This thesis describes the development of a simplified model of a battery energy storage. The battery energy storage is part of the ABB energy storage system DynaPeaQ®. The model has been built to be run in RTDS, a real time digital simulator. Batteries can be represented by equivalent electric circuits, built up of e.g voltage sources and resistances. The magnitude of the components in an equivalent circuit varies with a number of parameters, e.g. state of charge of the battery and current flow through the battery. In order to get a model of how the resistive behaviour of the batteries is influenced by various parameters, a number of simulations have been run on a Matlab/Simulink model provided by the battery manufacturer. This model is implemented as a black box with certain inputs and outputs, and simulates the battery behaviour. From the simulation results a set of equations have been derived, which approximately give the battery resistance under different operational conditions. The equations have been integrated in the RTDS model, together with a number of controls to calculate e.g. state of charge of the batteries and battery temperature. Results from the RTDS model have been compared with results from the Simulink model. The results coincide reasonably well for the conditions tested. However, further testing is needed to ensure that the RTDS model produces results similar enough to the ones from the Simulink model, over the entire operational range.

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.

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Switching from fossil-fuel-powered vehicles to electric vehicles has become an international focus in the pursuit of combatting climate change. Regardless, the adoption of electric vehicles has been slow, in part, due to range anxiety. One solution to mitigating range anxiety is to provide a more accurate state of charge (SOC) and range estimation. SOC estimation of lithium-ion batteries for electric vehicle application is a well-researched topic, yet minimal tools and code exist online for researchers and students alike. To that end, a publicly available Kalman filter-based SOC estimation function is presented. The MATLAB function utilizes a second-order resistor-capacitor equivalent circuit model. It requires the SOC-OCV (open circuit voltage) curve, internal resistance, and equivalent circuit model battery parameters. Users can use an extended Kalman filter (EKF) or adaptive extended Kalman filter (AEKF) algorithm and temperature-dependent battery data. A practical example is illustrated using the LA92 driving cycle of a Turnigy battery at multiple temperatures ranging from -10C to 40C. Current range estimation methods suffer from inaccuracy as factors including temperature, wind, driver behaviour, battery voltage, current, SOC, route/terrain, and much more make it difficult to model accurately. One of the most critical factors in range estimation is the battery. However, most models thus far are represented using equivalent circuit models as they are more widely researched. Another limitation is that any machine learning-based range estimation is typically based on historical driving data that require odometer readings for training. A range estimation algorithm using a machine learning-based voltage estimation model is presented. Specifically, the long short-term memory cell in a recurrent neural network is used for the battery model. The model is trained with two datasets, classic and whole, from the experimental data of four Tesla/Panasonic 2170 battery cells. All network training is completed on SHARCNET, a resource provided by Canada Compute to researchers. The classically trained network achieved an average root mean squared error (RMSE) of 44 mV compared to 34 mV achieved by the network trained on the whole dataset. Based on the whole dataset, all test cases achieve an end range estimation of less than 5 km with an average of 0.29 km. / Thesis / Master of Applied Science (MASc)

Advancements in batteries, microprocessors as well as an extra emphasis being put on the environment has pushed electric vehicles to the forefront of today. Despite the many benefits of electric vehicles, range anxiety and long charge times are hurdles to overcome. These shortfalls are a result of the current battery technology regardless of the many breakthroughs over the last decade. Lithium-ion Batteries and other modern chemistries pose a number of challenges in testing and research when compared to the traditional lead acid batteries. Current test systems fall short in providing a complete testing solution with. The focus of this thesis is to develop a complete software framework for battery characterization: testing, modelling and characterization to accompany battery testing hardware developed by D&V Electronics. The first step in battery characterization, involves battery testing in order to obtain data. This required development of the test software and a number of battery tests, including: Charge and discharge, state of charge vs. open circuit voltage curve generation, Electro-Impedance Spectroscopy, and capacity test. Research was done in order to ensure developed test procedures lined up with that of other publications. All data from the testing data is logged to a central database, allowing for the second major development, the model framework. The model framework is composed of seven different battery models that can be parameterized with the touch of a button, using data collected from the tester. It is a software framework that is meant to be expandable by abstracting the details of a model from the tester. This allows for new models and parameterization techniques to be integrated into the software without the need of new software development. Lastly, all development was used to do a battery characterization of a prismatic battery cell. All tests were conducted on a battery over two hundred cycles, followed by battery parameterization using the mode framework. The battery models were then used to simulate a US06 drive profile and compared to the same profile with measurements taken from the tester. With an average root mean square error of 8 millivolts, the battery characterization using the framework proved to be a success. / Thesis / Master of Applied Science (MASc)

