Current consumption measurements are useful in a wide variety of applications, including power monitoring and fault detection within a lithium battery management system (BMS). This measurement is typically taken using either a shunt resistor or a Hall-effect current transducer. Although both methods have achieved accurate current measurements, shunt resistors have inherent power loss and often require isolation circuitry, and Hall-effect sensors are generally expensive. This work explores a novel alternative to sensing battery current by measuring terminal voltages and cell temperatures and using an unknown input observer (UIO) to estimate the battery current. An accurate model of a LiFePO4 cell is created and is then used to characterize a model of the proposed current estimation technique. Finally, the current estimation technique is implemented in hardware and tested in an online BMS environment. Results show that the current estimation technique is sufficiently accurate for a variety of applications including fault detection and power profiling.

Electrical vehicles are getting more popular as the technology around batteries and electrical motors are catching up to the more common combustion engines. Electrical boats are no exception but there are still a lot of boats using old combustion engines that have a big impact on the environment. This study aims to deepen the understanding of the integration of electrical motors into boats by proposing a design of a system using a bidirectional synchronous buckboost converter. This converter is designed to handle the power transfer in a dual battery application, namely consisting of a 12 V battery and a 48 V battery. The converter includes proposed components, a PCB design, as well as the software that is required for the control of the power transfer. The results show that the converter design meets specification and, when using a test-bench, the software is capable of controlling the converter to achieve constant current and constant voltage in both directions.

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<p><strong>Abstract</strong></p> <p><strong> </strong>Lithium-ion cells have been the workhorse for customer products including laptop computers, cell phones and battery energy storage systems that can store energy from renewable energy sources. The internal resistance of them is very low, resulting in a low amount of wasted energy. Performance of Li-Ion cells is wonderful if they treated well. For this reason, an efficient battery management system (BMS) is essential in order to maximize the battery's capacity, battery means a collection of cells wired in series providing a higher voltage. So by using a BMS, SOC (State Of Charge) levels of cells get closer to each other resulting increased life and capacity of a battery. There are active and passive battery balancers. The energy which is extracted from those cells with higher SOC using passive balancer is wasted in heat, so it is not so efficient in terms of wasted energy in comparison with active balancing algorithms. There are different types of active balancing techniques. Cell to cell technique, cell to battery and battery to cell. Among the above mentioned, cell to battery performs well. However, when push-pull converters (which have high performance) are used, flux imbalance phenomenon ( which is resulted in a asymmetric hysteresis loop of a magnetic core) is unavoidable. In order to prevent this phenomenon, current mode topology is used. Hence, the transistors won't burn out due to this effect. In this thesis I simulated current mode algorithms with push-pull converters with MATLAB SIMULINK. I also physically designed push-pull converters and built it with help of chips ( current mode controllers, optocouplers, transistors, fast recovery diodes, linear and shunt regulators). I did simulate two active balancing methods ( Cell to cell and cell to battery) and compared the results. The results that came from cell to battery indicated a better performance in terms of balancing speed. For three cells balancing time reduced from 3570 seconds in cell to cell method to 518 seconds in cell to battery method.</p> / Master of Science in Electrical and Computer Engineering (MSECE)

Lithium-ion based battery energy storage system has become one of the most popular forms of energy storage system for its high charge and discharge efficiency and high energy density. This dissertation proposes a high-efficiency grid-tie lithium-ion battery based energy storage system, which consists of a LiFePO4 battery based energy storage and associated battery management system (BMS), a high-efficiency bidirectional ac-dc converter and the central control unit which controls the operation mode and grid interface of the energy storage system. The BMS estimates the state of charge (SOC) and state of health (SOH) of each battery cell in the pack and applies active charge equalization to balance the charge of all the cells in the pack. The bidirectional ac-dc converter works as the interface between the battery pack and the ac grid, which needs to meet the requirements of bidirectional power flow capability and to ensure high power factor and low THD as well as to regulate the dc side power regulation. A highly efficient dual-buck converter based bidirectional ac-dc converter is proposed. The implemented converter efficiency peaks at 97.8% at 50-kHz switching frequency for both rectifier and inverter modes. To better utilize the dc bus voltage and eliminate the two dc bus bulk capacitors in the conventional dual-buck converter, a novel bidirectional ac-dc converter is proposed by replacing the capacitor leg of the dual-buck converter based single-phase bidirectional ac-dc converter with a half-bridge switch leg. Based on the single-phase bidirectional ac-dc converter topology, three novel three-phase bidirectional ac-dc converter topologies are proposed. In order to control the bidirectional power flow and at the same time stabilize the system in mode transition, an admittance compensator along with a quasi-proportional-resonant (QPR) controller is adopted to allow smooth startup and elimination of the steady-state error over the entire load range. The proposed QPR controller is designed and implemented with a digital controller. The entire system has been simulated in both PSIM and Simulink and verified with hardware experiments. Small transient currents are observed with the power transferred from rectifier mode to inverter mode at peak current point and also from inverter mode to rectifier mode at peak current point. The designed BMS monitors and reports all battery cells parameters in the pack and estimates the SOC of each battery cell by using the Coulomb counting plus an accurate open-circuit voltage model. The SOC information is then used to control the isolated bidirectional dc-dc converter based active cell balancing circuits to mitigate the mismatch among the series connected cells. Using the proposed SOC balancing technique, the entire battery storage system has demonstrated more capacity than the system without SOC balancing. / Ph. D.

