This bachelor thesis investigates what kind of battery system that is suitable for an electric driveline equipped with a mechanical fly wheel, focusing on a battery with high specific energy capacity. Basic battery theory such as the principle of an electrochemical cell, limitations and C-rate is explained as well as the different major battery systems that are available. Primary and secondary cells are discussed, including the major secondary chemistries such as lead acid, nickel cadmium (NiCd), nickel metal hydride (NiMH) and lithium ion (Li-ion). The different types of Li-ion chemistries are investigated, explained and compared against each other as well as other battery technologies. The need for more complex protection circuitry for Li-ion batteries is included in the comparison. Request for quotations are made to battery system manufacturers and evaluated. The result of the research is that the Li-ion NMC energy cell is the best alternative, even if the cost per cell is the most expensive compared to other major technologies. Due to the budget, the LiFeMnPO4 chemistry is used in the realisation of the final system, which is scaled down with consideration to the power requirement.

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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M.Tech (Department of Electronic Engineering, Faculty of Engineering and Technology), Vaal University of Technology. / Due to the fickle nature of weather upon which renewable energy sources mostly depend, a shift towards a sustainable renewable energy system should be accompanied with a good intermediate energy storage system, such as a battery bank, set up to store the excess supply from renewable sources during their peak periods. The stored energy can later be utilised to supply a regulated and steady power supply for use during the off-peak periods of these renewable energy sources. Battery banks, however, are often faced with the challenge of charge imbalance due to the disparities that occur in the operating characteristics of the batteries that constitute a bank. When a battery bank with charge imbalance is repeatedly used in applications without an effective battery management system (BMS) through active charge equalisation, there could be an early degradation, loss of efficiency and reduction of service life of the entire batteries in the bank. In this research, a universal battery management system (BMS) in stand-alone photovoltaic application was proposed and designed. The BMS consists majorly of a switched capacitor (SC) active charge equaliser, designed with a unique configuration of high capacitance and relatively low switching frequency, which can be applicable to common battery types used in stand-alone photovoltaic application. The circuit was mathematically optimised to minimise losses attributed to impulsive charging and tested with lead acid, silver calcium, lead calcium and lithium ion batteries being commonly used in stand-alone photovoltaic application. The SC design was verified by comparing its simulation results to the digital oscilloscope results, and with both results showing similar values and graphs, the design configuration was validated. The design introduced a simple control strategy and less complicated circuit configuration process, which can allow an easy setup for local usage. The benefit of its multiple usage with different stand-alone photovoltaic battery types saves the cost of purchasing a different charger and balancer for different battery types. More so, the design is solar energy dependent. This could provide an additional benefit for usage in areas where energy dependence is off-grid.

The growing interest in more electrified vehicles has been pushing the industry and academia to pursue new and more accurate ways to estimate the xEV batteries State-of-Charge (SOC). The battery system still represents one of the many technical barriers that need to be eliminated or reduced to enable the proliferation of more xEV in the market, which in turn can help reduce CO2 emissions. Battery modelling and SOC estimation of Lithium-ion batteries (Li-ion) at a wide temperature range, including negative temperatures, has been a challenge for many engineers. For SOC estimation, several models configurations and approaches were developed and tested as results of this work, including different non-recurrent neural networks, such as Feedforward deep neural networks (FNN) and recurrent neural networks based on long short-term memory recurrent neural networks (LSTM-RNN). The approaches have considerably improved the accuracy presented in the previous state-of-the-art. They have expanded the application throughout five different Li-ion at a wide temperature range, achieving error as low as 0.66% Root Mean Square Error at -10⁰C using an FNN approach and 0.90% using LSTM-RNN. Therefore, the use of deep neural networks developed in this work can increase the potential for xEV application, especially where accuracy at negative temperatures is essential. For Li-ion modelling, a cell model using LSTM-RNN (LSTM-VM) was developed for the first time to estimate the battery cell terminal voltage and is compared against a gated recurrent unit (GRU-VM) approach and a Third-order Equivalent Circuit Model based on Thevenin theorem (ECM). The models were extensively compared for different Li-ion at a wide range of temperature conditions. The LSTM-VM has shown to be more accurate than the two other benchmarks, where could achieve 43 (mV) Root Mean Square Error at -20⁰C, a third when compared to the same situation using ECM. Although the difference between LSTM-VM and GRU-VM is not that steep. Finally, throughout the work, several methods to improve robustness, accuracy and training time have been introduced, including Transfer Learning applied to the development of SOC estimation models, showing great potential to reduce the amount of data necessary to train LSTM-RNN as well as improve its accuracy. / Thesis / Doctor of Philosophy (PhD) / For electric vehicle State-of-Charge estimation, several models configurations and approaches were developed and tested as results of this work, including different non-recurrent neural networks, such as Feedforward deep neural networks (FNN) and recurrent neural networks based on long short-term memory recurrent neural networks (LSTM-RNN). The approaches have considerably improved the accuracy presented in the previous state-of-the-art. They have expanded the application throughout five different Li-ion at a wide temperature range, achieving error as low as 0.66% Root Mean Square Error at -10⁰C using an FNN approach and 0.90% using LSTM-RNN. Therefore, the use of deep neural networks developed in this work can increase the potential for xEV application, especially where accuracy at negative temperatures is essential. For Li-ion modelling, a cell model using LSTM-RNN (LSTM-VM) was developed for the first time to estimate the battery cell terminal voltage and is compared against a gated recurrent unit (GRU-VM) approach and a Third-order Equivalent Circuit Model based on Thevenin theorem (ECM). The models were extensively compared for different Li-ion at a wide range of temperature conditions. The LSTM-VM has shown to be more accurate than the two other benchmarks, where could achieve 43 (mV) Root Mean Square Error at -20⁰C, a third when compared to the same situation using ECM. Although the difference between LSTM-VM and GRU-VM is not that steep.

