State Of Charge Estimation Techniques Research Articles

State of Charge Estimation of Lithium Batteries in Electric Vehicles Using IndRNN

Accurate state of charge (SOC) estimation is considered as the main barrier to adopt Lithium-ion battery-based electric vehicles as alternatives to conventional internal combustion engine vehicles. There are several SOC estimation techniques recommended by many researchers still; they are all not accurate. This paper presents a robust and accurate SOC estimation battery model developed using an independently recurrent neural network (IndRNN). The proposed SOC estimation battery model is developed by training IndRNN constructively under various experimental datasets collected from Lithium Nickel Cobalt Aluminium Oxide battery cell at different ambient temperatures. Without any prior knowledge about battery internals, the proposed battery model successfully characterizes the non-linear behaviour of the battery effectively. Furthermore, its performance is proved by comparing with similar RNN architecture such as gated recurrent unit (GRU) and long short-term memory (LSTM). The attained results demonstrate that an IndRNN outperformed both GRU and LSTM in terms of accuracy under different electric vehicle drive cycle with minimal root mean square error of 0.7633% and mean absolute error of 0.6389% for diverse temperature conditions. Abbreviations: ANN: artificial neural network; BMS: battery management system; BPNN: back-propagation neural network; DFS: deep feature selection; EV: electric vehicle; FL: fuzzy logic; FNN: feedforward neural network; GA: genetic algorithm; GRU: gated recurrent unit; HWFET: highway fuel economy test; ICE: internal combustion engine; IndRNN: independently recurrent neural network; LA92: Los Angeles 92; LiB: lithium-ion battery; LSTM: long short-term memory; MAE: mean absolute error; MAX: maximum error; RNN: recurrent neural network; RMSE: root mean square error; ReLU: rectified linear unit; SOC: state of charge; SOH: state of health; SVM: support vector machine; US06: supplemental federal test procedure; UDDS: urban dynamometer driving schedule.

Robust Observer Design for Mitigating the Impact of Unknown Disturbances on State of Charge Estimation of Lithium Iron Phosphate Batteries Using Fractional Calculus

In recent times, lithium iron phosphate (LFP) batteries have found wide usage in electric vehicles (EVs). Efficient utilization of the storage capacity of any battery used in EVs is heavily dependent on the accuracy of state of charge (SOC) estimation. The classical SOC-OCV (open circuit voltage) based approach for estimation of SOC is quite ineffective under rapid variation in operating condition of EVs. The impact of parameter variations and unknown disturbances due to widely varying environmental conditions and drive terrain, demands a robust observer for SOC estimation. In this regard, this paper proposes a novel SOC estimation technique using a nonlinear fractional-order unknown input observer (FOUIO). The scheme based on the combined framework of fractional calculus and state estimation with unknown input is able to meet the challenges of SOC estimation in EVs by decoupling the plant disturbance inputs from the state estimation process. The optimally designed FOUIO using a linear matrix inequality (LMI) based formulation has been analytically proved for convergence. The proposed FOUIO has been validated and compared with reported SOC estimation schemes for different drive profiles in terms of accuracy and convergence speed. The numerical simulations and hardware in loop (HIL) based experimental results confirm the effectiveness of the proposed FOUIO in estimating the SOC robustly under different scenarios of battery operation.

배터리 임피던스 변화를 반영한 확장 칼만 필터를 기반으로 한 배터리 SOC 추정에 관한 연구

The state of charge(SOC) of battery should be accurately estimated because it is used as basic data for various functions of the battery management(BMS) as well as the remaining battery capacity. Extended Kalman Filter(EKF) based on a battery model is widely used as a real-time SOC estimation technique. However, the parameters of battery model are slightly different for each SOC section and changed by C-rate. As a result, if the battery parameters are used a fixed value, the accuracy of SOC estimation using EKF is decreased. In this paper, to improve the accuracy of the SOC estimation using EKF, the changes in battery parameters according to C-rate were analyzed for five SOC sections. Also, the accuracy of the SOC estimation according to the number of SOC sections was calculated by simulation. Experimental results show that the battery parameters reduce as C-rate increases. In addition, as the SOC decreased, the internal resistor R<SUB>i</SUB> and loss resistor R<SUB>d</SUB> increased, but the double layer capacitor C<SUB>d</SUB> decreased. Therefore, in order to increase the accuracy of SOC estimation using EKF, it is necessary to reflect the changes in battery parameters in each SOC section according to the C-rage. This was verified by simulation.

Experimental implementation of a smart battery charger for electric vehicles charging station

In this paper, an implementation of a DC/DC buck converter for electric vehicles charging station and a DSP based closed-loop digital controller design are presented and analyzed. The aim of this work is to achieve an improved control strategy for a Li-ion battery charger implemented on a Real-time test platform. The test platform consists of a popular power pole board (MPCA75136) dedicated to studying the DC/DC converters, and a DSP development kit (TMS320F28379D) that is used to drive the DC/DC buck converter. The control strategy is based on a digital control system containing the closed-loop current controller followed by a pulse width modulation block, and on a real time state of charge estimation technique for a Li-ion battery. However, the overall control design is modeled on Simulink via block diagrams, and automatically generated code that is targeted into the DSP processor. Simulation and experimental results have shown the effectiveness of the proposed test bench and its external digital control strategy via a charging scenario for electric vehicles batteries.

Optimal SoC Estimation Considering Hysteresis Effect for Effective Battery Management in Shipboard Batteries

The energy storage is critical in shipboard systems since there is no alternate energy in case of primary energy source failure. Hence, a battery management system (BMS) is necessary to monitor the state of the battery. Since a BMS operates the battery based on its state, an accurate estimation of the state of charge (SoC) is essential. Hysteresis is predominantly present in lead–acid batteries, whose effect is generally not accounted for in existing SoC estimation methods. In this work, the effect of hysteresis on SoC estimation is considered and the differential evolution-based SoC estimation technique is applied to accurately estimate the SoC while minimizing the hysteresis effect. A dynamic charge and discharge dynamic model of a 100-Ah lead–acid battery with ship thruster load is used to study the battery hysteresis. The battery model with the proposed SoC estimation and the BMS algorithm is simulated in MATLAB/SIMULINK 2018A and is tested under different charging and discharging conditions. The proposed model is validated in a real-time testbed comprising of a 62-Ah lead–acid battery connected to a 25 HP thruster of a boat. From the test results, it has been demonstrated that the proposed estimation algorithm reduces the estimation error thereby achieving an accurate SoC.

Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach.

Blockchain and Computational Intelligence Inspired Incentive-Compatible Demand Response in Internet of Electric Vehicles

By leveraging the charging and discharging capabilities of Internet of electric vehicles (IoEV), demand response (DR) can be implemented in smart cities to enable intelligent energy scheduling and trading. However, IoEV-based DR confronts many challenges, such as a lack of incentive mechanism, privacy leakage, and security threats. This motivates us to develop a distributed, privacy-preserved, and incentive-compatible DR mechanism for IoEV. Specifically, we propose a consortium blockchain-enabled secure energy trading framework for electric vehicles (EVs) with moderate cost. To incentivize more EVs to participate in DR, a contract theory-based incentive mechanism is proposed, in which various contract items are tailored for the unique characteristics of EV types. The contract optimization problem falls into the category of difference of convex programing, and is solved by using the iterative convex–concave procedure algorithm. Furthermore, we consider the scenario where the statistical knowledge of the EV type is unknown. In such a case, we demonstrate how to derive the probability distribution of the EV type by exploring computational intelligence-based state of charge estimation techniques, e.g., Gaussian process regression. Finally, the security and efficiency performance of the proposed scheme is analyzed and validated.

State-of-Charge Estimation for Li-Ion Batteries: A More Accurate Hybrid Approach

Modeling of battery energy storage systems used for applications, such as electric vehicles and smart grids, emerged as a necessity over the last decade and depends heavily on the accurate estimation of battery states and parameters. Depending on the battery-cell type and operation, a combination of algorithms is used to identify battery parameters and define battery states. This paper deals with robust Li-ion batteries modeling with a specific focus on a hybrid approach for a more accurate state-of-charge (SOC) estimation. The analysis presents a detailed description of the state-of-the-art stand-alone SOC estimation methods and focuses on a hybrid SOC estimation technique to improve accuracy under varying conditions. Emphasis is given on performance improvements of the proposed hybrid approach compared to the conventional methods, whereas a thorough experimental validation is presented to evaluate the accuracy of the proposed method.

