36 research outputs found
Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview
In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the battery packâs SOC. Notably, the advent of advanced microcontrollers and the availability of extensive datasets have contributed to the growing popularity and practicality of data-driven methodologies. This study examines the developments in SOC estimation over the past half-decade, explicitly focusing on data-driven estimation techniques. It comprehensively assesses the performance of each algorithm, considering the type of battery and various operational conditions. Additionally, intricate details concerning the modelsâ hyperparameters, including the number of layers, type of optimiser, and neuron, are provided for thorough examination. Most of the models analysed in the paper demonstrate strong performance, with both the MAE and RMSE for the estimation of SOC hovering around 2% or even lower
Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques.
State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions
Generating realistic data for developing artificial neural network based SOC estimators for electric vehicles
Tracking the state of a lithium-ion battery in an electric vehicle (EV) is a challenging task. In order to tackle one aspect of this task, we choose a data-driven approach for estimating the State of Charge (SOC), which is one of the most import parameters. In this context, the quality of the provided data is of utmost importance. Usually, standardized driving profiles are used to generate current profiles which are then applied to battery cells during testing. However, these standardized driving profiles exhibit significant deviation from real-world conditions, which can considerably affect the learning and validation performance of data-driven approaches. In this paper, we first propose a test profile generator which generates realistic current profiles for EV battery testing. Second, to demonstrate the effect of the proposed test profiles a multilayer perceptron (MLP) based SOC estimator is presented. Finally, we compare the results to the standardized driving profiles
A novel adaptive state of charge estimation method of full life cycling lithium-ion batteries based on the multiple parameter optimization.
The state of charge (SoC) estimation is the safety management basis of the packing lithium-ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state-space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is less than 0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs
Impact of Distributed Battery Energy Storage on Electric Power Transmission and Distribution Systems
The penetration of Renewable Energy Sources (RES) in electricity grids has increased worldwide over the past decade because of their decreasing costs, especially of Photovoltaic (PV) and wind generation resources with government support for their deployment to counteract global warming effects. Indeed, nowadays, not only utility-scale, but small-scale RES connected at the distribution level are being installed by residential and industrial customers to improve their energy supply and costs. In this context, Energy Storage Systems (ESSs) can be used to facilitate the integration of RES into the grid; Battery Energy Storage Systems (BESSs) being a relatively matured and suitable storage technology for such applications. In particular, distribution systems in some jurisdictions are experiencing an increasing number of new installations of Distributed Energy Resources (DERs), including PV generation accompanied by BESSs, thus, transforming the traditionally passive utility grids into Active Distribution Networks (ADNs), whose operation has the potential to influence the transmission system upstream. Some issues associated with large quantities of DERs connected to ADNs are reduction of transmission level flexibility to accommodate changes at the distribution system, larger frequency deviations due to reduction of system inertia, and various other grid stability issues associated with DER converter interfaces. BESSs can help address some of these problems by providing grid services such as voltage control, oscillation damping, frequency regulation, and active and reactive power control. As a result, appropriate assessment of the integration of distributed DERs on transmission grids, particularly BESSs, is necessary.
In this thesis, the impacts of grid-scale and distributed BESSs connected at the distribution system level, on the transmission grid are studied, for which suitable models for steady-state and dynamic analyses are proposed. Thus, first, a dynamic average BESS model is proposed, which comprises a simplified representation of the battery cells to allow simulating the effects of battery degradation, a bidirectional buck-boost converter (dc-to-dc), a Voltage Source Converter (VSC), an ac filter, and associated controls. The decoupled dq-current control of the VSC enables independent control of the BESSsâ active and reactive power injections, thus allowing their operation in several modes studied and improved in this work, namely, constant active and reactive power, constant power factor, voltage regulation, frequency regulation, oscillation damping, and a combination of the last two. The BESS average model is included within a commercial-grade software for power system analysis, validated against a detailed model that considers the high-frequency switching in the converters, and tested for different contingencies when connected to a benchmark system to demonstrate the effectiveness of a grid-scale BESS to provide the services stated earlier.
In the second part of the thesis, in order to investigate the effects of distributed BESSs connected to ADNs, on the transmission grid, for dynamic electrical studies, an aggregated black-box BESS model at the boundary bus of the transmission and ADN is proposed. ADN measurements of the aggregated response of the BESSs at the boundary bus with the transmission system are used to develop the aggregated black-box model, which is based on two Neural Networks (NNs), one for active power and the other for reactive power, with their optimal topology obtained using a Genetic Algorithm (GA). Detailed simulations are performed considering multiple BESSs connected to a CIGRE benchmark and located at a load bus of the 9-bus WSCC benchmark transmission network to generate training data for the NNs. Then, the test ADN is replaced by the proposed black-box model, with aggregated models of the loads and PV generation, demonstrating that the model can accurately reproduce the results obtained for trained and untrained events.
The main conclusions of this work are that the inclusion of the proposed controllers for the BESS can significantly improve the contribution of both grid-scale and distribute BESSs to the stability of transmission grids. In addition, the need of including the dc-to-dc converter in the BESS model for dynamic studies is demonstrated, especially when degraded batteries are considered, due to the limitations this operating condition creates on the dc-to-dc operation and its associated controller. Finally, the proposed methodology used to develop the black-box model to represent the aggregated response of BEESs is proved to be robust, since this model is shown to accurately reproduce the behavior the aggregated response of the battery systems providing various grid services, not only for the events and associated data used to train the proposed NN-based models, but also for contingencies for which the models were not trained
Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation
An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice