Showing papers on "State of charge published in 2017"

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

Abstract: Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks

Abstract: This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate ( $\text{LiFePO}_4$ ) and lithium titanate ( $\text{LTO}$ ) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS).

Operation of a Grid-Connected Lithium-Ion Battery Energy Storage System for Primary Frequency Regulation: A Battery Lifetime Perspective

Abstract: Because of their characteristics, which have been continuously improved during the last years, Lithium-ion batteries have been proposed as an alternative viable solution to present fast-reacting conventional generating units to deliver the primary frequency regulation service. However, even though there are worldwide demonstration projects, where energy storage systems based on Lithium-ion batteries are evaluated for such applications, the field experience is still very limited. In consequence, at present, there are no very clear requirements on how the Lithium-ion battery energy storage systems should be operated, while providing frequency regulation service and how the system has to reestablish its state of charge (SOC) once the frequency event has passed. Therefore, this paper aims to investigate the effect on the lifetime of the Lithium-ion batteries energy storage system of various strategies for reestablishing the batteries’ SOC after the primary frequency regulation is successfully delivered.

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Abstract: Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

Optimal Charging of Li-Ion Batteries With Coupled Electro-Thermal-Aging Dynamics

Abstract: Fast and safe charging protocols are crucial for enhancing the practicality of batteries, especially for mobile applications, such as smartphones and electric vehicles. This paper proposes an innovative approach to devising optimally health-conscious fast-safe charge protocols. A multiobjective optimal control problem is mathematically formulated via a coupled electro-thermal-aging battery model, where electrical and aging submodels depend upon the core temperature captured by a two-state thermal submodel. The Legendre–Gauss–Radau pseudospectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting highly nonlinear six-state optimal control problem. Charge time and health degradation are, therefore, optimally traded off, subject to both electrical and thermal constraints. Minimum-time, minimum-aging, and balanced charge scenarios are examined in detail. Sensitivities to the upper voltage bound, ambient temperature, and cooling convection resistance are investigated as well. Experimental results are provided to compare the tradeoffs between a balanced and traditional charge protocol.

Lithium-Ion Battery Parameters and State-of-Charge Joint Estimation Based on H-Infinity and Unscented Kalman Filters

Abstract: Accurate estimation of state-of-charge (SoC) is vital to safe operation and efficient management of lithium-ion batteries Currently, the existing SoC estimation methods can accurately estimate the SoC in a certain operation condition, but in uncertain operating environments, such as unforeseen road conditions and aging related effects, they may be unreliable or even divergent This is due to the fact that the characteristics of lithium-ion batteries vary under different operation conditions and the adoption of constant parameters in battery model, which are identified offline, will affect the SoC estimation accuracy In this paper, the joint SoC estimation method is proposed, where battery model parameters are estimated online using the H-infinity filter, while the SoC are estimated using the unscented Kalman filter Then, the proposed method is compared with the SoC estimation methods with constant battery model parameters under different dynamic load profiles and operation temperatures It shows that the proposed joint SoC estimation method possesses high accuracy, fast convergence, excellent robustness and adaptability

Coordinated Control Strategy of a Battery Energy Storage System to Support a Wind Power Plant Providing Multi-Timescale Frequency Ancillary Services

Abstract: With increasing penetrations of wind generation on electric grids, wind power plants (WPPs) are encouraged to provide frequency ancillary services (FAS); however, it is a challenge to ensure that variable wind generation can reliably provide these ancillary services. This paper proposes using a battery energy storage system (BESS) to ensure the WPPs’ commitment to FAS. This method also focuses on reducing the BESS's size and extending its lifetime. In this paper, a state-machine-based coordinated control strategy is developed to utilize a BESS to support the obliged FAS of a WPP (including both primary and secondary frequency control). This method takes into account the operational constraints of the WPP (e.g., real-time reserve) and the BESS (e.g., state of charge [SOC], charge and discharge rate) to provide reliable FAS. Meanwhile, an adaptive SOC-feedback control is designed to maintain SOC at the optimal value as much as possible, and, thus, reduce the size and extend the lifetime of the BESS. The effectiveness of the control strategy is validated with an innovative multi-area interconnected power system simulation platform that can mimic realistic power systems operation and control by simulating real-time economic dispatch, regulating reserve scheduling, multi-area automatic generation control, and generators’ dynamic response.

