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A computationally efficient state estimation method for lithium-ion (Li-ion) batteries is proposed based on a degradation-conscious high-fidelity electrochemical-thermal model for advanced battery management systems. The computational burden caused by the high-dimensional nonlinear nature of the battery model is effectively eased by adopting an ensemble-based state estimator using the singular evolutive interpolated Kalman filter (SEIKF). Unlike the existing schemes, it shows that the proposed algorithm intrinsically ensures mass conservation without imposing additional constraints, leading to a battery state estimator simple to tune and fast to converge. The model uncertainty caused by battery degradation and the measurement errors are properly addressed by the proposed scheme as it adaptively adjusts the error covariance matrices of the SEIKF. The performance of the proposed adaptive ensemble-based Li-ion battery state estimator is examined by comparing it with some well-established nonlinear estimation techniques that have been used previously for battery electrochemical state estimation, and the results show that excellent performance can be provided in terms of accuracy, computational speed, as well as robustness.

Existing faulted feeder identification methods in the resonant grounded distribution network are primarily based on feature extraction of the fault-generated transient currents. The reliability of these approaches is significantly compromised by the fluctuating transient signals and interfering on-off operation of the neighboring switches. To sidestep the problems, a novel method is proposed to identify the faulted feeder by consecutively tuning the arc suppression coil around the full compensation state. Once a series of steady states are reached after tuning, the trajectories of the corresponding zero-sequence currents for both the sound and the faulted feeders are obtained to formulate an adjustment trajectory matrix (ATM). With the ATM, the similarity measure of the adjustment trajectories of all feeders is then employed to identify the faulted feeder based on the selected Deng's grey relational analysis. Results show that the adjustment trajectories of the two sound lines share a high similarity degree, while the similarity between the sound and the faulted lines is much lower. The effectiveness of the proposed method is validated via simulation and some case studies are provided. The results show that the faulted feeder can be correctly identified with high reliability and robustness compared to the existing fault-generated signal-based techniques.

The installation of current sensors on lithium-ion batteries (LIBs) can be challenging due to practical constraints in specific applications like portable electronics and smart batteries. Motivated by this, our letter proposes a method for online load current and state-of-charge (SOC) coestimation, which mitigates the need of installing the current sensor for LIB management. The essence is to transform the state observation into a constrained optimization problem, which is solved numerically in a moving horizon framework to allow the online coestimation of SOC and input current. Experimental results suggest that the proposed method can coestimate the load current and SOC of LIB precisely even if the current sensor is absent. The encouraging results are insightful for reducing the structural complexity and cost of future LIB utilization.

Electric vehicles (EVs) have excellent demand response potential, but a single type of demand response is difficult to fully utilise the dispatching potential of EVs. To improve the utilisation of the demand response potential of EVs, this study proposes two kinds of dispatching strategies for electric vehicle aggregator (EVA) combined price‐based and incentive‐based demand response: long‐term contract strategy and short‐term contract strategy. The difference of the two strategies depends on the length of time for EV users to sign incentive agreements with EVA. The long‐term contract strategy assumes that some EV users sign a long‐term incentive agreement with the EVA. The short‐term contract strategy takes an EV charging process as the dispatching cycle. The objective of maximising revenues and minimising load fluctuation of EVA is optimised in two strategies. An improved multi‐objective particle swarm optimisation algorithm that combines region selection strategy and grouping strategy is used to solve the problem. The simulation results show that the two dispatching strategies can improve the comprehensive benefits of EVA as well as EV users. The two dispatching strategies can effectively improve the utilisation of dispatching potential of EVs.

This article proposes a novel model-based estimator for distributed electrochemical states of lithium-ion (Li-ion) batteries. Through systematic simplifications of a high-order electrochemical–thermal coupled model consisting of partial differential-algebraic equations, a reduced-order battery model is obtained, which features an equivalent circuit form and captures local state dynamics of interest inside the battery. Based on the physics-based equivalent circuit model, a constrained ensemble Kalman filter (EnKF) is pertinently designed to detect internal variables, such as the local concentrations, overpotential, and molar flux. To address slow convergence issues due to weak observability of the battery model, the Li-ion's mass conservation is judiciously considered as a constraint in the estimation algorithm. The estimation performance is comprehensively examined under a wide operating range. It demonstrates that the proposed EnKF-based nonlinear estimator is able to accurately reproduce the physically meaningful state variables at a low computational cost and is significantly superior to its prevalent benchmarks for online applications.

