Modelling and state-of-charge estimation for ultracapacitors in electric vehicles

University of Technology Sydney. Faculty of Engineering and Information Technology.In order to cope with the global challenges like fossil fuel depletion and environmental pollution, electrified vehicles (EVs) have been widely accepted as an enabling option for future ground mobility. In comparison to conventional combustion engine vehicles, EVs have the advantage of high efficiency, environment-friendly operation and excellent control flexibility. There is a proviso here that the electricity used by the EV is from a green source such as hydro, wind or solar. The energy storage system (ESS) is a key ingredient of an EV, and significantly affects its driving performance and cost-effectiveness. The exploration of a vehicular ESS poses a formidable challenge, because of high power/energy demands and unpredictable driving environments. Li-ion batteries represent a main choice for this use, but suffer the drawbacks of low power density and poor recyclability. Recently, ultracapacitors (UCs), also referred to as supercapacitors (SCs) or electric double-layer capacitors (EDLCs), have gained increasing attention in the energy storage community, thanks to their high power density, high efficiency, fast charge, wide temperature window and excellent recyclability. These advantages make UCs a good augmentation to high-energy ESSs (e.g., fuel cells, lithium-ion batteries). This combination represents a hybrid energy storage system (HESS) that can fully leverage the synergistic benefits of each constituent device. To ensure efficient, reliable and safe operations of UC systems, numerous challenges including modelling and characterization, and State-of-Charge (SOC) estimation should be effectually surmounted. In order to meet the above mentioned challenges, the main research presented in this dissertation includes: 1. A special test rig for UC characteristic investigation has been established. A test procedure is proposed to collect comprehensive test data. A plethora of tests have been conducted on this test rig including capacity calibration, experimental impedance investigation under different temperatures and SOC values, and dynamic cycling including pulse tests and driving-cycle-based tests under different temperatures, resulting in a wide-ranging UC database. 2. The impedance characteristics of UCs are experimentally investigated under different temperatures and SOC values. The results show that the impedance is highly sensitive to temperature and SOC; and the temperature effect is more significant. In particular, the coupling effect between the temperature and SOC is illustrated, and the high-efficiency SOC window is highlighted. 3. For UC modelling, three commonly used equivalent circuit models are systematically examined in terms of model accuracy, complexity and robustness in the context of EV applications. A genetic algorithm (GA) is employed to extract the optimal model parameters based on the Hybrid Pulse Power Characterization (HPPC) test data. The performance of these models is then evaluated and compared by measuring the model complexity, accuracy, and robustness against “unseen” data collected in the Dynamic Stress Test (DST) and a self-designed pulse (SDP) test. The validation results show that the dynamic model has the best overall performance for EV applications. 4. Online parameter identification of UC models is researched. The extended Kalman Filter (EKF) is proposed to recursively estimate the model parameters using the DST dataset, in which the dynamic model is used to represent the UC dynamics. The effectiveness and robustness of the proposed method is validated using another driving-cycle-based dataset. 5. A novel robust H-infinity observer is presented to realize UC SOC estimation in real-time. In comparison to the state-of-the-art Kalman filter-based (KF-based) methods, the developed robust scheme can ensure high estimation accuracy without prior knowledge of process and measurement noise statistical properties. More significantly, the proposed H-infinity observer proves to be more robust to modelling uncertainties arising from the change of thermal conditions and/or cell health status. 6. A novel fractional-order model is put forward to emulate the UC dynamics. In contrast to integer-order models, the presented fractional-order model has the merits of better model accuracy and fewer parameters. It consists of a series resistor, a constant-phase-element (CPE), and a Warburg-like element. The model parameters are optimally extracted using GA, based on the time-domain Federal Urban Driving Schedule (FUDS) test data acquired through the established test rig. By means of this fractional-order model, a fractional Kalman filter is synthesized to recursively estimate the UC SOC. Validation results show that the proposed fractional-order modelling and state estimation scheme is accurate and outperforms the current practice based on integral-order techniques. 7. An optimal HESS sizing method using a multi-objective optimization algorithm is presented, in which the primary goal is reducing the ESS cost and weight while prolonging battery life. To this end, a battery state-of-health (SOH) model is incorporated to quantitatively investigate the impact of component sizing on battery life. The wavelet-transform-based power management algorithm is adopted to realize the power coordination between the battery and UC packs. The results provide prudent insights into HESS sizing with different emphases

