Accuracy versus simplicity in online battery model identification

This paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, “identification surfaces” are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery’s ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack

Methods of Technical Prognostics Applicable to Embedded Systems

Hlavní cílem dizertace je poskytnutí uceleného pohledu na problematiku technické prognostiky, která nachází uplatnění v tzv. prediktivní údržbě založené na trvalém monitorování zařízení a odhadu úrovně degradace systému či jeho zbývající životnosti a to zejména v oblasti komplexních zařízení a strojů. V současnosti je technická diagnostika poměrně dobře zmapovaná a reálně nasazená na rozdíl od technické prognostiky, která je stále rozvíjejícím se oborem, který ovšem postrádá větší množství reálných aplikaci a navíc ne všechny metody jsou dostatečně přesné a aplikovatelné pro embedded systémy. Dizertační práce přináší přehled základních metod použitelných pro účely predikce zbývající užitné životnosti, jsou zde popsány metriky pomocí, kterých je možné jednotlivé přístupy porovnávat ať už z pohledu přesnosti, ale také i z pohledu výpočetní náročnosti. Jedno z dizertačních jader tvoří doporučení a postup pro výběr vhodné prognostické metody s ohledem na prognostická kritéria. Dalším dizertačním jádrem je představení tzv. částicového filtrovaní (particle filtering) vhodné pro model-based prognostiku s ověřením jejich implementace a porovnáním. Hlavní dizertační jádro reprezentuje případovou studii pro velmi aktuální téma prognostiky Li-Ion baterii s ohledem na trvalé monitorování. Případová studie demonstruje proces prognostiky založené na modelu a srovnává možné přístupy jednak pro odhad doby před vybitím baterie, ale také sleduje možné vlivy na degradaci baterie. Součástí práce je základní ověření modelu Li-Ion baterie a návrh prognostického procesu.The main aim of the thesis is to provide a comprehensive overview of technical prognosis, which is applied in the condition based maintenance, based on continuous device monitoring and remaining useful life estimation, especially in the field of complex equipment and machinery. Nowadays technical prognosis is still evolving discipline with limited number of real applications and is not so well developed as technical diagnostics, which is fairly well mapped and deployed in real systems. Thesis provides an overview of basic methods applicable for prediction of remaining useful life, metrics, which can help to compare the different approaches both in terms of accuracy and in terms of computational/deployment cost. One of the research cores consists of recommendations and guide for selecting the appropriate forecasting method with regard to the prognostic criteria. Second thesis research core provides description and applicability of particle filtering framework suitable for model-based forecasting. Verification of their implementation and comparison is provided. The main research topic of the thesis provides a case study for a very actual Li-Ion battery health monitoring and prognostics with respect to continuous monitoring. The case study demonstrates the prognostic process based on the model and compares the possible approaches for estimating both the runtime and capacity fade. Proposed methodology is verified on real measured data.

Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter

This paper investigates the state estimation of a high-fidelity spatially resolved thermal- electrochemical lithium-ion battery model commonly referred to as the pseudo two-dimensional model. The partial-differential algebraic equations (PDAEs) constituting the model are spatially discretised using Chebyshev orthogonal collocation enabling fast and accurate simulations up to high C-rates. This implementation of the pseudo-2D model is then used in combination with an extended Kalman filter algorithm for differential-algebraic equations to estimate the states of the model. The state estimation algorithm is able to rapidly recover the model states from current, voltage and temperature measurements. Results show that the error on the state estimate falls below 1 % in less than 200 s despite a 30 % error on battery initial state-of-charge and additive measurement noise with 10 mV and 0.5 K standard deviations.Comment: Submitted to the Journal of Power Source

Wireless Power Charging Control in Multiuser Broadband Networks

Recent advances in wireless power transfer (WPT) technology provide a cost-effective solution to charge wireless devices remotely without disruption to the use. In this paper, we propose an efficient wireless charging control method for exploiting the frequency diversity in multiuser broadband wireless networks, to reduce energy outage and keep the system operating in an efficient and sustainable state. In particular, we first analyze the impact of charging control method to the operating lifetime of a WPT-enabled broadband system. Based on the analysis, we then propose a multi-criteria charging control policy that optimizes the transmit power allocation over frequency by jointly considering the channel state information (CSI) and the battery state information (BSI) of wireless devices. For practical implementation, the proposed scheme is realized by a novel limited CSI estimation mechanism embedded with partial BSI, which significantly reduces the energy cost of CSI and BSI feedback. Simulation results show that the proposed method could significantly increase the network lifetime under stringent transmit power constraint. Reciprocally, it also consumes lower transmit power to achieve near-perpetual network operation than other single-criterion based charging control methods.Comment: This paper had been accepted by IEEE ICC 2015, Workshop on Green Communications and Networks with Energy Harvesting, Smart Grids, and Renewable Energie