The potential of variable-speed dish-Stirling (VSDS) solar-thermal generating plant in providing grid frequency support is investigated. In the proposed VSDS frequency support control scheme, the reference speed of the Stirling engine is regulated to track a deloaded power curve which is governed by the solar insolation level. The gain of a supplementary speed-frequency droop controller is then set to meet the primary frequency control requirement. Further uniqueness of the VSDS control scheme pertains to the provision of virtual inertia response by regulating the kinetic energy in the rotating mass of the engine-generator and the thermal energy in the heat absorber/receivers. Small-signal analysis shows that the frequency support scheme is inherently stable, and it will provide higher degree of damping as the penetration level of the VSDS system and/or the solar insolation level increase. The efficacy of the proposed scheme is validated by computer simulation.

Considered as one of the ultimate energy storage technologies for electrified transportation, the emerging all-solid-state batteries (ASSBs) have attracted immense attention due to their superior thermal stability, increased power and energy densities, and prolonged cycle life. To achieve the expected high performance, practical applications of ASSBs require accurate and computationally efficient models for the design and implementation of many onboard management algorithms, so that the ASSB safety, health, and cycling performance can be optimized under a wide range of operating conditions. A control-oriented modeling framework is thus established in this work by systematically simplifying a rigorous partial differential equation (PDE) based model of the ASSBs developed from underlying electrochemical principles. Specifically, partial fraction expansion and moment matching are used to obtain ordinary differential equation based reduced-order models (ROMs). By expressing the models in a canonical circuit form, excellent properties for control design such as structural simplicity and full observability are revealed. Compared to the original PDE model, the developed ROMs have demonstrated high fidelity at significantly improved computational efficiency. Extensive comparisons have also been conducted to verify its superiority to the prevailing models due to the consideration of concentration-dependent diffusion and migration. Such ROMs can thus be used for advanced control design in future intelligent management systems of ASSBs.

The development of the smart grid promotes the rapid development of distributed generation (DG), while the intermittent nature of DG and the growing demand for electricity make it difficult for the grid to maintain the reliability and stability of the grid. Moreover, the time interval between peak demand of electricity usually differs from the peak supply provided by DG, which creates a mismatch between supply and demand. An energy storage system (ESS) can be an effective solution to improve the self-consumption of electricity generated by DG. In this paper, an optimization strategy of household energy management based on DG and ESS is proposed, which makes full use of the rechargeable characteristics of ESS and EV to improve the economy and flexibility of the system in the case of uncertain output prediction of DG. The objective is to minimize the cost of electricity consumption and user comfort. The actual situation of the users is taken into account, as well as the integration of renewable energy, energy storage, demand response, and other new technologies in the smart grid. Finally, the effectiveness of the proposed model and algorithm is verified by multi-scenario analysis.

An efficient model-based state estimation method for lithium-ion (Li-ion) batteries is proposed using an extended electrochemical single particle model enhanced by electrolyte and thermal dynamics. To address the spatially distributed characteristics across the particle radius and the electrode width, an adaptive ensemble adjustment Kalman filter (AEAKF) algorithm is established to effectively reduce the computational burden caused by the high-dimensional nonlinear model for online operation. The uncertainties in the modeling and the measurement error are considered in the proposed algorithm by adaptively adjusting the error covariance matrices in the filtering scheme. The performance of the AEAKF-based distributed battery state estimator is examined via simulation by comparing it with other nonlinear estimation techniques. It is shown that the proposed method exhibits excellent performance in terms of prediction accuracy and computational speed, while the capability to signify the degradation-related internal states can be utilized for advanced health-aware battery management systems designed for various applications such as electric vehicles and smart grids.

State of charge (SOC) is an important index to ensure the safe and stable operation of vanadium redox flow battery (VRB) and its accurate estimation can protect the battery from overcharge and over-discharge. Time-varying model parameters and nonlinear estimation algorithm are the main challenges for accurate estimation of SOC. This paper proposes the SOC estimation method for VRB based on online equivalent circuit model. Firstly, the recursive least squares with forgetting factor (FRLS) is used to online identify the model parameters. Then, based on this online model, the unscented Kalman filter (UKF) is employed to online estimate SOC. Finally, a hybrid pulse current experiment is conducted to verify the proposed the estimation method on a 5kW/3kWh VRB system in the laboratory. Validated with experimental data, the online identified model parameters and the estimated SOC present excellent accuracy, the root mean square error (RMSE) of the model and SOC is 0.0165V and 0.01 respectively. The proposed SOC estimation method is capable of estimating SOC accurately.