<p>Fast charging is crucial to the proliferation of electric vehicles. Fast charging is limited by lithium plating, which is the deposition of lithium metal on the anode surface instead of intercalation of lithium into the anode. Lithium plating causes capacity fade, increases cell resistance, and presents safety issues. A fast charging strategy was implemented using a battery management system (BMS) that avoided lithium plating by predicting the anode impedance. Commercial pouch cells modified with a reference electrode were cycled with and without the BMS. Cells cycled with the BMS avoided lithium plating but experienced significant degradation at the cathode. Cells cycled without the BMS underwent extensive lithium plating at the anode. Capacity loss was differentiated into irreversible and irretrievable capacity to understand electrode-based degradation mechanisms. Post-mortem analysis on harvested electrodes showed that the BMS cycled cells exhibited minimal anode degradation and had a two-times higher capacity loss on the cathode. The cells cycled without the BMS had extensive anode degradation caused by lithium plating and a seven-times higher capacity loss on the anode. </p> <p> </p> <p>Understanding and preventing the aging mechanisms of lithium-ion batteries is necessary to prolong battery life. Traditional full cell measurements are limited because they cannot differentiate between degradation processes that occur separately on anode and cathode. A reference electrode was inserted into commercial cylindrical lithium-ion cells to deconvolute the anode and cathode performance from the overall cell performance. Two configurations of the reference electrode placement inside the cell were tested to find a location that was stable and had minimal interference on the full cell performance. The reference electrode inside the mandrel of the cylindrical cell had stable potential measurements for 80 cycles and at different C-rates and had minimal impact on the full cell performance.<b></b></p>