Comparative Study on SOC Estimation Techniques for Optimal Battery Sizing for Hybrid Vehicles

Automotive Industry is growing at a very fast rate. Hence problems pertaining to the increasing CO2 levels in the atmosphere and the ever increasing fuel rates also increase. Electric and Hybrid electric technology has become the latest milestone for the automotive industry. In Hybrid Electric Vehicles (HEV), the reliable range of operation is characterized by batteries and battery state of charge (SOC), that describes its remaining capacity, is an important factor for providing the control strategy for the battery management system (BMS) in plug-in hybrid electric vehicles (PHEV) and electric vehicles (EV). Accuracy in estimation of the SOC is necessary not only to protect the battery, prevent it’s over discharge, and improve the battery life but also to allow the application to make rational control strategies to save energy. However, the chemical energy of a battery which is a chemical energy storage source cannot be directly accessed and this issue makes the estimation of the SOC of a battery difficult. Hence, estimation of the SOC accurately becomes very complex and is difficult to implement, as there are parametric uncertainties and the battery models are limited. In fact, in practice several examples of models of the estimation of the SOC are found which have poor accuracy and reliability. Hence a comparative study done in this paper on the various methods will help choose the right method based on the requirements and the application. This paper also reviews a case study on modeling and simulation of one of the methods of SOC estimation and efforts have been put in obtaining the performance of Li-ion batteries by calculating the SOC using Coulomb counting method in MATLAB Simulink. DOI: 10.17762/ijritcc2321-8169.16044

Circuit Synthesis of Electrochemical Supercapacitor Models

This paper is concerned with the synthesis of RC electrical circuits from physics-based supercapacitor models describing conservation and diffusion relationships. The proposed synthesis procedure uses model discretisation, linearisation, balanced model order reduction and passive network synthesis to form the circuits. Circuits with different topologies are synthesized from several physical models. This work will give greater understanding to the physical interpretation of electrical circuits and will enable the development of more generalised circuits, since the synthesized impedance functions are generated by considering the physics, not from experimental fitting which may ignore certain dynamics

Accelerated internal resistance measurements of lithium-ion cells to support future end-of-life strategies for electric vehicles

Industrial and academic communities have embarked on investigating the sustainability of vehicles that contain embedded electrochemical energy storage systems. Circular economy strategies for electric vehicle (EV) or hybrid electric vehicle (HEV) battery systems are underpinned by implicit assumptions about the state of health (SOH) of the battery. The internal resistance of battery systems is the essential property for determining available power, energy efficiency, and heat generation. Consequently, precise measurement is crucial to estimate the SOH; however, the international standards and best practice guides that exist to define the measurements include long preconditioning and rest times that make the test duration prohibitive. The aim of this research is to critically evaluate whether test duration times for internal resistance measurements can be reduced to values that may facilitate further end-of-life (EOL) options. Results reveal a newly developed technique using pulse-multisines is two to four times faster to perform when compared to the standard protocol whilst maintaining accuracy for battery electric vehicle (BEV) and HEV cells, respectively. This novel method allows different stakeholders to rank the relative importance of test accuracy verses experimental test time when categorising used Li-ion cells for different EOL applications. View Full-Tex

Meetod elektrisõiduki aku laetuse taseme täpsemaks hindamiseks

The electric vehicle (EV) is a complex, safety-critical system, which must ensure the safety of the operator and the reliability and longevity of the device. The battery management system (BMS) of an EV is an embedded system, whose main responsibility is the protection and safety of the high-voltage battery pack. The BMS must ensure that the requirements for health, status and deliverable power are met by maintaining the battery pack within the defined operational conditions for the defined lifetime of the battery. The state of charge (SOC) of a cell describes the ratio of its current capacity (amount of charge stored) to the nominal capacity as defined by the manufacturer. SOC estimation is a crucial, but not trivial BMS task as it can not be measured directly, but must be estimated from known and measured parameters, such as the cell terminal voltage, current, temperature, and the static and dynamic behavior of the cell in different conditions. Many different SOC estimation methods exist, out of which (currently) the most practical methods for implementing on a real-time embedded system are adaptive methods, which adapt to different internal and external conditions. This thesis proposes the use of the sigma point Kalman filter (SPKF) for non-linear systems as an equivalentcircuit model-based state estimator to be used in one of the current series production EV projects developed by Rimac Automobili. The estimator has been implemented and validated to yield better results than the currently used SOC estimation method, and will be deployed on the BMS of a high-performance hybrid-electric vehicle

Multi-Level Data-Driven Battery Management: From Internal Sensing to Big Data Utilization

Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multi-dimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management