Responding to rapid growth of the renewable energy applications, it is crucial to develop low cost and high efficient large-scale energy storage systems in order to smooth out the intermittency of the renewable energy resources. As one of the most promising large-scale energy storage systems, vanadium redox flow battery (VRFB) has attracted great attention in recent times. Membrane is one of the key components of VRFB which not only affects the whole cyclability performance but also determines the economic viability of the system. The membrane separates the positive and negative half-cells and prevents the cross-mixing of vanadium ions while providing required ionic conductivity. The ideal membrane should have good ionic exchange capacity; high ionic conductivity, low water uptake, swelling ratio, area electrical resistance and vanadium and other poly-halide ions permeability; and good chemical stability, as well as low cost. Numerous efforts have been spent on the development of different types of membranes, including different functional groups ion exchange membrane and non-ionic porous membrane. This paper reviews the research on membranes in VRFB system, including the properties, development of traditional commercial membranes as well as recently developed membranes. It explores various methods of fabrication of the membrane products which have received relatively little attention. A detailed summary table of the new membranes with their properties, fabrication and costs is provided to serve as a reference guide for researchers and industrialists interested in VRFB system building and dynamic modelling set. Subsequently, the challenges and future directions of membrane research are examined. Accepted version This work is supported by Natural Science Foundation of China (#61703318), Natural Science Foundation of Hubei Province (#2017CFB130) and Open Fund of Operation and Control of Renewable Energy & Storage System of National Key Laboratory, China (#DGB51201801584).

Nowadays, a growing number of plug-in electric vehicles are charged by electric vehicle chargers (EVCs) in low-voltage (LV) residential networks. Without proper control, the increasing charge demand together with high PV penetration will lead to severe voltage issues. In this paper, a two-level coordinated voltage control scheme of EVCs is proposed for voltage regulation in LV distribution networks. The proposed control scheme includes two coordinated levels to effectively utilize EVCs. In the higher level, the distributed voltage control based on consensus algorithm achieves equal utilization among EVCs for network voltage regulation. In the lower level, the localized power allocator determines the real and reactive power outputs, considering the power limits and charging requirements of each EVC. The entire control scheme ensures that the voltages along the distribution network are within the allowable voltage ranges while avoid affecting the users’ charging demand. The proposed control method is evaluated in a LV residential distribution network under various operation conditions. The simulation results validate the effectiveness of the proposed method. Accepted version

Thermal issue is one of the major concerns for safe, reliable, and efficient operation of the vanadium redox flow battery (VRB) energy storage systems. During the design of the operational strategy for a grid-connected VRB system, a suitable mathematical model is needed to predict the dynamic behaviors under various operating conditions. However, conventional VRB models usually neglect the impact of temperature variations on system performance. This work develops an enhanced VRB model with the consideration of the coupling effects between the electrochemical and the thermal behaviors. The proposed model consists of two equivalent circuits. First, the electrochemical behaviors of the VRB are modeled by a second-order RC network taking account of the effects of concentration variation of the vanadium ions and the electrochemical activation. Second, a third-order Cauer network is used to model the heat transfer process in the VRB system, and the dynamic thermal behaviors of stacks, pipes and heat exchangers are characterized. Well-designed experiments and particle swarm optimization algorithm are use to identify the parametric values of the developed model. The proposed modeling method was validated experimentally using a 5kW/3kWh VRB platform, and the results show that the model is capable of accurately predicting the VRB performance under variable temperature conditions. The developed coupled electro-thermal model is then used for simulating and analyzing the performance of a VRB system operated in conjunction with a wind power plant under real-world conditions.

Precise state of health (SOH) estimation for lithium-ion batteries (LIBs) can provide guidance for the rational use, reduce the failure rate of batteries, and extend the battery life. Due to the complex aging mechanism, and uncertainty of aging paths of LIB system, the model-based method needs to establish a complex mechanism model, which requires a large amount of calculation. In this paper, the Gaussian process regression (GPR) method is introduced to analyze battery experimental data and estimate battery SOH. GPR is a data-driven method with the ability to describe uncertainty. Firstly, the aging experiment of LIB was carried out. In the experiment, three LIBs of the same type were operated under three different charging and discharging current rates until the end of life. Secondly, based on the charging curve of LIB, four features reflecting battery aging were extracted. In addition, the grey relational analysis method was introduced to analyze the correlation between the extracted features and SOH. Thirdly, a SOH estimation framework based on improved GPR is proposed, and the GPR model is improved by using an explicit mean function and a combined covariance function. Finally, based on the experimental data, the proposed framework is used to estimate the SOH, and the results are analyzed.