The report deals with energy conservation, mainly in the field of portable energy, which is asubject that today raises questions around the world. This report describes the design and theimplementation of a Battery Management System on the platform NI ELVIS II+ managed bythe software Labview. The first aim has been on finding information about the design of theBattery Management System that corresponds to the choice of the battery itself. The systemwas designed completely independent with different charging methods, simulations ofdischarge, and its own cell balancing, as a 3 cells battery pack was used. The battery chosenwas the lithium-ion technology that has the most promising battery chemistry and is the fastestgrowing. Several experimentations and simulations have been done, with and without cellbalancing that permited to highlight that the cell balancing is mandatory in a Batterymanagement System. Furthermore, a simulation of use of the battery in an Electrical Vehiclewas made, which can lead to conclude that the Lithium-Ion battery must be manageddifferently to be used in the application of an Electrical Vehicle.

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The proposed cell monitoring unit for sensing voltage, current, and temperature in a 12-cell 18650 lithium-ion battery module aims to be low-power, serving as the core of an energy-efficient battery management system and facilitating battery management functions with cell data. Notable features include a switchable voltage divider, a single op-amp differential amplifier and level shifter, and a high-precision composite amplifier. The proposed circuit is implemented on a printed circuit board. Measurement results show that the highest power dissipation under continuous operation is from the current sensing circuit at 6.03 mW under a 4 A string current, followed by the voltage sensing at 2.52 mW for the top cell and the temperature sensing at 34.9 μW. The measured power figures include the power dissipation from the battery cells in addition to the cell monitoring unit. The maximum output error is 68 mV for cell voltages up to 44.4 V, 36 mA for current up to 4 A, and 0.37 ◦C for temperature up to 73 ◦C. / M.S. / Battery management systems are required in modern rechargeable battery-operated devices to help ensure that the batteries operate within the manufacturer-specified operating range. Otherwise, damage to the batteries or to the device may occur. Battery modules are comprised of smaller energy cells to achieve the specified energy capacity and power output. At the core of a battery management system is a battery cell monitoring unit that interfaces the management system with the battery module by providing data about each of the battery cells, including voltage, current, and temperature. To help minimize the power dissipation of battery-powered devices and prolong the battery life, the power consumed by the battery management system should be small. This project aims to detail the design and results of a low-power cell monitoring unit as the core component of energy-efficient battery management systems. The proposed circuit is designed for a 12-cell lithium-ion battery module and implemented on a printed circuit board. The maximum measured power dissipation under continuous operation is 6.03 mW for the current sensing circuit, followed by the voltage sensing circuit at 2.52 mW and the temperature sensing circuit at 34.9 μW.

<p>  </p> <p>Many machine learning models exist for battery management systems to utilize. Few have been shown to work. This work focuses on gathering data from cycling battery packs and sending this data directly to machine learning models built off robust datasets for applying the resulting predicted values and outputs directly on top of real-time systems. A parasitic sensor network was created composed of a main microcontroller, a host CPU, and various sensors including resistance temperature detection devices (RTDs), a voltage measurement circuit, current measurement circuit, and an accelerometer/gyroscope. The resulting network was integrated parasitically with a 4-cell 18650 SONY VTC6 battery pack, then tested both on-ground and in-flight with a commercial quadcopter. Real-time data for the battery pack with four cells in series was gathered. This real-time data stream was then integrated with data-driven neural network algorithms trained on various 18650 datasets and a real physical model to finalize the “AI BMS”. Using the power of non-linear models to infer battery health impacts not normally considered in battery management systems, the “AI BMS” was able to use low-fidelity real-time data in conjunction with a powerful multi-faceted model to make predictive decisions about battery health characteristics on top of normal system operations.</p>

Batteries have imparted momentum to the process of transition towards a green future. However, mass application of batteries is obstructed due to their explosive nature, a trait specific to Li-Ion batteries. To cater to an efficient battery utilization, an introduction of a battery management system would provide an ultimate solution. This thesis deals with different aspects crucial in designing a battery management system for high energy as well as high power applications. To build a battery management system capable of predicting battery behavior, it is necessary to analyze the dynamic processes happening inside the battery. Hence, a battery equivalent circuit model is proposed in this thesis as well as proper analysis is done in MATLAB to project a generic structure applicable to all Li-Ion chemistries. The model accounts for all dynamic characteristics of a battery including non-linear open circuit voltage, discharge current and capacity. Effect of temperature is also modeled using a cooling system. The model is validated with test current profiles. Less than 0.1% error between measured and simulated voltage profiles indicates the effectiveness of the proposed model to predict the runtime behavior of the battery. Furthermore, the model is implemented with the energy as well as the power battery pack. State of charge calculations are performed using the proposed model and the coulomb counting method and the results indicate only a 4% variance. Therefore, the proposed model can be applied to develop a real-time battery management system for accurate battery states estimation.

This thesis presents novel battery pack designs and control methods to be used with battery packs enhanced with power electronics. There are two areas of focus: 1) intelligent battery packs that are constructed out of many hot swappable modules and 2) smart cells that form the foundation of a completely decentralised battery management system (BMS). In both areas, the concept of active loading/charging is introduced. Active loading/charging balances the cells in a battery pack by loading each cell in proportion to its capacity. In this way, the state of charge of all cells in a series string remain synchronized at all times and all of the energy storage potential from every cell is utilized, despite any differences in capacity there may be. Experimental results from the intelligent battery show how the capacity of a pack of variably degraded cells can be increased by 46% from 97 Wh to 142 Wh using active loading/charging. Engineering design challenges of building a practical intelligent battery pack are addressed. Start up and shut down procedures, and their respective circuits, were carefully designed to ensure zero current draw from the battery cells in the off state, yet also provide a simple mechanism for turning on. Intra-pack communication was designed to provide adequate information flow and precise control. Thus, two intra-pack networks were designed: a real time communication network, and a data communication network. The decentralised control algorithms of the smart cell use a small filtering inductor as a multi-purpose sensor. By analysing the voltage across this filtering inductor, the switching actions of a string of smart cells can be optimised. Experimental results show that the optimised switching actions reduce the output voltage ripple by 83% and they synchronize the terminal voltages of the smart cells, and by extension, their states of charge. This forms the basis of a decentralised BMS that does not require any communication between cells or with a centralised controller, but can still achieve cell balancing through active loading/charging.

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This thesis suggests a new approach to extend the lifetime of Lithium-ion batteries for a Home Energy Storage System equipped with a renewable energy source. The new configuration improves the lifetime of the energy storage device by using the pulsed charge-discharge method. The batteries in this system can be charged either using solar panels when solar energy is available or by the grid power during off-peak hours when the electricity cost is at its lowest rate. In the new configuration, the battery bank is split into two equal sections to employ pulsed charge-discharge method. Interrupting the charge or discharge current provides a relaxation time for the lithium ions to diffuse gradually into the electrodes material of Lithium-ion batteries, this reduces the damage in the microstructure of the electrodes and thus it helps to prolong the battery lifetime. The spilt bank strategy improves the longevity of Lithium-ion batteries while maximizing the solar energy utilization. This strategy leads to reduce the reliance on the grid power which decreases the consumer’s total energy cost as well. To show the usefulness of the new approach, different modes of operation are discussed in details along with simulation results. An experimental setup is also developed to evaluate the effectiveness of the new approach in extending the Lifetime of Lithium-ion batteries. / Thesis / Master of Applied Science (MASc)

As a conventional transportation modality, bicycles have been gradually electrified to meet the desire for convenient and green commuting patterns, especially in developed urban areas. The electric bicycle battery pack and its management system are core elements that determine key performance metrics such as electric range and output power. With respect to electric bicycle applications, focused research on the battery, its management system, and performance has received less attention compared to other energy storage applications. In this thesis, a well-developed conversion kit produced by BionX is studied. A data collecting system is first installed to record both mechanical and electrical data, such as speed, power and voltage; this enables defining two standard riding cycles at different riding conditions. Two benchmarking tests are performed to investigate the battery life in pure electric mode and at different threshold levels of optimal assistance. A novel quadratic programming based fitting algorithm is derived and applied in both time and frequency domain parameter identification tests. The proposed algorithm is able to fit single/multiple pulses by applying a masking vector. Sensitivity study and experimental results show the high robustness and fast computation time of the approach compared to existing and commonly used methods, such as fmincon. The comparison between hybrid power pulse characterization (HPPC) and electrochemical impedance spectrum (EIS) tests are performed in terms of extracted internal resistance. A second-order RC battery model is developed using parameters extracted from HPPC tests. The model is validated by experimental riding cycles and used to generate the reference SOC profiles that are employed in a SOC estimation study. Four estimation strategies, including extended Kalman Filter (EKF), Sigma point Kalman Filter (SPKF), Cubature Kalman Filter (CKF), and joint extended Kalman Filter (JEKF), are compared systematically in terms of accuracy, robustness and computation complexity. / Thesis / Master of Applied Science (MASc)