Lithium-Ion Battery Pack State of Charge and State of Energy Estimation Algorithms Using a Hardware-in-the-Loop Validation

Abstract: An adaptive H infinity filter approach is proposed to estimate the multistates including state of charge (SOC) and state of energy (SOE) for a lithium-ion battery pack. In the proposed approach, the covariance matching technique is used to adaptively update the covariance of system and observation noises and the recursive least square method is used to identify the battery model parameters in real time. The hardware-in-the-loop (HIL) platform for battery charge/discharge is set up to evaluate the accuracy and robustness of the SOC and the SOE estimation and compare the proposed approach with the multistate estimators using an extended Kalman filter and an H infinity filter. The experimental results indicate that the adaptive H infinity filter-based estimator is able to estimate the battery states in real time with the highest accuracy among the three filters.

Centralized Control Architecture for Coordination of Distributed Renewable Generation and Energy Storage in Islanded AC Microgrids

Abstract: The coordinated operation of distributed energy resources such as storage and generation units and also loads is required for the reliable operation of an islanded microgrid. Since in islanded microgrids the storage units are commonly responsible for regulating the voltage amplitude and frequency in the local power system, the coordination should consider safe operating limits for the stored energy, which prevents fast degradation or damage to the storage units. This paper proposes a centralized control architecture, applicable for local area power systems such as a small-scale microgrid. The centralized architecture is based on three supervisory control tasks which consider: active power curtailment of generation for avoiding overcharge of the storage units, load shedding actions for preventing deep discharge of the storage units, and equalization of the state of charge (SoC) among distributed storage systems for avoiding uneven degradation. The proposed equalization method has proved to be effective for equalizing the SoC of distributed energy storage systems and for ensuring uniform charge/discharge ratios regardless of differences in the capacity of the storage units. Additionally, the strategy is complemented with an optimal scheduling of load connection, which minimizes the connection and disconnection cycles of the loads within a time horizon of 24 h. The proposed architecture is verified experimentally in a lab-scale prototype of a microgrid, which has real communication between the microgrid and the central controller.

Active Balancing of Li-Ion Battery Cells Using Transformer as Energy Carrier

Abstract: A circuit for balancing Li-ion battery cells is proposed. This circuit requires one small transformer and N + 3 bilateral switches to equalize the charging states of N serially connected battery cells. The transformer works as an energy carrier, and the switches select two unbalanced cells that require an energy transfer from one to the other cell. The circuit was tested for a 12-cell Li-ion battery under static, cyclic, and dynamic charging/discharging conditions. Under static condition, the power-transfer efficiency was measured as 80.4% at a balancing power of 0.78 W; two 4400-mA·h battery cells at a state of charge $(\text{SOC}) = 70$ and 80% were equalized after 78 min. The results of cyclic and dynamic charging/discharging conditions show that the circuit is appropriate for balancing the Li-ion battery cells for vehicles and energy storage systems.

Power Flow Management of a Grid Tied PV-Battery System for Electric Vehicles Charging

Abstract: The prospective spread of electric vehicles (EV) and plug-in hybrid EV raises the need for fast charging rates. High required charging rates lead to high power demands, which may not be supported by the grid. In this paper, an optimal power flow technique of a PV-battery powered fast EV charging station is presented to continuously minimize the operation cost. The objective is to help the penetration of PV-battery systems into the grid and to support the growing need of fast EV charging. An optimization problem is formulated along with the required constraints and the operating cost function is chosen as a combination of electricity grid prices and the battery degradation cost. In the first stage of the proposed optimization procedure, an offline particle swarm optimization (PSO) is performed as a prediction layer. In the second stage, dynamic programming (DP) is performed as an online reactive management layer. Forecasted system data is utilized in both stages to find the optimal power management solution. In the reactive management layer, the outputs of the PSO are used to limit the available state trajectories used in the DP and, accordingly, improve the system computation time and efficiency. Online error compensation is implemented into the DP and fed back to the prediction layer for necessary prediction adjustments. Simulation and 1 kW prototype experimental results are successfully implemented to validate the system effectiveness and to demonstrate the benefits of using a hybrid grid tied system of PV-battery for fast EVs charging stations.

