State Of Charge Model Research Articles

A Novel Transfer Learning-Based Cell SOC Online Estimation Method for a Battery Pack in Complex Application Conditions

The crucial function of a battery management system is to estimate the state of charge (SOC). However, complex application conditions like different temperatures, aging, and inconsistency between cells would cause a distribution difference between data domains and lead to a significant estimation error in the established SOC model. Transfer learning's domain adaptation offers a way to lessen the disparity in distribution across the data domains, and yet it also requires that the target space be the same across domains, which is challenging to do with the SOC online estimate. This article proposes a SOC differential processing method as well as designs a combined transfer learning method. As experiments of actual battery pack provided by China First Automobile Work-based confirmed, the proposed method can obtain precise and robust SOC estimation results with a mean absolute percentage error of less than 1.5%.

Joint electric vehicle routing and battery health management integrating an explicit state of charge model

Although fleet management has been extensively explored in transportation science, the rise of electromobility imposes several scientific challenges and opportunities. So far, few attempts were made to include battery degradation in the Electric Vehicle Routing Problem (EVRP). To do it realistically, it is necessary to model State of Charge (SoC), however most versions of routing problems use oversimplified SoC models or consider only energy consumption which leads to less robust solutions overall. In this work, a method for estimating battery degradation, which relies on a realistic SoC model is presented and incorporated into a new version of the electric vehicle routing problem. In this version, not only battery degradation is integrated, but also the possibility of limiting different vehicle parameters, such as maximum vehicle speed and acceleration. Due to the extra computational complexity related to the SoC and degradation models, a genetic algorithm capable of solving the aforementioned extended EVRP is presented. Finally, through different numerical experiments, the advantages of the proposed methodology are shown.

Estimation of State-of-Charge and Energy Efficiency of Supercapacitors Using a 1-D Electrochemical Model

Supercapacitors (SCs) have found a broad application spectrum due to their unique characteristics, including high power density and long cycle life [1]. Supercapacitors have been extensively studied to be used with Li-ion batteries for electric vehicle applications due to their complementary characteristics [1]. The energy management system of these applications relies on accurate information about the state-of-charge (SOC) and energy efficiency of the storage device at the cell level for effective monitoring and control. In the case of SCs, coulomb counting is the most commonly used SOC identification method; however, it entails certain challenges, including initial SOC information, effects of leakage current, and measurement noise [1]. Alongside improving the capacitive health diagnosis, it is critical to identify utilizable capacitance. During rapid charge/discharge of an SC, the ions in the electrolyte are immediately adsorbed/desorbed within the macro-pores, which leads to achieving the desired terminal voltage level instantaneously. However, this hinders ions’ penetration in the micropores, causing an inhomogeneous distribution of the ions across the electrodes, increasing the electrolyte resistance, resulting in poor utilizable capacitance and a drop in its energy efficiency [2]. Derived from a one-dimensional model [2, 3], a simplified SOC and energy efficiency estimation scheme of the SCs is proposed in this study. In the solid phases, the charge density of the SC is approximated as a function of its electrodes’ specific surface area, double-layer differential capacitance, and the potential drop across the double-layer. The SOC of the SCs can be expressed as a function of the solid phase charge density in the positive electrode or negative electrode, as defined in Eqn. (1). The energy efficiency (Eeff ) of the SCs can be expressed as a function of the ratio of bulk double-layer potential difference of the electrodes to the output terminal voltage change, as defined in Eqn. (2). SOC(t)=(ρ(t)/ρmax)×100 ...............(1); Eeff=(ΔΦ/ΔΦd/ΔV)×CrΔV2/2 ..............(2) where ρ and ρmax are the electrode charge density at t time and a fully charged state, respectively; ΔΦd is the bulk double-layer potential difference; ΔV is the terminal voltage change; and Cr is the rated capacitance.The electrochemical model is used to analyze the effectiveness of the proposed SOC and energy efficiency model considering a galvanostatic charge and discharge. In this limited scope, a concise response of the model compared to their respective standard methods is shown in Fig. 1. A SAFT 3500 F SC cell parameters are considered to perform the simulations [2, 3]. The results show that the SOC estimation scheme identifies the remaining charge without considering the initial SOC information. Similarly, the energy efficiency model calculates the energy received/delivered by the SC effectively. Figure 1