Battery management system for Li-Ion batteries in hybrid electric vehicles

The Battery Management System (BMS) is the component responsible for the effcient and safe usage of a Hybrid Electric Vehicle (HEV) battery pack. Its main activity is to track the State of Charge (SoC) and State of Health (SoH) of the battery pack, as well as perform maintenance actions such as cell balancing and cooling to provide maximum lifetime for the pack. This work reviews the development of a rigorous electrochemical model for a lithium-ion battery suitable for real-time monitoring, and explores one of the most recent techniques proposed by the literature that enables an accurate SOC estimation by means of an Unscented Kalman Filter. The theoretical framework is accompanied by simulations supporting the validity of the chosen approac

A new state-of-charge estimation method for electric vehicle lithium-ion batteries based on multiple input parameter fitting model

Copyright © 2017 John Wiley & Sons, Ltd. The estimation of state-of-charge (SOC) is crucial to determine the remaining capacity of the Lithium-Ion battery, and thus plays an important role in many electric vehicle control and energy storage management problems. The accuracy of the estimated SOC depends mostly on the accuracy of the battery model, which is mainly affected by factors like temperature, State of Health (SOH), and chemical reactions. Also many characteristic parameters of the battery cell, such as the output voltage, the internal resistance and so on, have close relations with SOC. Battery models are often identified by a large amount of experiments under different SOCs and temperatures. To resolve this difficulty and also improve modeling accuracy, a multiple input parameter fitting model of the Lithium-Ion battery and the factors that would affect the accuracy of the battery model are derived from the Nernst equation in this paper. Statistics theory is applied to obtain a more accurate battery model while using less measurement data. The relevant parameters can be calculated by data fitting through measurement on factors like continuously changing temperatures. From the obtained battery model, Extended Kalman Filter algorithm is applied to estimate the SOC. Finally, simulation and experimental results are given to illustrate the advantage of the proposed SOC estimation method. It is found that the proposed SOC estimation method always satisfies the precision requirement in the relevant Standards under different environmental temperatures. Particularly, the SOC estimation accuracy can be improved by 14% under low temperatures below 0 °C compared with existing methods. Copyright © 2017 John Wiley & Sons, Ltd

Edge computing for vehicle battery management:Cloud-based online state estimation

The adoption of electric vehicles (EVs), including battery EVs and hybrid EVs, makes it possible to reduce fossil fuel consumption and greenhouse gas emission. However, an accurate battery model and an effective battery management system should be established to enable this benefit. This paper proposes a novel cloud-assisted online battery management method based on artificial intelligence and edge computing technologies. Integration of cloud computation and big data resources into real-time vehicle battery management is realized by establishing a novel cloud-edge battery management system (CEBMS). A deep learning algorithm-based cloud data mining and battery modeling method is developed to estimate the voltage and energy state of the battery. The accuracy of the established cloud battery model outperforms the onboard battery management system by utilizing multi-sources information from different EVs. Meanwhile, a cloud-assisted battery management method is established at edge nodes in the onboard battery management unit to realize real-time state estimation locally. By using precise battery state estimation provided by the cloud platform, vehicle battery model accuracy can be significantly improved. The performance of the proposed battery management method is verified by a vehicle big data platform and battery pack experimental test bench. Experimental results justify the effectiveness of the proposed method in battery state estimation, which can help the EVs use and manage the battery more effectively.</p

Dc Line-Interactive Uninterruptible Power Supply (UPS) with Load Leveling for Constant Power and Pulse Loads

Uninterruptable Power Supply (UPS) systems are usually considered as a backup power for electrical systems, providing emergency power when the main power source fails. UPS systems ensure an uninterruptible, reliable and high quality electrical power for systems with critical loads in which a continuous and reliable power supply is a vital requirement. A novel UPS system topology, DC line-interactive UPS, has been introduced. The new proposed UPS system is based on the DC concept where the power flow in the system has DC characteristic. The new DC UPS system has several advantageous with respect to the on-line 3-phase UPS which is extensively used in industry, such as lower size, cost and weight due to replacing the three-phase dual converter in the on-line UPS system with a single stage single phase DC/DC converter and thus higher efficiency is expected. The proposed system will also provide load leveling feature for the main AC/DC rectifier which has not been offered by conventional AC UPS systems. It applies load power smoothing to reduce the rating of the incoming AC line and consequently reduce the installation cost and time. Moreover, the new UPS technology improves the medical imaging system up-time, reliability, efficiency, and cost, and is applicable to several imaging modalities such as CT, MR and X-ray as well. A comprehensive investigation on different energy storage systems was conducted and couple of most promising Li-ion cell chemistries, LFP and NCA types, were chosen for further aggressive tests. A battery pack based on the LFP cells with monitoring system was developed to be used with the DC UPS testbed. The performance of the DC UPS has also been investigated. The mathematical models of the system are extracted while loaded with constant power load (CPL) and constant voltage load (CVL) during all four modes of operation. Transfer functions of required outputs versus inputs were extracted and their related stability region based on the Routh-Hurwitz stability criteria were found. The AC/DC rectifier was controlled independently due to the system configuration. Two different control techniques were proposed to control the DC/DC converter. A linear dual-loop control (DLC) scheme and a nonlinear robust control, a constant frequency sliding mode control (CFSMC) were investigated. The DLC performance was convincing, however the controller has a limited stability region due to the linearization process and negative incremental impedance characteristics of the CPL which challenges the stability of the system. A constant switching frequency SMC was also developed based on the DC UPS system and the performance of the system were presented during different operational modes. Transients during mode transfers were simulated and results were depicted. The controller performances met the control goals of the system. The voltage drop during mode transitions, was less than 2% of the rated output voltage. Finally, the experimental results were presented. The high current discharge tests on each selected Li-ion cell were performed and results presented. A testbed was developed to verify the DC UPS system concept. The test results were presented and verified the proposed concept

State of charge estimation of ultracapacitor based on equivalent circuit model using adaptive neuro-fuzzy inference system

Ultracapacitors have been attracting interest to apply as energy storage devices with advantages of fast charging capability, high power density, and long lifecycle. As a storage device, accurate monitoring is required to ensure and operate safely during the charge/discharge process. Therefore, high accuracy estimation of the state of charge (SOC) is needed to keep the Ultracapacitor working properly. This paper proposed SOC estimation using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS is tested by comparing it to true SOC based on an equivalent circuit model. To find the best method, the ANFIS is modified and tested with various membership functions of triangular, trapezoidal, and gaussian. The results show that triangular membership is the best method due to its high accuracy. An experimental test is also conducted to verify simulation results. As an overall result, the triangular membership shows the best estimation. Simulation results show SOC estimation mean absolute percentage error (MAPE) is 0.70 % for charging and 0.83 % for discharging. Furthermore, experimental results show that MAPE of SOC estimation is 0.76 % for random current. The results of simulations and experimental tests show that ANFIS with a triangular membership function has the most reliable ability with a minimum error value in estimating the state of charge on the Ultracapacitor even under conditions of indeterminate random current

IEEE Access special section editorial: battery energy storage and management systems

Battery energy storage and management systems constitute an enabling technology for more sustainable transportation and power grid systems. On the one hand, emerging materials and chemistries of batteries are being actively synthesized to continually improve their energy density, power density, cycle life, charging rate, etc. On the other hand, advanced battery management systems (BMSs) are being intensively developed to guarantee the safety, reliability, efficiency, and cost-effectiveness of batteries in realistic operations, as well as their integration with mechatronics. Owing to their multi-physics nature, designing high-performance batteries and their management systems requires multidisciplinary approaches, with an ever-increasing synergy of electrochemi- cal, material, mechatronics, computer, and control disciplines

E-transportation: the role of embedded systems in electric energy transfer from grid to vehicle

Electric vehicles (EVs) are a promising solution to reduce the transportation dependency on oil, as well as the environmental concerns. Realization of E-transportation relies on providing electrical energy to the EVs in an effective way. Energy storage system (ESS) technologies, including batteries and ultra-capacitors, have been significantly improved in terms of stored energy and power. Beside technology advancements, a battery management system is necessary to enhance safety, reliability and efficiency of the battery. Moreover, charging infrastructure is crucial to transfer electrical energy from the grid to the EV in an effective and reliable way. Every aspect of E-transportation is permeated by the presence of an intelligent hardware platform, which is embedded in the vehicle components, provided with the proper interfaces to address the communication, control and sensing needs. This embedded system controls the power electronics devices, negotiates with the partners in multi-agent scenarios, and performs fundamental tasks such as power flow control and battery management. The aim of this paper is to give an overview of the open challenges in E-transportation and to show the fundamental role played by embedded systems. The conclusion is that transportation electrification cannot fully be realized without the inclusion of the recent advancements in embedded systems