A Decentralized Power Management Strategy for Hybrid Energy Storage System With Autonomous Bus Voltage Restoration and State-of-Charge Recovery

Abstract: For hybrid energy storage system in dc microgrid, effective power split, bus voltage deviation, and state-of-charge (SoC) violation are significant issues. Conventionally, they are achieved by centralized control or hierarchical control methods with communications. This paper proposes a simple and effective strategy to solve the problem in a decentralized manner. A high-pass filter-based droop (HPFD) controller is proposed to regulate the battery converter, and a virtual capacitance droop (VCD) controller is implemented for a supercapacitor (SC) converter. The cooperation of HPFD and VCD first achieves autonomous power split that high-frequency fluctuation is buffered by SC and low-frequency power is supplied by battery. Meanwhile, the bus voltage deviation induced by the droop-based power sharing is eliminated automatically at steady state. The resulted bus voltage restoration simultaneously enforces SC SoC back to its nominal value, and, thus, ensures continuous operation of SC as a power buffer without the violation of its SoC boundary. A design guideline is developed to ensure expected system dynamics. The effectiveness of the proposed method and analytical results are validated by simulations and experiments.

Optimal Management for Parking-Lot Electric Vehicle Charging by Two-Stage Approximate Dynamic Programming

Abstract: This paper targets the day-time charging scenario for plug-in electric vehicles at parking-lots near commercial places, where most vehicles have extended parking time. Compared with night-time charge scenarios for residential buildings, commercial building parking-lot charging during day-time feature significant stochastic vehicle arrival and departure, as well as highly dynamic electricity price. A two-stage approximate dynamic programming framework is proposed to determine the optimal charging strategy, utilizing the predicted short-term future information and long-term estimation from historical data. All the vehicles are desired to be charged to full prior to the departure time specified under constrained total charging capacity. The uncharged amount is subject to a significant penalty cost. Simulation scenarios are created by modeling the vehicle arrival behavior as Poisson process, including arrival time, departure time, and arrival state of charge. The simulation results show that the proposed method can significantly decrease the energy cost.

A Scalable Active Battery Management System With Embedded Real-Time Electrochemical Impedance Spectroscopy

Abstract: Electrochemical energy storage is critical for a range of applications spanning electrified transportation and grid energy storage, and there is a need to further improve both the active management and diagnostic capability of current battery management systems. Lithium-based battery chemistries have been favored for their high energy and power densities but require precise management to prevent premature degradation and failure. This work presents an efficient power converter (based on a switched-inductor ladder topology), instrumentation, and an embedded control platform that can provide both active balancing and real-time diagnostic capability through electrochemical impedance spectroscopy (EIS). A digital proportional-integral controller enforces sinusoidal reference signals from a direct digital synthesizer, enabling the power converter to perturb the cells and extract their impedance. Cell-level diagnostics allow for noninvasive measurement of physical electrochemical battery properties that can be used to assess the state of charge and state of health of a battery. A ladder converter prototype was implemented on a printed circuit board to perform EIS on two Panasonic 18650 cells in series. Experimental results showed balancing converter efficiency of 95%, and the accuracy of the prototype was validated through comparison to a state-of-the-art commercial benchtop system.

Cycling degradation testing and analysis of a LiFePO4 battery at actual conditions

Abstract: Summary This paper presents a degradation testing of a lithium-ion battery developed using real world drive cycles obtained from an electric vehicle (EV). For this, a data logger was installed in the EV, and real world drive cycle data were collected. The EV battery system consists of 3 lithium-ion battery packs with a total of 20 battery modules in series. Each module contains 6 series by 49 parallel lithium-ion cells. The vehicle was driven in the province of Ontario, Canada, and several drive cycles were recorded over a 3-month period. However, only 4 drive cycles with statistical analysis are reported in this paper. The reported drive cycles consist of different modes: acceleration, constant speed, and deceleration in both highway and city driving at −6°C, 2°C, 10°C, and 23°C ambient temperatures with all accessories on. Additionally, individual cell characterization was conducted using a C/25 (0.8A) charge-discharge cycle and hybrid pulse power characterization (HPPC). The Thevenin battery model was constructed in MATLAB along with an empirical degradation model and validated in terms of voltage and SOC for all drive cycles reported. The presented model closely estimated the profiles observed in the experimental data. Data collected from the drive cycles showed that a 4.6% capacity fade occurred over the 3 months of driving. The empirical degradation model was fitted to these data, and an extrapolation estimated that 20% capacity fade would occur after 900 daily drive cycles.

State of Charge Estimation of Battery Energy Storage Systems Based on Adaptive Unscented Kalman Filter With a Noise Statistics Estimator

Abstract: Since the noise statistics of large-scale battery energy storage systems (BESSs) are often unknown or inaccurate in actual applications, the estimation precision of state of charge (SOC) of BESSs using extended Kalman filter (EKF) or unscented Kalman filter (UKF) is usually inaccurate or even divergent. To resolve this problem, a method based on adaptive UKF (AUKF) with a noise statistics estimator is proposed to estimate accurately SOC of BESSs. The noise statistics estimator based on the modified Sage-Husa maximum posterior is aimed to estimate adaptively the mean and error covariance of measurement and system process noises online for the AUKF when the prior noise statistics are unknown or inaccurate. The accuracy and adaptation of the proposed method is validated by the comparison with the UKF and EKF under different real-time conditions. The comparison shows that the proposed method can achieve better SOC estimation accuracy when the noise statistics of BESSs are unknown or inaccurate.

Multiagent-Based Distributed State of Charge Balancing Control for Distributed Energy Storage Units in AC Microgrids

Abstract: In this paper, a multiagent-based distributed control algorithm has been proposed to achieve state of charge (SoC) balance of distributed energy storage (DES) units in an ac microgrid. The proposal uses frequency scheduling instead of adaptive droop gain to regulate the active power. Each DES unit is taken as an agent and it schedules its own frequency reference given of the real power droop controller according to the SoC values of all other DES units. Further, to obtain the average SoC value of DES, the dynamic average consensus algorithm is utilized by each agent. A generalized small-signal model of the proposed frequency scheduling for the proposed frequency scheduling is developed in order to verify the stability of the control system and to guide control parameters design. The convergence characteristics for the dynamic consensus adopted in the multiagent system are also analyzed to choose the proper control parameter. Experimental results verified the effectiveness, the robustness against communication topology changes, and capability of “plug & play” for the proposed multiagent system through different case studies.

State-of-Charge Estimation for Lithium-Ion Batteries Based on a Nonlinear Fractional Model

Abstract: This paper presents a new battery state-of-charge (SOC) estimation method for lithium-ion batteries (LIBs) based on a nonlinear fractional model with incommensurate differentiation orders. A continuous frequency distributed model is used to describe the incommensurate fractional system. A Luenberger-type observer is designed for battery SOCestimation. The observer gain that can stabilize the zero equilibrium of the estimation error system is derived by Lyapunov’s direct method. The proposed SOC observer is examined using the real-time experimental data of LIBs. The robustness of the observer under different test conditions, including different aging levels, different driving cycles, and different cells, is also presented.

An Energy Management Strategy for a Concept Battery/Ultracapacitor Electric Vehicle With Improved Battery Life

Abstract: Using multi-input converters (MICs) in hybrid energy storage systems (HESSs) presents several advantages, such as low component count, control simplicity, and fully control of source energies. The power levels of sources in these systems need to be determined wisely by an energy management strategy (EMS). This paper presents an EMS for a battery/ultracapacitor (UC) HESS including a bidirectional MIC for electric vehicles (EVs). Thanks to the fact that energy flow between battery and UC is free in this MIC, the proposed EMS not only regulates the state-of-charge of UC but also smooths the battery power profile by using a fuzzy logic controller and a rate limiter. Therefore, it results in a sustainable HESS with longer battery life. Through a simulation study and an experimental setup including a real EV, the performance of the proposed system is evaluated comprehensively. Then, based on experimental results, battery cycle-life improvement due to the battery/UC hybridization is explored.