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Electric Vehicles (EVs) are seen as one of the solutions to some of the world’s global current problems, such as global warming and air pollution, due to non-existent operational emissions and increased efficiency. There are multiple types of EVs, one of them being Battery Electric Vehicles (BEVs) which in most cases utilizes a Li-Ion battery pack as energy storage. Li-Ion batteries for EV applications are deemed to have reached End of Life (EoL) at 80%-70% of the battery’s initial energy capacity, the degradation is caused by calendar and cycle aging. Calendar aging is dependent on storage temperature and State of Charge (SoC), controlling these factors are therefore of importance when storing battery packs to minimize aging. Diagnostics of battery packs is also useful for determining second-life applications, State of Health (SoH) or other future usage. AFRY has therefore an interest in developing a product that would enable diagnostics and charging/discharging of off-vehicle battery packs, as regular chargers and tools are not compatible with battery packs separated from its vehicle. This thesis is a part of a project to develop a product that enables diagnostics and charging/discharging for different types off-vehicle battery packs. Due to time limitation of the thesis work this report focuses on the hardware design of the Power Conversion Module (PCM) from a charging/discharging perspective. This thesis project will propose a theoretical PCM from received requirements and choose appropriate components for this subsystemfrom a charging/discharging perspective. The design requirements were gathered through interviews, archive analysis and literature studies and argued for. This to achieve an understanding of the requirements that the system design needs to fulfill. A system design proposal of the PCM was presented and a component analysis of the included components was conducted. The proposed solution should in theory enable charging/discharging of battery packs, but further work and studies needs to be performed to validate the results in practice. Some calculations and variables were inferredwith the help of discussions, due to lack of information and time. The goal of the thesis was fulfilled, and the wider project objective was partially fulfilled within the boundaries of this thesis project.

Lithium-ion (Li-ion) batteries are amongst the most commonly used types in Electric (EVs) and Hybrid Electric (HEVs) Vehicles due to their high energy and power densities, as well as long lifetime. A battery is one of the most important components of an EV and hence it needs to be monitored and controlled accurately. The safety, and reliability of battery packs must then, be ensured by accurate management, control, and monitoring functions by using a Battery Management System (BMS). A BMS is also responsible for accurate real-time estimation of the State of Charge (SoC), State of Health (SoH) and State of Power (SoP) of the battery. The battery SoC provides information on the amount of energy left in the battery. The SoH determines the remaining capacity and health of a pack, and the SoP represents the maximum available power. These critical battery states cannot be directly measured. Therefore, they have to be inferred from measurable parameters such as the current delivered by the battery as well as its terminal voltage. Consequently, in order to offer accurate monitoring of SoC, SoH and SoP, advanced numerical estimation methods need to be deployed. In the estimation process, the states and parameters of a system are extracted from measurements. The objective is to reduce the estimation errors in the presence of uncertainties and noise under different operating conditions. This thesis uses and provides different enhancements to a robust estimation strategy referred to as the Smooth Variable Structure Filter (SVSF) for condition monitoring of batteries. The SVSF is a predictor-corrector method based on sliding mode control that enhances the robustness in the presence of noise and uncertainties. The methods are proposed to provide accurate estimates of the battery states of operation and can be implemented in real-time in BMS. To improve the performance of battery condition monitoring, a measurement-based SoC estimation method called coulomb counting is paired with model-based state estimation strategy. Important considerations in parameter and state estimation are model formulation and observability. In this research, a new model formulation that treats coulomb counting as an added measurement is proposed. It is shown that this formulation enhanced information extraction, leading to a more accurate state estimation, as well as an increase in the number of parameters and variables that can be estimated while maintaining observability. This model formulation is used for characterizing the battery in a range of operating conditions. In turn, the models are integral to a proposed adaptive filter that is a combination of the Interacting Multiple Model (IMM) concept and the SVSF. It is shown that this combined strategy is an efficient estimation approach that can effectively deal with battery aging. The proposed method provides accurate estimation for various SoH of a battery. Further to battery aging adaptation, measurement errors such as sensor noise, drift, and bias that affect estimation performance, are considered. To improve the accuracy of battery state estimation, a noise covariance adaptation scheme is developed for the SVSF method. This strategy further improves the robustness of the SVSF in the presence of unknown physical disturbances, noise, and initial conditions. The proposed estimation strategies are also considered for their implementation on battery packs. An important consideration in pack level battery management is cell-to-cell variations that impact battery safety. This study considers online battery parametrization to update the pack’s model over time and to detect cell-to-cell variability in parallel-connected battery cells configurations. Experimental data are used to validate and test the efficacy of the proposed methods in this thesis. / Thesis / Doctor of Philosophy (PhD) / To address the critical issue of climate change, it is necessary to replace fossil-fuel vehicles with battery-powered electric vehicles. Despite the benefits of electric vehicles, their popularity is still limited by the range anxiety and the cost determined by the battery pack. The range of an electric vehicle is determined by the amount of charge in its battery pack. This is comparable to the amount of gasoline in a gasoline vehicle’s tank. In consideration of the need for methods to address range anxiety, it is necessary to develop advanced algorithms for continuous monitoring and control of a battery pack to maximize its performance. However, the amount of charge and health of a battery pack cannot be measured directly and must be inferred from measurable variables including current, voltage and temperature. This research presents several algorithms for detecting the range and health of a battery pack under a variety of operating conditions. With a more accurate algorithm, a battery pack can be monitored closely, resulting in lower long-term costs. Adaptive methods for determining a battery’s state of charge and health in uncertain and noisy conditions have been developed to provide an accurate measure of available charge and capacity. Methods are then extended to improve the determination of state of charge and health for a battery module.