This dissertation focuses on the introduction and development of an integrated model-based design and optimization platform to solve the optimal design and optimal control, or hardware and software co-design, problem for hybrid electric propulsion systems. Specifically, the hybrid and plug-in hybrid electric powertrain systems with diesel and natural gas (NG) fueled compression ignition (CI) engines and large Li-ion battery energy storage system (ESS) for propelling a hybrid electric marine vessel are investigated. The combined design and control optimization of the hybrid propulsion system is formulated as a bi-level, nested optimization problem. The lower-level optimization applies dynamic programming (DP) to ensure optimal energy management for each feasible powertrain system design, and the upper-level global optimization aims at identifying the optimal sizes of key powertrain components for the powertrain system with optimized control. Recently, Li-ion batteries became a promising ESS technology for electrified transportation applications. However, these costly Li-ion battery ESSs contribute to a large portion of the powertrain electrification and hybridization costs and suffer a much shorter lifetime compared to other key powertrain components. Different battery performance modelling methods are reviewed to identify the appropriate degradation prediction approach. Using this approach and a large set of experimental data, the performance degradation and life prediction model of LiFePO4 type battery has been developed and validated. This model serves as the foundation for determining the optimal size of battery ESS and for optimal energy management in powertrain system control to achieve balanced reduction of fuel consumption and the extension of battery lifetime. In modelling and design of different hybrid electric marine propulsion systems, the life cycle cost (LCC) model of the cleaner, hybrid propulsion systems is introduced, considering the investment, replacement and operational costs of their major contributors. The costs of liquefied NG (LNG), diesel and electricity in the LCC model are collected from various sources, with a focus on present industrial price in British Columbia, Canada. The greenhouse gas (GHG) and criteria air pollutant (CAP) emissions from traditional diesel and cleaner NG-fueled engines with conventional and optimized hybrid electric powertrains are also evaluated. To solve the computational expensive nested optimization problem, a surrogate model-based (or metamodel-based) global optimization method is used. This advanced global optimization search algorithm uses the optimized Latin hypercube sampling (OLHS) to form the Kriging model and uses expected improvement (EI) online sampling criterion to refine the model to guide the search of global optimum through a much-reduced number of sample data points from the computationally intensive objective function. Solutions from the combined hybrid propulsion system design and control optimization are presented and discussed. This research has further improved the methodology of model-based design and optimization of hybrid electric marine propulsion systems to solve complicated co-design problems through more efficient approaches, and demonstrated the feasibility and benefits of the new methods through their applications to tugboat propulsion system design and control developments. The resulting hybrid propulsion system with NG engine and Li-ion battery ESS presents a more economical and environmentally friendly propulsion system design of the tugboat. This research has further improved the methodology of model-based design and optimization of hybrid electric marine propulsion systems to solve complicated co-design problems through more efficient approaches, and demonstrated the feasibility and benefits of the new methods through their applications to tugboat propulsion system design and control developments. Other main contributions include incorporating the battery performance degradation model to the powertrain size optimization and optimal energy management; performing a systematic design and optimization considering LCC of diesel and NG engines in the hybrid electric powertrains; and developing an effective method for the computational intensive powertrain co-design problem. / Graduate