Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data

Predictive maintenance aims to predict failures in components of a system, a heavy-duty vehicle in this work, and do maintenance before any actual fault occurs. Predictive maintenance is increasingly important in the automotive industry due to the development of new services and autonomous vehicles with no driver who can notice first signs of a component problem. The lead-acid battery in a heavy vehicle is mostly used during engine starts, but also for heating and cooling the cockpit, and is an important part of the electrical system that is essential for reliable operation. This paper develops and evaluates two machine-learning based methods for battery prognostics, one based on Long Short-Term Memory (LSTM) neural networks and one on Random Survival Forest (RSF). The objective is to estimate time of battery failure based on sparse and non-equidistant vehicle operational data, obtained from workshop visits or over-the-air readouts. The dataset has three characteristics: 1) no sensor measurements are directly related to battery health, 2) the number of data readouts vary from one vehicle to another, and 3) readouts are collected at different time periods. Missing data is common and is addressed by comparing different imputation techniques. RSF- and LSTM-based models are proposed and evaluated for the case of sparse multiple readouts. How to measure model performance is discussed and how the amount of vehicle information influences performance

Energy Storage Management and Simulation for Nano-Grids

Energy storage has been utilized in many forms and applications from a flashlight to the Space Shuttle. There is a worldwide effort to develop battery model with high energy level and power densities for a variety range of applications, including hybrid electric vehicles (HEV) and photovoltaic system (PV). To improve battery technology, understanding the battery modeling is very important. So, modeling the thermal behavior of a battery is a vital consideration before designing an effective thermal management system which will operate safely and prolong the lifespan of an energy storage system. The first part of this work focused on the aging model of lithium-ion battery and a simple thermal model of lithium-ion and lead-acid battery using MATLAB/Simulink. After that, an artificial neural network model (ANN) is developed to predict various characteristics at wide temperature range. In this case, comparisons between the training/testing data outputs and targets validating both models with a regression accuracy of 99.839% and 98.727% respectively for Li-ion and Lead-Acid battery while it is 99.912% for the aging model of Li-ion battery. In the end, this energy storage device is used to interconnect with HOMER. This HOMER project aims at designing a solar-wind hybrid power system for Statesboro, Georgia. The cost analysis is performed utilizing HOMER software based on solar irradiance, wind speed, and residential load profile. The proposed HOMER model, using solar & wind with the grid was more cost efficient as the cost of energy (COE) was found 0.0618$/kWh where the average residential electricity rate in Statesboro is 0.116$/kWh. As a result of using this model, the total cost is reduced by 46.72% compared to other conventional power systems. In the second part of HOMER simulation, while comparing among three types of storage devices, another minimum COE is found using wind with grid connection. As the wind speed is good enough for Statesboro, Georgia, simulation shows that minimum COE is 0.0499$/kWh, 0.0386$/kWh and 0.0633$/kWh respectively for Li-ion, Lead-acid, and Vanadium

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation

Second Life of Lithium-Ion Batteries of Electric Vehicles: A Short Review and Perspectives

Technological advancement in storage systems has currently stimulated their use in miscellaneous applications. The devices have gained prominence due to their increased performance and efficiency, together with the recent global appeal for reducing the environmental impacts caused by generating power or by combustion vehicles. Many technologies have been developed to allow these devices to be reused or recycled. In this sense, the use of lithium-ion batteries, especially in electric vehicles, has been the central investigative theme. However, a drawback of this process is discarding used batteries. This work provides a short review of the techniques used for the second-life batteries of electric vehicles and presents the current positioning of the field, the steps involved in the process of reuse and a discussion on important references. In conclusion, some directions and perspectives of the field are shown

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods

Artificial Intelligence in Process Engineering

In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre- and postprocessing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering