Data driven techniques for on-board performance estimation and prediction in vehicular applications.

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Methodology and Guidelines for Designing Flexible BMS in Automotive Applications

The fragile characteristics of Li-ion batteries lead to the need of battery management system (BMS) to carefully supervise them during the operation. Since there are so many variations in battery configurations, the BMS usually must undergo many iterations of the development cycle, which take a long time to optimize and finalize the design. Previously, many works adopted the idea of modularized BMS to address these issues, but they still have some skeptical issues such as measurement approaches or difficulties in reconfiguration. This paper presents a guideline on the crucial aspects of flexible BMS designs for automotive applications, which aims to reduce time and effort for developing a new BMS for automotive battery pack. The guideline covers some crucial aspects pertaining the automotive BMS hardware implementation, SOC estimation algorithm and its computational performance based on Extended Kalman Filter (EKF) and Luenberger Observer (LO) with 3 levels of Electrochemical model (ECM). All of the tests were carried out in a small-scale microcontroller. It was found that 2-RC ECM gives the best trade-off between SOC estimation accuracy and computational time. While the 3-RC ECM provides 9.5% and 31% higher accuracy than the 2-RC and 1-RC ECM, respectively, but taking 88% and 240% higher computational time than the latter two cases. The optimal speed of the observer poles of LO algorithm are suggested to be in the range of 2-5 times faster than the system poles, which makes the convergence speed to be comparable to the EKF algorithm but is still able to keep the SOC estimation error in the range of 3-5%. These results can be used to make a trade-off between estimation accuracy and computational time, to select the optimal SOC estimation algorithm for onboard BMSs

Modelling and State Estimation of Batteries

En este trabajo se ha realizado un estudio sobre el modelado y la estimación de los estados de una batería de Litio principalmente, y en la parte final del mismo sobre una batería de Plomo. Este estudio se ha desarrollado simultáneamente en MapleSim, Matlab y Simulink, utilizando el algoritmo denominado Filtro de Kalman (Kalman Filter en inglés) para estimar el estado de carga (SOC) y el estado de salud (SOH) de la batería. Este algoritmo ha sido extensamente validado a lo largo del trabajo mediante simulación, y se ha llegado a demostrar su robustez contra el ruido utilizado. Por otro lado también se ha estudiado la degradación que sufre la batería en función de la temperatura de las celdas que la componen.Departamento de Ingeniería de Sistemas y AutomáticaGrado en Ingeniería en Organización Industria

Practice and Innovations in Sustainable Transport

The book continues with an experimental analysis conducted to obtain accurate and complete information about electric vehicles in different traffic situations and road conditions. For the experimental analysis in this study, three different electric vehicles from the Edinburgh College leasing program were equipped and tracked to obtain over 50 GPS and energy consumption data for short distance journeys in the Edinburgh area and long-range tests between Edinburgh and Bristol. In the following section, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation is proposed to accurately estimate state of charge (SOC), which is vital for safe operation and efficient management of lithium-ion batteries. A coupled-inductor DC-DC converter with a high voltage gain is proposed in the following section to match the voltage of a fuel cell stack to a DC link bus. Finally, the book presents a review of the different approaches that have been proposed by various authors to mitigate the impact of electric buses and electric taxis on the future smart grid