Prognosis of Li-ion Batteries Under Large Load Variations Using Hybrid Physics-Informed Neural Networks

The development of new modes of transportation such as electric vertical takeoff and landing aircraft and the use of drones for package and medical delivery have increased the demand for reliable and powerful electric batteries. Therefore, accurately predicting the degradation of a battery’s state-ofhealth (SOH) and state-of-charge (SOC) is a crucial albeit still challenging task. There is a need for models that can accurately predict the SOH and SOC while taking into account the specific characteristics of a battery cell and its usage profile. While traditional physics-based and data-driven approaches are used to monitor the SOH and SOC, they both have limitations related to computational costs or that require engineers to continually update their prediction models as new battery cells are developed and put into use in battery-powered vehiclefleets.Battery capacity degradation can vary from battery to battery and can also be influenced by changes in load due to internal thermal stress. While sophisticated electrochemistry-based models can provide precise predictions of the SOC during adischarge cycle when parameters are well-tuned, using highfidelity models for prognostics purposes can be computationally expensive. Those models also require tuning to specific battery types and at times to specific specimens, thus hindering generalization. In contrast, purely data-driven approaches can learn the relationship between input and output for SOC prediction based on load input, but they require a large and diverse training dataset and lack any physical or electrochemical understanding, making far-ahead predictions challenging if test loading conditions fall outside the training distribution. To address some of the drawbacks of the aforementioned modeling approaches, in this paper, we enhance a hybrid physics-informed machine learning version of a battery SOC model we presented in previous work to predict voltage dropduring discharge. The enhanced model captures the effect of wide variation of load levels, in the form of input current,which causes large thermal stress cycles. The cell temperature build-up during a discharge cycle is used to identify temperature-sensitive model parameters. Additionally, we enhance an existing aging model built upon cumulative energy drawn by introducing the effect of the load level. We then map cumulative energy and load level to battery capacity with a Gaussian process model. To validate our approach we use a battery aging dataset collected on a self-developed testbed, where we used a wide current level range to age battery packs in accelerated fashion. Prediction results show that our model can be successfully calibrated and generalizes across all applied load levels.

Predictive hierarchical eco-driving control involving speed planning and energy management for connected plug-in hybrid electric vehicles

The connected vehicle technique has offered great opportunities to improve further plug-in hybrid electric vehicles (PHEVs) fuel economy. In this context, a predictive hierarchical eco-driving control scheme is proposed for connected PHEVs under a car-following scenario containing a cloud-layer speed planner and vehicle-layer energy management. In the cloud layer, based on the traffic data separately processed by the dynamic programming (DP) algorithm and k-means clustering method, the reference state of charge (SOC) model, the energy consumption model, the equivalent factor model and the variable-horizon speed predictor can be constructed, respectively. And for ensuring safety, comfort and fuel economy in the car-following process, the energy and SOC models are used separately as the index and state equation in the data-driven speed planning problem. Then, with the driving pattern recognition technique, the power allocation under the planned speed can be conducted by integrating the adaptive equivalent consumption minimization strategy (ECMS) with the model predictive control (MPC), thus guaranteeing fuel economy, adaptability and near-global optimality with high computational efficiency. Compared with other strategies, the effectiveness and advantages of the proposed scheme are validated in the joint simulation platform of MATLAB/Simulink and GT-SUITE.

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.

A Hierarchical Identification Method for Lithium-Ion Battery SOC Based on the Hammerstein Model

Two-input one-output Hammerstein model consists of two parallel nonlinear static blocks followed by a linear dynamic part. By using Hammerstein structure to map relation between a battery State of Charge (SOC) and its terminal voltage/current, a hierarchical stochastic gradient algorithm is studied to estimate parameters of Hammerstein SOC model, so as to predict battery SOC. Firstly, the Hammerstein model is transformed into a bilinear parameter system with the least number of required parameters. Then, a hierarchical stochastic gradient algorithm with a forgetting factor is used to update the two sets of model parameters of the bilinear parameter system, so as to realize SOC estimation. Furthermore, the experiment platform of lithium-ion battery was built and the data of the urban dynamometer driving schedule (UDDS) profile and the Los Angeles 92 (LA92) profile were collected. Finally, the MATLAB simulation results show that the proposed parameter optimized method based Hammerstein model has the advantages of fast convergence speed and high SOC estimation accuracy.

Joint Prediction of the State of Charge and the State of Health of Lithium-Ion Batteries Based on the PSO-XGBoost Algorithm

Lithium-ion batteries are widely used in power grids as a common form of energy storage in power stations. The state of charge (SOC) and state of health (SOH) reflect the capacity and lifetime variation in the Li-ion batteries, and they are important state parameters of Li-ion batteries. Therefore, the establishment of accurate SOC and SOH prediction models is an essential prerequisite for the correct assessment of the status of lithium batteries, the improvement of the operational accuracy of energy-storage stations, and the development of maintenance plans for energy-storage stations. This paper first analyzes the correlation between SOC and SOH, and then proposes a joint SOC and SOH prediction model using the particle swarm optimization (PSO) algorithm to optimize the extreme gradient boosting algorithm (XGBoost), which takes into account the dynamic correlation between SOC and SOH dynamics, thus enabling more accurate SOC and SOH prediction. Finally, the prediction model is validated using the Oxford battery aging dataset. The correlation between SOC and SOH is verified by comparing the joint prediction results with the SOC individual prediction results. Then, the prediction results of the PSO-XGBoost model, the traditional XGBoost model, and the long short-term memory neural network are compared to verify the effectiveness and accuracy of the PSO-XGBoost model.

Hierarchical AGC Dispatch With Detailed Modeling of Energy Storage System Behaviors

The key challenge for automatic generation control (AGC) dispatch lies in the contradiction between the detailed modeling required for optimal dispatch and the tight calculation time. The current method includes (1) the heuristics method that allocates real-time commands based on certain rules (fast but nonoptimal) and (2) the proactive dispatch method with a detailed AGC dispatch model (relatively optimal but the forecast error exists). The development of renewables and the prevalence of energy storage systems (ESSs) with costly degradation calls for combining the advantages of heuristics and proactive methods in AGC dispatch. With this in mind, a hierarchical AGC dispatch method is proposed. A proactive AGC dispatch model embedding state of charge (SOC) recovery is established to make the best use of regulating resources while avoiding the degradation of ESSs. A uniform SOC model considering comprehensive ramping circumstances of ESSs is proposed with low model complexity. A theoretical analysis demonstrates the desired modeling accuracy. To compensate for the forecast error, real-time AGC dispatch is considered. The dead band and forced SOC recovery of ESSs are used to alleviate their degradation. Time-domain simulations based on practical data demonstrate that the proposed method can improve the system frequency performance while obviously reducing the degradation of ESSs.

State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence

Enhancing the accuracy of the battery state of charge (SOC) estimation is essential in developing more effective, dependable, and convenient electric vehicles. In this paper, a hybrid CNN and gated recurrent unit-long short-term memory (CNN-GRU-LSTM) approach, which is a recurrent neural network (RNN) based model with an explainable artificial intelligence (EAI) was used for the battery SOC estimation, where the cell parameters were explicitly synchronized to the SOC. The complexed link between the monitoring signals related to current, voltage, and temperature, and the battery SOC, was established using the deep learning (DL) technique. A LG 18650HG2 li-ion battery dataset was used for training the model so that the battery was subjected to a dynamic process. Moreover, the data recorded at ambient temperatures of −10 °C, 0 °C, 10 °C, and 25 °C are fed into the method during training. The trained model was subsequently used to estimate the SOC instantaneously on the testing datasets. At first, the training process was carried out with all temperature data to estimate the SOC by the trained model at various ambient temperatures. The proposed approach was capable to encapsulate the relationships on time into the network weights and, as a result, it produced more stable, accurate, and reliable estimations of the SOC, compared to that by some other existing networks. The hybrid model achieved a mean absolute error (MAE) of 0.41% to 1.13% for the −10 °C to 25 °C operating temperatures. The EAI was also utilized to explain the battery SOC model making certain decisions and to find out the significant features responsible for the estimation process.

