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.

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.

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.

<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>