하이브리드 에너지 시스템용 리튬 이온 전지에 대한 모델링, 상태 추정 및 제어

학위논문 (박사)-- 서울대학교 대학원 : 화학생물공학부, 2012. 8. 한종훈.최근 휴대용 기기와 친환경 자동차의 에너지 저장 시스템이 큰 관심을 받으며, 특히 전압과 출력 면에서 높은 성능을 보이는 리튬 이온 전지가 가장 각광을 받고 있다. 이러한 리튬 이온 전지는 친환경 자동차와 같은 하이브리드 에너지 시스템의 주요 출력 공급원 또는 보조 출력 공급원으로 쓰인다. 친환경 자동차 내에 탑재되는 리튬 이온 전지의 경우 자동차에 출력을 공급하는 동시에 여분의 출력이 남는 경우 이를 저장하는 역할을 수행한다. 따라서 리튬 이온 전지의 성능이 친환경 자동차의 성능을 대표할 수 있는 중요한 요소 중 하나라고 할 수 있다. 리튬 이온 전지의 성능을 대표하여 나타낼 수 있는 것으로 가용 잔존 용량과 건전 상태를 들 수 있다. 이러한 두 종류의 상태는 센서를 통해 직접 측정이 불가능하기 때문에 이를 추정하기 위해 동적 상세 모델의 개발이 필요하다. 따라서 본 논문에서는 리튬 이온 전지에 대한 동적 상세 모델을 개발하였다. 그리고 이를 바탕으로 하여 전지의 가용 잔존 용량과 건전 상태를 추정하는 방법론을 개발하였다. 최종적으로 본 논문에서 개발한 모델 및 추정 방법론을 연료 전지 하이브리드 자동차의 최적 제어 방법론에 적용하였다. 우선, 친환경 자동차에 쓰이는 리튬 이온 전지의 가용 잔존 용량 추정 알고리즘을 제안하고자 한다. 제안된 방법론은 다양한 온도, 운전 상태 및 출력 부하에 따른 운전 조건에 부합할 수 있는 강건한 가용 잔존 용량 추정을 기본적인 목표로 한다. 이러한 방법론은 전기화학적 모델에 기반을 둔 등가 회로 모델과 재귀 추정자를 포함하고 있다. 등가 회로 모델에 필요한 각 파라미터들은 다양한 온도 및 전류 조건에 따른 실험에 의해 추정하였다. 이러한 모델에 기반을 둔 가용 잔존 용량 추정 방법론과 전류 적산에 의한 추정 방법론의 결합을 통해 가용 잔존 용량의 추정 알고리즘을 개발하였다. 제안된 방법론은 저온 및 상온, 고온 상태에서의 다양한 운전 범위 하에서 실시된 리튬 이온 전지 팩에 대한 실험을 통해 검증하였다. 또한 각종 센서의 이상으로 인한 경우에 대해서도 검증하여 신뢰성을 입증하였다. 그 결과 제안된 방법론은 가용 잔존 용량의 추정에 적당하다는 것을 알 수 있으며 다양한 조건에 적합하고 센서 오류에 대한 문제에도 신뢰성을 가지고 있으며 계산 부하가 작기 때문에 온라인으로 적용 가능하다는 사실 또한 입증하였다. 또한 건전 상태로 대표되는 리튬 이온 전지의 실제 성능에 대한 온라인 감시를 위한 알고리즘을 개발하였다. 여러 변수 중 전지의 충전 용량이 건전 상태를 나타낼 수 있는 대표적인 변수로 선정되었다. 이러한 충전 용량의 추정을 위해 주요 알고리즘, 보조 알고리즘, 두 알고리즘을 결합한 통합 알고리즘의 세 가지 알고리즘을 개발하였다. 주요 알고리즘은 간략화한 등가 회로 모델과 소프트 센서 기술을 결합하여 개발하였다. 소프트 센서 기술은 시스템 인지 방법론과 이동 지평선 추정 방법론에 기반을 둔 방법론으로 파라미터 추정 방법에 주로 사용하였다. 그리고 주요 알고리즘의 계산 부하 문제를 해결하기 위해 보조 방법론을 개발하였다. 그리고 이 두 알고리즘의 단점을 상쇄하고 장점을 극대화하기 위해 두 알고리즘을 결합하여 통합 알고리즘을 개발하였다. 개발된 알고리즘의 적합성을 평가하기 위해 새로운 상태의 전지와 열화된 전지에 대해 다양한 온라인 추정 시험을 거쳤다. 다양한 하이브리드 자동차 및 플러그인 하이브리드 자동차용 리튬 이온 전지에 대한 실험 결과, 개발한 알고리즘은 정확도, 신뢰성, 강건성 및 계산 부하에 대해 강점을 가지고 있어 적합하다는 결론을 내릴 수 있었다. 즉, 개발한 통합 알고리즘은 충전 용량 및 가용 출력에 대해 실시간으로 정량적인 값을 온라인 형태로 추정할 수 있어 실제 하이브리드 자동차 계열 리튬 이차 전지 시스템에 대한 응용에 적합하다는 것을 알 수 있다. 마지막으로 퍼지 제어 논리를 이용하여 고분자 전해질 연료 전지/리튬 이온 전지 하이브리드 에너지 시스템의 최적 제어 논리를 설계하였다. 이를 위해 우선적으로 앞에서 개발된 리튬 이온 전지 모델과 고분자전해질연료전지 시스템에 대해 모사를 하였다. 특히 고분자 전해질 연료 전지의 경우 수소 재활용과 공기극의 가습을 고려하여 모사하였다. 최적 제어기는 퍼지 논리 알고리즘을 활용하여 개발하였다. 이 제어기는 세 가지의 입력 변수가 있다. 그 중 첫 번째 변수인 연료 전지 하이브리드 자동차에서 요구하는 출력을 통해 연료 전지에서 생산해야 하는 출력을 계산하였다. 또한, 앞에서 개발한 방법론을 통해 추정이 가능한 가용 잔존 용량과 건전 상태 역시 제어기의 입력 값으로 사용되었다. 이렇게 개발한 퍼지 제어기를 친환경 자동차의 다양한 운전 조건 및 리튬 이온 전지의 상태에 대해 검증하였다. 검증 결과 제안된 퍼지 논리 제어기를 통해 친환경 자동차의 운전 및 부품 교환 비용을 줄일 수 있으며 최적으로 운전을 할 수 있어 실제 이러한 시스템의 운영에 적합하다는 것을 알 수 있었다. 이러한 연구는 하이브리드 에너지 시스템을 위한 리튬 이온 전지에 대한 상태 추정 및 제어를 온라인으로 가능하게 할 수 있다. 본 논문에서 소개한 모델, 상태 추정 방법론과 제어 논리는 친환경 자동차와 같은 하이브리드 에너지 시스템에 대해 온라인으로 적용할 수 있을 것으로 보인다.In recent years, energy storage systems have been highlighted in portable electronics and eco-friendly vehicle applications. In particular, lithium-ion batteries are used as principal or auxiliary power supply devices for the eco-friendly vehicle applications as hybrid energy systems due to high performance of voltage and power. The batteries in the eco-friendly vehicles either store excess power from the vehicle or supply insufficient power to vehicle motive power generator. Therefore, the performance of the lithium-ion battery is a key variable for the performance evaluation of eco-friendly cars. The key variables of the lithium-ion battery are state-of-charge and state-of-health. Rigorous dynamic model is required to estimate the key variables as state. Therefore, the lithium-ion battery model for hybrid energy system is presented in this thesis. The estimation methodologies for state-of-charge and state-of-health are suggested based on the developed model. Finally, the developed model and estimation methodologies are applied to the optimal control logic of fuel cell hybrid electric vehicle as the hybrid energy system. This thesis describes a state-of-charge estimation methodology for lithium-ion batteries in eco-friendly vehicles. The proposed methodology is intended for state-of-charge estimation under various operating conditions including changes in temperature, driving mode and power duty. The suggested methodology consists of a recursive estimator and employs an equivalent circuit as the electrochemical cell model. Model parameters are estimated by parameter map on experimental cell data with various temperatures and current conditions. The parameter map is developed by a least sum square error estimation method based on nonlinear programming. An adaptive estimator is employed and is based on the combination of current integration and battery model based estimation. The proposed state-of-charge estimation methodology is validated with experimental lithium-ion battery pack data under various driving schedules with low and ambient