Open-Circuit Voltage Models for Battery Management Systems: A Review

A battery management system (BMS) plays a crucial role to ensure the safety, efficiency, and reliability of a rechargeable Li-ion battery pack. State of charge (SOC) estimation is an important operation within a BMS. Estimated SOC is required in several BMS operations, such as remaining power and mileage estimation, battery capacity estimation, charge termination, and cell balancing. The open-circuit voltage (OCV) look-up-based SOC estimation approach is widely used in battery management systems. For OCV lookup, the OCV–SOC characteristic is empirically measured and parameterized a priori. The literature shows numerous OCV–SOC models and approaches to characterize them and use them in SOC estimation. However, the selection of an OCV–SOC model must consider several factors: (i) Modeling errors due to approximations, age/temperature effects, and cell-to-cell variations; (ii) Likelihood and severity of errors when the OCV–SOC parameters are rounded; (iii) Computing system requirements to store and process OCV parameters; and (iv) The required computational complexity of real-time OCV lookup algorithms. This paper presents a review of existing OCV–SOC models and proposes a systematic approach to select a suitable OCV–SOC for implementation based on various constraints faced by a BMS designer in practical application.

Development of low complexity open circuit voltage model for state of charge estimation with novel curve modification technique

The accurate estimation of State of Charge (SOC) is crucial for safe operation and long-life cycle of lithium-ion battery (LiB) system. Out of the several methods used for SOC estimation, data-based approaches have favorable results. These methods require higher computations and extensive training. This restricts the implementation of such methods using low computational power, cost-effective general-purpose microcontroller. In this paper, a simple, low complexity, open-circuit voltage (OCV) model with seven coefficients, third-degree polynomial function has been proposed for the SOC estimation. The performance of the proposed model is compared with the existing OCV models. To enhance the accuracy further, the model is suitably modified using a two-degree polynomial function dependent on the remaining useful life (RUL). The proposed model is validated on the experimental as well as standard datasets and results in terms of RMS error and R2 score are presented. The investigational results show that the SOC estimation based on proposed OCV–SOC model can efficiently limit the error below 0.01885 for entire SOC range and 0.01448 for partial (10−90%) SOC range.

A novel aging parameter method for online estimation of Lithium-ion battery states of charge and health

Real time monitoring and health managements of the battery require reliable estimation algorithms that are adaptive to the surrounding environment. However, modeling the performance degradation of batteries can be extremely challenging, as there are numerous factors that influence battery’s both internal and external conditions. This paper proposes a data driven approach to calculate a new aging parameter for Lithium-ion batteries by combining the ideas of incremental capacity analysis and voltage drops in discrete time. With this new parameter, we employ regression analysis and curve fitting methods to build two forecasting algorithms to track the state of charge (SOC) and state of health (SOH) of Lithium-ion battery under constant current discharge conditions. Particularly the proposed SOC and SOH models are decoupled from each other, and assume no prior relationships between open circuit voltage (OCV) and SOC since their input values are terminal measured voltages only. We also suggest how the proposed scheme can be extended to other operating conditions such as constant voltage/power. Overall, this paper introduces a novel aging parameter of Lithium-ion batteries, that provides a new paradigm of the battery state estimations.

Prediction of the Battery State Using the Digital Twin Framework Based on the Battery Management System

Electric Vehicles (EVs) reliance on batteries, which currently have lower energy and power densities than liquid fuels and are prone to aging and performance degradation over time, restricts their mainstream adoption. With applications like electric vehicles and grid-scale energy storage, effective management of lithium-ion batteries is a vital enabler for a low-carbon future. Monitoring the battery’s condition of health and charge over the lifetime of an EV is, therefore, a highly pertinent issue. Battery Management Systems (BMS) are used during the operation of EVs to monitor, estimate and control battery states to ensure that batteries can function effectively and safely. Additionally, the materials composition, system design, and operating circumstances substantially impact a battery’s usable life, making it more challenging to govern and maintain battery systems. This work proposes the structure of a battery digital twin-based battery for the electronic vehicle, which has the potential to enhance BMS situational awareness greatly and enable the optimal functioning of battery storage units. Digitalization and Artificial Intelligence (AI) present an opportunity and offer a Digital Twin (DT) of the EV battery as a solution to the difficulty of onboard computation for the incremental State Of Health (SOH) and State Of Charge (SOC) based on Extreme Gradient Boost (XGBoost) and Extended Kalman Filter (EKF) to predict the state estimate. The battery’s condition has been determined by using the EKF, which can provide vital information for maintenance. The battery’s usable life can be extended with an accurate estimate of the SOC to continue then a learning-based prediction approach to gauge the battery’s health state is suggested in order to increase battery life. A SOC model is frequently retrained to depict the effects of aging, and a SOH model is often performed to foretell the reduction in the highest battery capacity. According to a result, DT models are useful for managing batteries, and full life cycle statistics are important for planning the battery’s upgrade path.