As the industry is being reshaped concentrically around sustainability, the consumption of fossil fuels is targeted to decrease day by day. As a consequence of that, a righteous rise of electricity as energy source prevails in different branches of industry. It results for electric vehicle components to increase in size and weight which in fact were relatively smaller on conventional fossil fuel driven vehicles e.g. trucks. Every component weighing more than two kilograms is classified as a large component by Scania and these large electric components uncovers the need of evaluation of established testing methods in terms of their validity. This thesis project was carried out at Scania, Södertälje, and deals with the investigation on vibration testing methods that are currently used in different fields of engineering and identification of potentially applicable ones at Scania in testing large components. A case study on the stiffness of an individual component is performed to assess its impact on alteration of eigenfrequencies. Few case studies based on empirical tests as well as finite element method simulations on certain large components, i.e. high voltage junction box and battery packs, are performed with respect to their vibration behaviours while undergoing Scania’s established vibration testing spectra. Investigation into the vibration behaviour on dependence on measurement locations were performed. Additionally, rudimentary case studies are conducted on thermal loads during vibration testing, thermal dwell time, and required energy to oscillate large components at certain levels. How thermal features of the component are affected due to its size is noted. / Den senaste tekniska paradigmen inom fordonsindustrin är fokus på minskning av beroende på fossila bränslen, högre energisnålhet och en ökad hållbarhet och effektivitet i produktionsprocessens alla delar. Detta och de stora genombrotten i batteritekniken hade som påföljd att utvecklingen av fordon riktades mot elektrifiering av alla fordon. Elmotorer har sedan tidigare funnit sin tillämpning på mindre maskiner i inomhusmiljöer som t.ex. palldragare och lasttruckar. För tyngre applikationer som t.ex. personbilar och lastbilar ställs det helt nya krav på komponenternas storlek och prestanda, vilket leder till de elektriska komponenternas högre vikt. Scania, som vill vara ledande inom denna omställning, klassificerar alla komponenter med större vikt än 2 kg som stora komponenter. De stora elektriska komponenterna skapar ett behov att utvärdera de aktuella testmetoderna och verifiera deras validitet. Det här magisterexamensarbetet har genomförts på Scania i Södertälje och handlar om undersökning av vibrationstestningsmetoderna som nuförtiden används i olika teknikområden samt identifiering av de eventuellt applicerbara metoderna för provning av stora elektriska komponenter. En fallstudie genomfördes gällande en individuell komponent och påverkan på dess egenfrekvenser med varierande styvhetskonstanter. Dessutom genomfördes några andra fallstudier utgående från empiriska tester samt simuleringar med hjälp av finita elementmetoden på vissa stora komponenter såsom högspänningsförgreningsdosa och batteripacken. Vibrationsprov genomfördes genom att utsätta komponenterna för Scanias nuvarande vibrationstestningsspektrum. Även vibrationsbeteendet beroende på positionen av mättningspunkterna har undersökts. Utöver det utfördes fallstudier angående termiska belastningar under vibrationstestning, tid för värmeöverföring samt energibehovet för att oscillera stora komponenter på förbestämda accelerationsnivåer. Inverkan av komponenternas storlek på deras värmeöverföringsegenskaper noterades.

Various potential methods and techniques for modifying surfaces to make themless sensitive to contamination have been researched for various applicationsbefore, to varying degrees of success. These are discussed in detail to potentiallyapply them in the automotive sector, to face the increasing technical cleanlinessrequirements, that are linked to more complexity in design and electrification. TheTheoretical background in dust adhesion and contact mechanics is discussed, withdevelopments in analytical and numerical methods highlighted. Then, someexamples of the surfaces in question are presented, and their topographiesmeasured. Potential techniques are identified from previous experimental research in otherfields, and their applicability and feasibility are discussed within the limits ofavailable data, then an attempt at a structured approach for choosing candidatesfor further experimental testing on a case by case basis is laid out, and suggestionsfor more comprehensive research into effective parameters and analytical methodsare made.