temperatures and sensor fault cases. The presented results show that the proposed model and methodology are appropriate for estimating state-of-charge under various conditionspower duty, temperature and sensor fault situations. State-of-health estimation algorithms for the actual performance of a lithium-ion battery as state-of-health are presented for on-line monitoring. The capacity is selected as the representative variable, which indicates the performance of the battery. Three algorithms are suggested to estimate the degree of capacity fading: principal algorithm, supplementary algorithm, and hybridized algorithm. The principal algorithm is based on a simplified equivalent circuit model and soft sensor technique. The soft sensor technique is based on a system identification methodology with variance inhibition based approach. The second algorithm is developed to compensate for the problem of computational load. Finally, both of the algorithms are combined in a hybridized algorithm to complement each other. The suitability of algorithms is demonstrated with on-line monitoring of fresh and aged cells using cyclic experiments. The results from diverse experiments for hybrid electric vehicle and plug-in hybrid electric vehicle applications demonstrate the appropriateness of the accuracy, reliability against the inaccurate previous estimated values and computational load. Consequently, the developed hybridized algorithm was appropriate for on-line estimation of the actual battery performance as quantitative values of capacity and power in real time. The optimal control logic for lithium-ion battery / proton exchange membrane fuel cell hybrid energy system is developed using fuzzy logic controller. The developed lithium-ion battery model is applied to design of the control logic. The proton exchange membrane fuel cell system model with hydrogen recirculation and cathode humidifier is developed. The optimal controller is suggested by fuzzy logic control algorithm. Demanded power of the fuel cell hybrid electric vehicle is used with the fuzzy logic controller to calculate the output power from the fuel cell system. In addition, estimated state-of-charge and state-of-health are used as input variables of the fuzzy logic controller. The fuzzy controller is validated with various operations for the eco-friendly vehicles as the hybrid energy system. The suggested control logic is appropriate for application in commercialization and practical usage of the eco-friendly vehicles. This work could contribute to state estimation and control of the lithium-ion battery for the hybrid energy system. The developed models, state estimation methodologies and control logic could be implemented to on-line application for practical usage of the eco-friendly vehicle.CHAPTER 1 : Introduction 1 1.1. Research motivation 1 1.2. Research objectives 7 1.3. Outline of the thesis 8 CHAPTER 2 : Lithium-ion Battery Modeling and State-of-Charge Estimation 9 2.1. Introduction 9 2.2. Lithium-ion battery modeling by equivalent circuit model 12 2.3. Estimation of model parameter 20 2.3.1. Estimation using least square estimation method 20 2.3.2. Parameter map – lumped Ri and Ci 23 2.3.3. Parameter map – lumped R0 24 2.4. Methodology for State-of-Charge estimation 35 2.5. Results and analysis 39 2.5.1. Dynamic battery modeling 39 2.5.2. State-of-Charge estimation methodology 40 2.6. Conclusions 55 CHAPTER 3 : State-of-Health Estimation 56 3.1. Introduction 56 3.2. Research background 58 3.2.1. State-of-Health estimation techniques 58 3.2.2. Research objectives 59 3.2.3. Experiments 60 3.3. Principal algorithm 62 3.3.1. Model description 62 3.3.2. Soft sensor estimation methodology 64 3.4. Supplementary algorithm 72 3.5. Hybridized algorithm 79 3.6. Results and analysis 82 3.6.1. Estimation results of the principal algorithm 82 3.6.2. Estimation results of the supplementary algorithm 84 3.6.3. Estimation results of the hybridized algorithm 85 3.6.4. Analysis and discussion 86 3.7. Conclusions 102 CHAPTER 4 : Optimal Control of Hybrid Energy System 103 4.1. Introduction 103 4.2. Hybrid energy system modeling 106 4.2.1. Target system configuration 106 4.2.2. Lithium-ion battery modeling 107 4.2.3. Proton exchange membrane fuel cell modeling 110 4.2.4. Vehicle modeling 112 4.3. Fuzzy control logic 119 4.3.1. Background theory 119 4.3.2. Control problem formulation 121 4.3.3. Design of fuzzy controller 123 4.4. Results and analysis 137 4.4.1. Vehicle driving schedule 137 4.4.2. Optimal fuzzy control 138 4.4.3. Analysis and discussion 140 4.5. Conclusions 152 CHAPTER 5 : Concluding Remarks 153 5.1. Conclusions 153 5.2. Future works 156 Nomenclature 157 Literature cited 162 Abstract in Korean (요 약) 180Docto