A Particle-Swarm-Optimization-Based Approach for the State-of-Charge Estimation of an Electric Vehicle When Driven Under Real Conditions

Recent developments in lithium-ion (Li-ion) storage technology have enabled a revolution in the automotive industry. Fully electric vehicles (EVs) operate under the most diverse combination of driving and environmental conditions affecting the autonomy range. In other words, an equal state-of-charge (SOC) on two same model EV does not mean the same traveling distance since the conditions such as the state-of-health (SOH) of the battery, type of driver and even the type of route will influence the EV performance. Typically, SOC estimation algorithms are proposed and validated under controlled laboratory conditions. However, when real conditions are present, it is necessary to incorporate new tools capable of handling the diverse variability present in all the conditions. For instance, the topography of the route influences the current that the battery pack delivers, and the performance on the same route can be affected by the SOH. One of the main concerns for EV owners is that once a battery pack is installed, it becomes almost impossible to perform laboratory tests under controlled conditions. This paper proposes a novel approach to estimate the SOC by extending an existing SOC model (obtained in laboratory conditions) with the novelty of the assistance of Particle-Swarm-Optimization (PSO) to estimate the model parameters using real EV driving data. The data was obtained by a real-driving experiment, which consists on driving the EV in a complete discharge cycle on a highway. During this experiment, the initial SOC was at 100\%, and the idea was to discharge the battery pack driving through a highway where the driving conditions are almost uniform making it possible to characterize the SOC curve. Then, PSO is used to estimate the model parameters, and afterwards the model is validated in different types of routes. The obtained results show that the proposed approach can estimate the SOC satisfactorily. In this regard, this type of real-driving experiment can be performed by any driver, and by combining the particular results with the proposed approach, the users can personalize the SOC estimation model to their vehicles, and even more, create their own knowledge base of their EV performance through time. Therefore, the real-driving experiment can be replicated when needed to update the model parameters, thus allowing a better understanding of the actual SOH of the battery pack. Furthermore, by combining the obtained model with the elevation profile of a given route, the user can assess where to stop in case that a recharge is necessary.

Modeling the Heat-Hydrogen Balance Characteristic of Hydrogen Energy Storage and Cooperative Dispatch of Wind-Hydrogen Hybrid System

The heat and hydrogen balance of the hydrogen energy storage system’s intermittent operation becomes a key factor affecting the performance of the wind-hydrogen hybrid system (W-HHS). This work designed a hydrogen energy storage system (HESS), including waste heat utilization. Then, a dual state of charge (SOC) model is established, in which hydrogen and heat storage is considered. Furthermore, based on the distributionally robust method, an optimal dispatch method of W-HHS is proposed to reduce the operation cost of conventional units in the grid and increase the revenue of the W-HHS. The previously proposed dual SOC model of heat-hydrogen balance is regarded as a constraint in this cooperative dispatch. The effectiveness and efficiency of the dual SOC model were verified on the IEEE 30-bus system with an actual wind plant data set. The results show that the hydrogen-heat dual SOC model can fully reflect the influence of heat and hydrogen balance on the operation of the W-HHS. The cooperative dispatch method improves the reliability of the W-HHS operation under the premise of ensuring the heat-hydrogen balance. When the constraints of hydrogen balance SOC and heat balance SOC are met simultaneously, the available power of the wind plant is 6–8% lower than the ideal situation. Parameter analysis indicates that reducing the heat dissipation coefficient can reduce the influence of the SOC constraint of heat balance on the dispatch strategy and increase the power output of the wind plant. When the heat dissipation coefficient is less than 1/1,200, the heat balance SOC constraint fails.

