S. Nejad

Bio: S. Nejad is an academic researcher from University of Sheffield. The author has contributed to research in topics: Extended Kalman filter & State of charge. The author has an hindex of 8, co-authored 16 publications receiving 372 citations.

A Battery Energy Management Strategy for U.K. Enhanced Frequency Response and Triad Avoidance

Abstract: This paper describes a control algorithm for a battery energy storage system (BESS) to deliver a charge/discharge power output in response to changes in the grid frequency constrained by the National Grid Electricity Transmission (NGET)—the primary electricity transmission network operator in the U.K.—while managing the state of charge of the BESS to optimize the availability of the system. Furthermore, this paper investigates using the BESS in order to maximize triad avoidance benefit revenues while layering other services. Simulation using a 2 MW/1 MWh lithium–titanate BESS validated model is carried out to explore possible scenarios using the proposed algorithms. Finally, experimental results of the 2 MW/1 MWh Willenhall Energy Storage System verify the performance of the proposed algorithms.

Online Battery State of Power Prediction Using PRBS and Extended Kalman Filter

Abstract: This paper presents a hybrid battery parameterization technique for the purpose of battery state-of-charge (SOC) and state-of-power (SOP) monitoring in real time. The proposed technique is centered around an opportunistic initialization of a dual extended Kalman filter (DEKF) algorithm using pseudorandom binary sequence (PRBS) battery excitation. A second-order electrical equivalent-circuit battery model is used whose parameters are identified using a carefully designed 10-bit 10-Hz PRBS signal while the battery is in a zero- or low-current quiescent mode. The PRBS-identified resistive elements of the battery model are then utilized to provide an initial estimate for the battery's SOP. Once in load conditions, the DEKF algorithm is implemented recursively to provide an accurate estimate of the battery's parameters, SOC, and subsequently its SOP in real time. The experimental results obtained from an electrochemical impedance spectroscopy method give confidence to the performance of the proposed hybrid battery parameterization technique.

A battery energy management strategy for UK enhanced frequency response

Abstract: Balancing the grid at 50 Hz requires managing many distributed generation sources against a varying load, which is becoming an increasingly challenging task due to the increased penetration of renewable energy sources such as wind and solar and loss of traditional generation which provide inertia to the system. In the UK, various frequency support services are available, which are developed to provide a real-time response to changes in the grid frequency. The National Grid (NG) — the main distribution network operator in the UK — have introduced a new and fast service called the Enhanced Frequency Response (EFR), which requires a response time of under one second. A battery energy storage system (BESS) is a suitable candidate for delivering such service. Therefore, in this paper a control algorithm is developed to provide a charge/discharge power output with respect to deviations in the grid frequency and the ramp-rate limits imposed by the NG, whilst managing the state-of-charge (SOC) of the BESS for an optimised utilisation of the available stored energy. Simulation results on a 2 MW/1 MWh lithium-titanate BESS are provided to verify the proposed algorithm based on the control of an experimentally validated battery model.

On-chip implementation of Extended Kalman Filter for adaptive battery states monitoring

Abstract: This paper reports the development and implementation of an adaptive lithium-ion battery monitoring system. The monitoring algorithm is based on the nonlinear Dual Extended Kalman Filter (DEKF), which allows for simultaneous states and parameters estimation. The hardware platform consists of an ARM cortex-M0 processor with six embedded analogue-to-digital converters (ADCs) for data acquisition. Two definitions for online state-of-health (SOH) characterisation are presented; one energy-based and one power-based. Moreover, a method for online estimation of battery's capacity, which is used in SOH characterisation is proposed. Two definitions for state-of-power (SOP) are adopted. Despite the presence of large sensor noise and incorrect filter initialisation, the DEKF algorithm poses excellent SOC and SOP tracking capabilities during a dynamic discharge test. The SOH prediction results are also in good agreement with actual measurements.

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Abstract: Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

Abstract: With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.

A brief review on key technologies in the battery management system of electric vehicles

Abstract: Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries

Abstract: Carbon impression and the growing reliance on fossil fuels are two unique concerns for world emission regulatory agencies. These issues have placed electric vehicles (EVs) powered by lithium-ion batteries (LIBs) on the forefront as alternative vehicles. The LIB has noticeable features, including high energy and power density, compared with other accessible electrochemical energy storage systems. However, LIB is exceedingly nonlinear and dynamic; therefore, it generally requires an accurate online state-of-charge (SOC) estimation algorithm for real-time applications. Accurate battery modelling is an essential and primary requirement of online SOC estimation to simulate the dynamics. In this paper, different modelling methods suitable for online SOC estimation are discussed, and four groups of available online SOC estimation approaches are reviewed. After the general survey, the study explores the available Kalman filter (KF) family algorithms suitable for model-based online SOC estimation. The mathematical process and limitations of different KF family algorithms are analysed in depth and discussed. Moreover, challenging steps in the implementation of KF family algorithms in model-based online SOC estimation processes, such as selection of battery model, initial SOC and filter tuning, are elaborated for the efficient development of a battery management system, especially for EV application. The on-going research is propelled by KF-based online SOC estimation approaches distinctly emphasised through reviewing various studies for future research progression.