Estimation of state of charge of lead-acid battery used in solar photovoltaic system

7-19An accurate estimation of State of charge (SOC) of the lead-acid battery is of paramount importance for the efficient and reliable operation of solar photovoltaic (SPV) sytem. There are mainly four methods used for estimating SOC of the battery, viz. chemical, voltage, current integration and kalman filtering. In this present study, the SOC as indicated by the solar power conditioning unit (SPCU) was taken as reference and at every 5% SOC reduction, the other parameters such as- i) specific gravity of electrolyte, ii) battery terminal voltage, iii) (Ampere Hour) Ah and iv) energy deliverd to the resistive load were recorded. Based on this recorded values the SOC was predicted. The standard deviation (S.D.) of the difference of predicted SOC to the reference SOC was calculated based on specific gravity, Voltage, Ah and energy. The SD obtained was 6.17, 5.67, 0.33, 0.75 respectively. The specific gravity value for the battery electrolyte decreases with the decrease in the battery SOC%, the maximum value of SG at 100% SOC was 1.23 and the minimum at 20% SOC was 1.14. The terminal voltage was also got reduced with the reduction in SOC, from 24.85V at 100% SOC to 22.4V at 20% SOC. The energy stored by the battery during charging was 3.65 units and the energy delivered from the battery to the load was 3.245 units.  The efficiency of solar panel, lead-acid battery and the combined SPV system was 12.79%, 88.9% and 9.68% respectively. It was found that the SOC of the lead-acid battery would be more accurate when it is estimated based on current integration i.e., Ah, the SOC estimation based on energy is also acceptable since the SD for both is less than 1. Hence, through this investigation we can say that SOC prediction based on Ah or kWh measurement is more appropriate than specific gravity and voltage methods