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Electric vehicles have received substantial attention in the past few years since they provide a more sustainable, efficient, and greener transportation alternative in comparison to conventional fossil-fuel powered vehicles. Lithium-Ion batteries represent the most important component in the electric vehicle powertrain and thus require accurate monitoring and control. Many challenges are still facing the mass market production of electric vehicles; these challenges include battery cost, range anxiety, safety, and reliability. These challenges can be significantly mitigated by incorporating an efficient battery management system. The battery management system is responsible for estimating, in real-time, the battery state of charge, state of health, and remaining useful life in addition to communicating with other vehicle components and subsystems. In order for the battery management system to effectively perform these tasks, a high-fidelity battery model along with an accurate, robust estimation strategy must work collaboratively at various power demands, temperatures, and states of life. Lithium ion batteries are considered in this research. For these batteries, electrochemical models represent an attractive approach since they are capable of modeling lithium diffusion processes and track changes in lithium concentrations and potentials inside the electrodes and the electrolyte. Therefore, electrochemical models provide a connection to the physical reactions that occur in the battery thus favoured in state of charge and state of health estimation in comparison to other modeling techniques. The research presented in this thesis focuses on advancing the development and implementation of battery models, state of charge, and state of health estimation strategies. Most electrochemical battery models have been verified using simulation data and have rarely been experimentally applied. This is because most electrochemical battery model parameters are considered proprietary information to their manufacturers. In addition, most battery models have not accounted for battery aging and degradation over the lifetime of the vehicle using real-world driving cycles. Therefore, the first major contribution of this research is the formulation of a new battery state of charge parameterization strategy. Using this strategy, a full-set of parameters for a reduced-order electrochemical model can be estimated using real-world driving cycles while accurately calculating the state of charge. The developed electrochemical model-based state of charge parameterization strategy depends on a number of spherical shells (model states) in conjunction with the final value theorem. The final value theorem is applied in order to calculate the initial values of lithium concentrations at various shells of the electrode. Then, this value is used in setting up constraints for the optimizer in order to achieve accurate state of charge estimation. Developed battery models at various battery states of life can be utilized in a real-time battery management system. Based on the developed models, estimation of the battery critical surface charge using a relatively new estimation strategy known as the Smooth Variable Structure Filter has been effectively applied. The technique has been extended to estimate the state of charge for aged batteries in addition to healthy ones. In addition, the thesis introduces a new battery aging model based on electrochemistry. The model is capable of capturing battery degradation by varying the effective electrode volume, open circuit potential-state of charge relationship, diffusion coefficients, and solid-electrolyte interface resistance. Extensive experiments for a range of aging scenarios have been carried out over a period of 12 months to emulate the entire life of the battery. The applications of the proposed parameterization method combined with experimental aging results significantly improve the reduced-order electrochemical model to adapt to various battery states of life. Furthermore, online and offline battery model parameters identification and state of charge estimation at various states of life has been implemented. A technique for tracking changes in the battery OCV-R-RC model parameters as battery ages in addition to estimation of the battery SOC using the relatively new Smooth Variable Structure Filter is presented. The strategy has been validated at both healthy and aged battery states of life using driving scenarios of an average North-American driver. Furthermore, online estimation of the battery model parameters using square-root recursive least square (SR-RLS) with forgetting factor methodology is conducted. Based on the estimated model parameters, estimation of the battery state of charge using regressed-voltage-based estimation strategy at various states of life is applied. The developed models provide a mechanism for combining the standalone estimation strategy that provide terminal voltage, state of charge, and state of health estimates based on one model to incorporate these different aspects at various battery states of life. Accordingly, a new model-based estimation strategy known as the interacting multiple model (IMM) method has been applied by utilizing multiple models at various states of life. The method is able to improve the state of charge estimation accuracy and stability, when compared with the most commonly used strategy. This research results in a number of novel contributions, and significantly advances the development of robust strategies that can be effectively applied in real-time on-board of a battery management system. / Thesis / Doctor of Philosophy (PhD)

A continuous increase in demands from the utility grid and traction applications have steered public attention toward the integration of energy storage (ES) and hybrid ES (HESS) solutions. Modern technologies are no longer limited to batteries, but can include supercapacitors (SC) and flywheel electromechanical ES well. However, insufficient control and algorithms to monitor these devices can result in a wide range of operational issues. A modern day control platform must have a deep understanding of the source. In this dissertation, specialized modular Energy Storage Management Controllers (ESMC) were developed to interface with a variety of ES devices. The EMSC provides the capability to individually monitor and control a wide range of different ES, enabling the extraction of an ES module within a series array to charge or conduct maintenance, while remaining storage can still function to serve a demand. Enhancements and testing of the ESMC are explored in not only interfacing of multiple ES and HESS, but also as a platform to improve management algorithms. There is an imperative need to provide a bridge between the depth of the electrochemical physics of the battery and the power engineering sector, a feat which was accomplished over the course of this work. First, the ESMC was tested on a lead acid battery array to verify its capabilities. Next, physics-based models of lead acid and lithium ion batteries lead to the improvement of both online battery management and established multiple metrics to assess their lifetime, or state of health. Three unique HESS were then tested and evaluated for different applications and purposes. First, a hybrid battery and SC HESS was designed and tested for shipboard power systems. Next, a lithium ion battery and SC HESS was utilized for an electric vehicle application, with the goal to reduce cycling on the battery. Finally, a lead acid battery and flywheel ES HESS was analyzed for how the inclusion of a battery can provide a dramatic improvement in the power quality versus flywheel ES alone.