In situ diagnostics and prognostics of wire bonding faults in IGBT modules for electric vehicle drives

This paper presents a diagnostic and prognostic condition monitoring method for insulated-gate bipolar transistor (IGBT) power modules for use primarily in electric vehicle applications. The wire-bond-related failure, one of the most commonly observed packaging failures, is investigated by analytical and experimental methods using the on-state voltage drop as a failure indicator. A sophisticated test bench is developed to generate and apply the required current/power pulses to the device under test. The proposed method is capable of detecting small changes in the failure indicators of the IGBTs and freewheeling diodes and its effectiveness is validated experimentally. The novelty of the work lies in the accurate online testing capacity for diagnostics and prognostics of the power module with a focus on the wire bonding faults, by injecting external currents into the power unit during the idle time. Test results show that the IGBT may sustain a loss of half the bond wires before the impending fault becomes catastrophic. The measurement circuitry can be embedded in the IGBT drive circuits and the measurements can be performed in situ when the electric vehicle stops in stop-and-go, red light traffic conditions, or during routine servicing

Advanced Modular Power Approach to Affordable, Supportable Space Systems

Recent studies of missions to the Moon, Mars and Near Earth Asteroids (NEA) indicate that these missions often involve several distinct separately launched vehicles that must ultimately be integrated together in-flight and operate as one unit. Therefore, it is important to see these vehicles as elements of a larger segmented spacecraft rather than separate spacecraft flying in formation. The evolution of large multi-vehicle exploration architecture creates the need (and opportunity) to establish a global power architecture that is common across all vehicles. The Advanced Exploration Systems (AES) Modular Power System (AMPS) project managed by NASA Glenn Research Center (GRC) is aimed at establishing the modular power system architecture that will enable power systems to be built from a common set of modular building blocks. The project is developing, demonstrating and evaluating key modular power technologies that are expected to minimize non-recurring development costs, reduce recurring integration costs, as well as, mission operational and support costs. Further, modular power is expected to enhance mission flexibility, vehicle reliability, scalability and overall mission supportability. The AMPS project not only supports multi-vehicle architectures but should enable multi-mission capability as well. The AMPS technology development involves near term demonstrations involving developmental prototype vehicles and field demonstrations. These operational demonstrations not only serve as a means of evaluating modular technology but also provide feedback to developers that assure that they progress toward truly flexible and operationally supportable modular power architecture

Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling

Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics

Data driven prognostics for predicting remaining useful life of IGBT

Power electronic devices such IGBT (Integrated Gate Bipolar Transistor) are used in wide range of applications such as automotive, aerospace and telecommunications. The ability to predict degradation of power electronic components can minimise the risk of their failure while in operation. Research in this area aims to develop prognostics strategies for predicting degradation behaviour, failure modes and mechanisms, and remaining useful life of these electronic components. In this paper, data driven prognostics approaches based on Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models are developed and used to predict the degradation of an IGBT device. IGBT life data under thermal overstress load condition with square signal gate voltage bias, available from NASA prognostics data repository, is used to demonstrate the proposed data-driven prognostics strategy. The monitored collector-emitter voltage is used to identify the pattern and duration of different phases in the applied voltage load. The NN and ANFIS models are trained with a subset of the test data to predict remaining useful life (RUL) of the IGBT device under varying load test profiles. The predictive capability and performance of the models is observed and analysed

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

In-situ health monitoring of IGBT power modules in EV applications

Power electronics are an enabling technology and play a critical role in the establishment of an environmentally-friendly and sustainable low carbon economy. The electrification of passenger vehicles is one way of achieving this goal. It is well acknowledged that Electric vehicles (EVs) have inherent advantages over the conventional internal combustion engine (ICE) vehicles owing to the absence of emissions, high efficiency, and quiet and smooth operation. Over the last 20 years, EVs have improved significantly in their system integration, dynamic performance and cost. It has attracted much attention in research communities as well as in the market. In 2011 electric vehicle sales were estimated to reach about 20,000 units worldwide, increasing to more than 500,000 units by 2015 and 1.3 million by 2020 which accounts for 1.8 per cent of the total number of passenger vehicles expected to be sold that year. In general, electric vehicles use electric motors for traction drive, power converters for energy transfer and control, and batteries, fuel cells, ultracapacitors, or flywheels for energy storage. These are the core elements of the electric power drive train and thus are desired to provide high reliability over the lifetime of the vehicle. One of the vulnerable components in an electric power drive train is the IGBT switching devices in an inverter. During the operation, IGBT power modules will experience high mechanical and thermal stresses which lead to bond wire lift-off and solder joint fatigue faults. Theses stresses can lead to malfunctions of the IGBT power modules. A short-circuit or open-circuit in any of the power modules may result in an instantaneous loss of traction power, which is dangerous for the driver and other road users. These reliability issues are very complex in their nature and demand for the development of analytical models and experimental validation. This work is set out to develop an online measurement technique for health monitoring of IGBT and freewheeling diodes inside the power modules. The technique can provide an early warning prior to a power device failure. Bond wire lift-off and solder fatigue are the two most frequently occurred faults in power electronic modules. The former increases the forward voltage drop across the terminals of the power device while the latter increase the thermal resistance of the solder layers. As a result, bond wire lift-off can be detected by a highly sensitive and fast operating in-situ monitoring circuit. Solder joint fatigue is detected by measuring the thermal impedance of the power modules. This thesis focuses on the design and optimisation of the in-situ health monitoring circuit in an attempt to reducing noise, temperature variations and measurement uncertainties. Experimental work is carried out on a set of various IGBT power modules that have been modified to account for different testing requirements. Then the lifetime of the power module can be estimated on this basis. The proposed health monitoring system can be integrated into the existing IGBT driver circuits and can also be applied to other applications such as industrial drives, aerospace and renewable energy.EThOS - Electronic Theses Online ServiceORSSchool of EEEGBUnited Kingdo