Optimal Charging Profile Design for Attaining Desired State of Charge in Symmetric Electrochemical Capacitor With Efficiency Analysis

The optimal utilization of ultracapacitor (symmetric electrochemical capacitor) for vehicular applications demands continuous monitoring and maintenance of its state of charge (SOC). The online estimation of SOC plays a vital role in the effective utilization of the energy storage capacity of ultracapacitors. In addition to monitoring SOC, high reliability in vehicle operation requires a predefined SOC level, as per the dynamic load requirement. For the ultracapacitor undergoing series of charge-discharge cycles, proper regulation of the SOC in accordance with the vehicle drive profile, increases the ultracapacitor life by avoiding possible stress due to undercharging/overcharging. Achieving the desired SOC in real-time warrants a mechanism for the optimal charging of the ultracapacitor. In this regard, the present article proposes an optimal charging scheme to attain a predefined SOC from any initial value, within a given time-frame. The proposed noniterative fractional calculus-based approach allows for analytically representing the charging current as a function of the desired SOC, charging duration and ultracapacitor model parameters. The scheme has been extensively validated experimentally for its effectiveness in attaining the desired SOC. Further, the appropriateness of the derived charging profiles for different operating scenarios has also been analyzed in terms of maintaining a tradeoff between faster charging and high charging efficiency.

On-line state of charge estimation of embedded metal hydride hydrogen storage tank based on state classification

With the further deterioration of environment and the depletion of fossil fuels, the alternative energy sources are urgently needed to be discovered. Hydrogen holds great promise thanks to its unlimited resources, high energy density and the environmentally friendly nature. However, its low volume density under normal temperature and pressure becomes the main challenge for on-board storage. Owing to its high potential of safety, one of the optimal solution for the future hydrogen vehicle is storing hydrogen using metal hydride (MH) under proper temperature and pressure. This work focuses on the state of charge (SOC) estimation of the embedded MH hydrogen storage tank. High precise estimating of the remaining energy will contribute to both the evaluation of reliability and the design of control strategy. A statistical model of SOC is proposed based on the database collected form laboratory experiments and real operation vehicle test. What’s more, a joint multi-classifier is designed to recognize the current state of reaction. Under this condition, the SOC of MH hydrogen storage tank is calculated through combining the state classifier and SOC model. This proposed on-line SOC estimation procedure is validated with the real operation vehicles in both charging and discharging process. It is proved to be effective with the estimation error of 0.2% during charging and 4.29% of discharging.

Optimal open-circuit voltage (OCV) model for improved electric vehicle battery state-of-charge in V2G services

Electric vehicles (EVs) with voltage-to-grid (V2G) capability are useful in augmenting grid capability to handle high energy demand of end users during peak periods. We propose a hybrid state-of-charge (SOC) battery model with aggregator to optimize battery charging and maintain grid stability during peak periods. The proposed SOC model leverages the advantages of three well-known previously proposed battery models namely: Shepherd, Unnewehr and Nernst models. The proposed hybrid model is a combination of the merits of the three specified empirical Lithium-ion battery models to optimize slow charging. This will enhance battery performance by improving its depth-of-discharge profile. This results in enhanced V2G capability and longer driving time for EV owners. Battery parameters used in the simulation are for Nissan Leaf 2019 EV. The proposed SOC model parameters are used to optimize a two-objective function which is used by the aggregator to maximize benefits to both EV owners and DSO. Multi-objective genetic algorithm (MOGA) is used to optimize the objective function because of its ability to obtain non-dominated solutions while still maintaining diversity of the solutions. From simulation results, proposed OCV model improves battery SOC by 10% after V2G operating period (2 p.m.) compared to a case without the model. Also, proposed model earns aggregator $445 and $45 more for voltage and frequency regulation services, respectively. Voltage stability of all 5 considered grid buses of the IEEE 33-node system remains at 0.9–1.0 p.u.

General Model and SOC Estimation of Battery

Monitoring the battery state is of great importance for the safety and normal of the systems which are powered by batteries. SOC (State of Charge) is one of the most important state parameters of battery. SOC cannot be measured directly. The Kalman filter algorithm is one of the techniques often applied to estimate SOC value. An accurate model is necessary for this algorithm. In this paper, a general SOC model is set up. It takes into account not only the difference between discharging and charging work conditions, but also the influence of the working atmosphere, such as temperature and discharging rate. Then based on this general model, unscented Kalman filter method is used to predict the SOC value. It can avoid the error which is caused by ignoring high-order terms, which is a shortcoming exist in the extended Kalman filter method. The simulation experiments prove the approach can get satisfactory results even when the measurement data is mixed with noise or the initial SOC value is not accurate.