State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries

Lithium-bismuth liquid metal batteries have much potential for stationary energy storage applications, with characteristics such as a large capacity, high energy density, low cost, long life-span and an ability for high current charge and discharge. However, there are no publications on battery management systems or state-of-charge (SoC) estimation methods, designed specifically for these devices. In this paper, we introduce the properties of lithium-bismuth liquid metal batteries. In analyzing the difficulties of traditional SoC estimation techniques for these devices, we establish an equivalent circuit network model of a battery and evaluate three SoC estimation algorithms (the extended Kalman filter, the unscented Kalman filter and the particle filter), using constant current discharge, pulse discharge and hybrid pulse (containing charging and discharging processes) profiles. The results of experiments performed using the equivalent circuit battery model show that the unscented Kalman filter gives the most robust and accurate performance, with the least convergence time and an acceptable computation time, especially in hybrid pulse current tests. The time spent on one estimation with the three algorithms are 0.26 ms, 0.5 ms and 1.5 ms.

A comparative study of ANN and Kalman Filtering-based observer for SOC estimation

Electrical Vehicle Batteries (EVB) study has gained a lot of interest in recent years, with the aim of better managing their use, due to the high changes in the electric vehicle dynamics and operational modes, which could cause severe damages to the battery if not properly managed. Recently lithium-ion (Li-ion) batteries have become the most suitable technology for electric vehicles, because of their interesting features such as a relatively long cycle life, lighter weight and high energy density. However, there is a lot of work that is still needed to be done in order to ensure safe operating lithium-ion batteries, starting with their internal status monitoring, cell balancing within a battery pack and thermal management. In this paper, a comparative study of two different methods for state of charge estimation techniques are presented: Kalman filtering observers and artificial neural network based observers. The respective results are compared in terms of accuracy, implementation requirement, and overall performances.

Reduced-Order Electrochemical Model-Based SOC Observer With Output Model Uncertainty Estimation

As an integral part of energy storage systems, Li-ion batteries require extensive management to guarantee their safe and efficient operation. Estimation of the remaining energy capability of the battery, usually expressed in terms of state of charge (SOC), plays an important role in any battery-powered application. Electrochemical model-based estimation techniques have proven very attractive for this purpose due to the additional information they provide regarding the internal battery operating conditions. A modified reduced-order model based on the single particle approximation of the electrochemical model, suitable for the real-time implementation of SOC estimation, is employed in this paper. This model, while maintaining some of the physical insights about the battery operation, provides a basis for an output-injection observer design to estimate the SOC. Output model uncertainties, originating primarily from the electrolyte-phase potential difference approximation and encountered mainly at higher discharge rates, are handled by incorporating an adaptation algorithm in the observer. Therefore, the proposed method, while being suitable for online implementation, provides an electrochemical model-based solution for battery SOC estimation over a wide range of operations. System stability and the robustness of the estimates given measurement noise are proved analytically using Lyapunov stability. Finally, accurate performance of the proposed SOC estimation technique is illustrated using simulation data obtained from a full-order electrochemical model of a lithium manganese oxide battery.

Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries

Battery state of charge (SOC) estimation is a crucial function of battery management systems (BMSs), since accurate estimated SOC is critical to ensure the safety and reliability of electric vehicles. A widely used technique for SOC estimation is based on online inference of battery open circuit voltage (OCV). Low-current OCV and incremental OCV tests are two common methods to observe the OCV-SOC relationship, which is an important element of the SOC estimation technique. In this paper, two OCV tests are run at three different temperatures and based on which, two SOC estimators are compared and evaluated in terms of tracking accuracy, convergence time, and robustness for online estimating battery SOC. The temperature dependency of the OCV-SOC relationship is investigated and its influence on SOC estimation results is discussed. In addition, four dynamic tests are presented, one for estimator parameter identification and the other three for estimator performance evaluation. The comparison results show that estimator 2 (based on the incremental OCV test) has higher tracking accuracy and is more robust against varied loading conditions and different initial values of SOC than estimator 1 (based on the low-current OCV test) with regard to ambient temperature. Therefore, the incremental OCV test is recommended for predetermining the OCV-SOCs for battery SOC online estimation in BMSs.

Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion

In this paper, advanced equivalent circuit models (ECMs) were developed to model large format and high energy nickel manganese cobalt (NMC) lithium-ion 20 Ah battery cells. Different temperatures conditions, cell characterization test (Normal and Advanced Tests), ECM topologies (1st and 2nd Order Thévenin model), state of charge (SoC) estimation techniques (Coulomb counting and extended Kalman filtering) and validation profiles (dynamic discharge pulse test (DDPT) and world harmonized light vehicle profiles) have been incorporated in the analysis. A concise state-of-the-art of different lithium-ion battery models existing in the academia and industry is presented providing information about model classification and information about electrical models. Moreover, an overview of the different steps and information needed to be able to create an ECM model is provided. A comparison between begin of life (BoL) and aged (95%, 90% state of health) ECM parameters (internal resistance (Ro), polarization resistance (Rp), activation resistance (Rp2) and time constants (τ) is presented. By comparing the BoL to the aged parameters an overview of the behavior of the parameters is introduced and provides the appropriate platform for future research in electrical modeling of battery cells covering the ageing aspect. Based on the BoL parameters 1st and 2nd order models were developed for a range of temperatures (15 °C, 25 °C, 35 °C, 45 °C). The highest impact to the accuracy of the model (validation results) is the temperature condition that the model was developed. The 1st and 2nd order Thévenin models and the change from normal to advanced characterization datasets, while they affect the accuracy of the model they mostly help in dealing with high and low SoC linearity problems. The 2nd order Thévenin model with advanced characterization parameters and extended Kalman filtering SoC estimation technique is the most efficient and dynamically correct ECM model developed.

State of charge estimation of LiFePO4 batteries based on online parameter identification

With the research object of LiFePO4 battery, this paper aims to correctly estimate the battery state of charge (SOC) by constructing a comprehensive SOC estimation strategy. Firstly, recursive least square (RLS) algorithm is adopted to realize online parameter identification of the equivalent battery model; and then an elaborate combination of RLS and Unscented Kalman Filter (UKF) is established, thus the battery model parameters used in UKF are actually obtained recursively by RLS; finally, SOC can be estimated by UKF. This strategy has an obvious adaptability due to the adoption of online parameter identification, so it is also called adaptive SOC estimation technique. Experimental results show that sometimes battery model parameters of different cells can be much different even though terminal voltages of these cells are very close or same when they are under resting state, and this inconsistency among LiFePO4 batteries is captured by the RLS-UKF strategy presented in this paper; and of course battery SOC can also be correctly estimated by using the continuously updated model parameters.

Sudden Drop in the Battery Level?

Battery State of Charge (SOC) estimation is a fundamental component of today's smartphones that affects the internal processes and observable behavior of the devices. This article systematically investigates and analyzes the SOC estimation techniques in smartphones. First, we discover that the voltage curve of a given smartphone implicitly captures the usable capacity of the battery while charging the mobile device. Second, we observe that today's SOC estimation techniques do not model battery capacity loss sufficiently to accurately capture the usable capacity. Finally, we report findings based on battery analytics of 2077 devices that validate the relationship between battery voltage and the usable capacity of a device. The presented results enable the development of more accurate battery gauges and metering solutions thus resulting in better power-saving decisions, recommendations for the users, and most importantly more reliable system.

A novel combined battery model for state-of-charge estimation in lead-acid batteries based on extended Kalman filter for hybrid electric vehicle applications

In this paper a novel combined battery model for state-of-charge (SoC) estimation in lead-acid batteries, based on extended Kalman filter (EKF) is presented. To obtain a more accurate SoC estimation technique, a combination of the two previously used models (RC and hysteresis battery models) is introduced; trying to compensate deficiencies of the individual models. The changes in the behavior of the battery are considered in the proposed SoC estimation method which makes it suitable for hybrid electric vehicle (HEV) applications. The effectiveness of the proposed method is verified using an experimental test. This Kalman filter modeling approach is shown to give SoC estimation error within 2% compared with Ah counting method; therefore, better results are obtained in comparison with the other conventional methods.