS), a fuzzy-based composite energy management strategy that incorporates the robustness and adaptability of fuzzy logic control. Through simulation tests under general aviation cruise flight and cargo transport flight scenarios, the results indicated that hybrid UAV equipped with the HEPS can decrease fuel consumption and CO2 emissions by at least 18.6% and NOx emissions by 10.1% in the first profile and by 23.6% and 11.2% in the second profile, compared to conventional oil-powered UAV. The FLC-ECMS method not only reduces fuel consumption and emissions, but also maintains the battery SOC within permitted range and minimizes the difference between front and back SOC. This study provides new insights into the optimal energy management of HEPS for complex profiles in UAVs, highlighting the significance of UAVs in reducing environmental impacts, and has the potential for broader application in other electric propulsion systems.

State of charge estimation of lithium-ion batteries using improved BP neural network and filtering techniques

The state of charge (SOC) is a critical parameter in the battery management system (BMS), and its accurate estimation is essential for ensuring the safety and reliability of batteries. This paper presents a lithium-ion battery SOC estimation method that combines an improved neural network with a filtering algorithm. Firstly, the backpropagation (BP) algorithm is chosen as the architecture of the neural network in the hybrid method due to its strong nonlinear approximation ability, and the particle swarm optimization (PSO) algorithm is used to optimize it to avoid falling into local optimal solutions. By combining the search ability of PSO with the learning ability of the BP neural network, the accuracy of the neural network model is improved. The proposed method integrates the PSO-BP model with the extended Kalman filter based on minimum error entropy (MEE-EKF). PSO-BP is utilized as the measurement equation for MEE-EKF, while the ampere-hour integration method is employed as the state equation to achieve closed-loop SOC estimation. Finally, experimental validation is conducted under four typical operating conditions and one random condition across a wide temperature range. The results demonstrate that the proposed method achieves high accuracy across all conditions compared with the results of other algorithms, with a maximum absolute error of not exceeding 3.13%, a mean absolute error of less than 0.54%, and a root mean square error of no more than 0.66%.