Batteries and Supercapacitors Aging

Electrochemical energy storage is a key element of systems in a wide range of sectors, such as electro-mobility, portable devices, and renewable energy. The energy storage systems (ESSs) considered here are batteries, supercapacitors, and hybrid components such as lithium-ion capacitors. The durability of ESSs determines the total cost of ownership, the global impacts (lifecycle) on a large portion of these applications and, thus, their viability. Understanding ESS aging is a key to optimizing their design and usability in terms of their intended applications. Knowledge of ESS aging is also essential to improve their dependability (reliability, availability, maintainability, and safety). This Special Issue includes 12 research papers and 1 review article focusing on battery, supercapacitor, and hybrid capacitor aging

Eco-Driving planification profile for electric motorcycles

Los perfiles de Eco-Driving son algoritmos capaces de utilizar información adicional para crear recomendaciones o limitaciones sobre las capacidades del conductor. Aumentan la autonomía del vehículo, pero actualmente su uso no está relacionado con la autonomía requerida por el conductor. Por esta razón, en este trabajo, el desafío de la conducción ecológica se traduce en un controlador óptimo de dos capas diseñado para vehículos eléctricos puros. Este controlador está orientado a asegurar que la energía disponible sea suficiente para completar un viaje demandado, agregando límites de velocidad para controlar la tasa de consumo de energía. Se exponen y analizan los modelos mecánicos y eléctricos requeridos. La función de costo está optimizada para corresponder a las necesidades de cada viaje de acuerdo con el comportamiento del conductor, el vehículo y la información de la trayectoria. El controlador óptimo propuesto en este trabajo es un controlador predictivo de modelo no lineal (NMPC) asociado a una optimización unidimensional no lineal. La combinación de ambos algoritmos permite aumentar alrededor de un 50% la autonomía con una limitación del 30% de las capacidades de velocidad y aceleración. Además, el algoritmo es capaz de asegurar una autonomía final con un 1,25% de error en presencia de ruido de sensor y actuador.The Eco-Driving profiles are algorithms capable to use additional information in order to create recommendations or limitation over the driver capabilities. They increase the autonomy of the vehicle but currently its usage is not related to the autonomy required by the driver. For this reason, in this paper, the Eco-Driving challenge is translated into two layers optimal controller designed for pure electric vehicles. This controller is oriented to ensure that the energy available is enough to complete a demanded trip, adding speed limits to control the energy consumption rate. The mechanical and electrical models required are exposed and analyzed. The cost function is optimized to correspond to the needs of each trip according to driver behavior, vehicle and trajectory information. The optimal controller proposed in this paper is a nonlinear model predictive controller (NMPC) associated to a nonlinear unidimensional optimization. The combination of both algorithms lets to increase around 50% the autonomy with a limitation of the 30% of the speed and acceleration capabilities. Also, the algorithm is capable to ensure a final autonomy with a 1.25% of error in the presence of sensor and actuator noise.Doctor en IngenieríaDoctorad

Critical evaluation of the battery electric vehicle for sustainable mobility

Can Battery Electric Vehicles replace conventional internal combustion engine vehicles for commuting purposes when exposed to a busy corporate activity within the city of Edinburgh?This thesis investigates the application of Battery Electric Vehicles (BEV) use in a commercial business environment in the city of Edinburgh, Scotland UK. The motivation behind this work is to determine if the Battery Electric Vehicle can replace conventional fossil fuel vehicles under real world drive cycles and the desire by many to combat the causes of climate change.Due to the nature of this work a significant part of the work will be underpinned by the quantitative methodology approach to the research. As the question indicates the research is supported by real live data coming from the vehicle both in proprietary data logging as well as reading and analysing the data coming from the vehicles own Electronic Control Unit (ECU).There will be mixed research methodology encompassing quantitative and qualitative research to obtain a complete response in respect to the management of the vehicle these methodologies will be the analysis of the measurable data as well as explorative, to gain the underlying reasons and motivations for choosing a battery electric vehicle as an option to the conventional vehicle for this type of application use