In-situ health monitoring of IGBT power modules in EV applications

Power electronics are an enabling technology and play a critical role in the establishment of an environmentally-friendly and sustainable low carbon economy. The electrification of passenger vehicles is one way of achieving this goal. It is well acknowledged that Electric vehicles (EVs) have inherent advantages over the conventional internal combustion engine (ICE) vehicles owing to the absence of emissions, high efficiency, and quiet and smooth operation. Over the last 20 years, EVs have improved significantly in their system integration, dynamic performance and cost. It has attracted much attention in research communities as well as in the market. In 2011 electric vehicle sales were estimated to reach about 20,000 units worldwide, increasing to more than 500,000 units by 2015 and 1.3 million by 2020 which accounts for 1.8 per cent of the total number of passenger vehicles expected to be sold that year. In general, electric vehicles use electric motors for traction drive, power converters for energy transfer and control, and batteries, fuel cells, ultracapacitors, or flywheels for energy storage. These are the core elements of the electric power drive train and thus are desired to provide high reliability over the lifetime of the vehicle. One of the vulnerable components in an electric power drive train is the IGBT switching devices in an inverter. During the operation, IGBT power modules will experience high mechanical and thermal stresses which lead to bond wire lift-off and solder joint fatigue faults. Theses stresses can lead to malfunctions of the IGBT power modules. A short-circuit or open-circuit in any of the power modules may result in an instantaneous loss of traction power, which is dangerous for the driver and other road users. These reliability issues are very complex in their nature and demand for the development of analytical models and experimental validation. This work is set out to develop an online measurement technique for health monitoring of IGBT and freewheeling diodes inside the power modules. The technique can provide an early warning prior to a power device failure. Bond wire lift-off and solder fatigue are the two most frequently occurred faults in power electronic modules. The former increases the forward voltage drop across the terminals of the power device while the latter increase the thermal resistance of the solder layers. As a result, bond wire lift-off can be detected by a highly sensitive and fast operating in-situ monitoring circuit. Solder joint fatigue is detected by measuring the thermal impedance of the power modules. This thesis focuses on the design and optimisation of the in-situ health monitoring circuit in an attempt to reducing noise, temperature variations and measurement uncertainties. Experimental work is carried out on a set of various IGBT power modules that have been modified to account for different testing requirements. Then the lifetime of the power module can be estimated on this basis. The proposed health monitoring system can be integrated into the existing IGBT driver circuits and can also be applied to other applications such as industrial drives, aerospace and renewable energy.EThOS - Electronic Theses Online ServiceORSSchool of EEEGBUnited Kingdo

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place

Analysis, Development And Design For Early Fault Detection And Fire Safety In Lithium-Ion Battery Technology

Energy storage technologies in its natural form play a key role in the electrical infrastructure, renewable and mobility industry. This form includes the material nomenclature for cell. technology, battery module design, Battery enclosure system design, control, and communication strategy, chemistry profile of various cell technologies, formation and formfactors of cell structure, electrical and mechanical properties of a lithium-ion cell, behavior of the cell under high voltage, low voltage, elevated temperature and lower temperature, multiple charging of a lithium-ion batteries. Energy storage industry is growing rapidly, and the industry is experiencing an unprecedented safety concern and issues in terms of fire and explosion at cell and system level. There has been. other research conducted with proposed theories and recommendations to resolve these issues. The failure modes for energy storage systems can be derived using different methodologies such as failure mode effects analysis (FMEA). Early detection mode and strategies in lithium-ion batteries to overcome the failure modes can be caused by endothermic reaction in the cell, further protection. devices, fire inhibition and ventilation. Endothermic safety involves modifications of materials in anode, cathode, and electrolyte. Chemical components added to the battery electrolyte improve the characteristics helping in the improvement of solid-electrolyte interphase and stability. Traditional energy storage system protection device fuse at the cell level, and contactors at the rack level and circuit breakers, current interrupt devices, and positive temperature coefficient devices at the system level. This research will employ classical experimental methods to explore, review and evaluate all the five main energy technologies and narrow down to electrochemical energy storage technologies. with the two main market ready lithium-ion battery technology (LiFePO4/ G and NMC/G) technology cells and why are they valuable in the energy storage and E-mobility space. Also, will focus on the electrical, mechanical design, testing of the battery module into a rack system, advancements in battery chemistries, relevant modes, mechanisms of potential failures, and early detection strategies to overcome these failures. Finally, how the problems of fires, safety concerns and difficulty in transporting already fully assembled energy storage systems can be resolved and be demystified in lithium-ion technology. Keywords Control strategy, Energy storage system, electrolyte, failure mode, early detection, Lithium-Ion cell technology, Battey system