Optimal control of energy flows in a Hybrid Electric Vehicle (HEV) is crucial to maximising the benefits of hybridisation. The problem is complex because the optimal solution depends on future power demands, which are often unknown. Stochastic Dynamic Programming (SDP) is among the most advanced control optimisation algorithms proposed and incorporates a stochastic representation of the future. The potential of a fully developed SDP controller has not yet been demonstrated on a real vehicle; this work presents what is believed to be the most concerted and complete attempt to do so. In characterising typical driving patterns of the target vehicles this work included the development and trial of an eco-driving driver assistance system; this aims to reduce fuel consumption by encouraging reduced rates of acceleration and efficient use of the gears via visual and audible feedback. Field trials were undertaken using 15 light commercial vehicles over four weeks covering a total of 39,300 km. Average fuel savings of 7.6% and up to 12% were demonstrated. Data from the trials were used to assess the degree to which various legislative test cycles represent the vehicles’ real-world use and the LA92 cycle was found to be the closest statistical match. Various practical considerations in SDP controller development are addressed such as the choice of discount factor and how charge sustaining characteristics of the policy can be examined and adjusted. These contributions are collated into a method for robust implementation of the SDP algorithm. Most reported HEV controllers neglect the significant complications resulting from extensive use of the electrical powertrain at high power, such as increased heat generation and battery stress. In this work a novel cost function incorporates the square of battery C-rate as an indicator of electric powertrain stress, with the aim of lessening the affliction of real-world concerns such as temperatures and battery health. Controllers were tested in simulation and then implemented on a test vehicle; the challenges encountered in doing so are discussed. Testing was performed on a chassis dynamometer using the LA92 test cycle and the novel cost function was found to enable the SDP algorithm to reduce electrical powertrain stress by 13% without sacrificing any fuel savings, which is likely to be beneficial to battery health.

Battery Management System (BMS) is an essential part in battery powered applications where large battery packs are in use. BMS ensures protection, controlling, supervision and accurate state estimation of battery pack to provide efficient energy management. However the particular application determines the accuracy and requirements of BMS where it has to implement; in electric vehicles (EVs) accuracy cannot be compromised. The software part of BMS estimates the states of the battery pack and takes the best possible decision. In EVs one of the key tasks of BMS’s software part is to provide the actual state of charge (SOC), which represents a crucial parameter to be determined, especially in lithium iron phosphate (LiFePO4) batteries, due to the presence of the high hysteresis behavior in the open circuit voltage than other kind of lithium batteries. This hysteresis phenomena appears with two different voltage curves during the charging and discharging process. The value of the voltage that the battery is going to assume during the off-loading operation depends on several factors, such as temperature, loop direction and ageing. In this research work, hybrid method is implemented in which advantages of several methods are achieved by implementing one technique combined with another. In this work SOC is calculated from coulomb counting method and in order to correct the error of SOC, an hysteresis model is developed and used due to presence of hysteresis effect in LiFePO4 batteries. An hysteresis model of the open circuit voltage (OCV) for a LiFePO4 cell is developed and implemented in MATLAB/Simulink© in order to reproduce the voltage response of the battery when no current from the cell is required (no load condition). Then the difference of estimated voltage and measured voltage is taken in order to correct the error of SOC calculated from coulomb counting or current integration method. To develop the hysteresis model which can reproduce the same voltage behavior, lot of experiments have been carried out practically in order to see the hysteresis voltage response and to see that how voltage curve change with the variation of temperature, ageing and loop direction. At the end model is validated with different driving profiles at different ambient temperatures.

This master’s thesis deals with the design and fabrication of DC arc welder supplied from its own rechargeable battery. Battery cells’ type is LiFePO4 which can provide high currents even at relatively low capacity. The BMS circuits are designed within the battery. Major power part of the welder is a step-down converter with synchronously switching low transistors at the position of free-wheel diode. The converter operates at 100 kHz frequency. The current of the battery welder can be regulated in the range from 0 to 120 A.

This work deals with design of battery management system. Requirements for battery management system and its conception is discussed in the first part of the work. System is able to disconnect load or charger from battery using MOS-FET transistors. It measures battery cell's voltages and is capable of passive balancing. Microcontroller is used for data processing and system control. Schematics, printed circuit board layout and control algorithm was designed. Prototype of the battery management system was then manufactured and tested.

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