Denna studie syftar på att undersöka klienternas, inom Öppenvård missbruk, utmaningar efter avslutad behandling. Vidare avser studien att framföra det stöd som klienterna framhåller som viktigt i en övergång till en stabil och drogfri tillvaro. För att besvara studiens syfte och frågeställningar genomfördes en kvalitativ studie med semistrukturerade intervjuer med respondenter från Öppenvård missbruk. För att analysera den insamlade datan används teorierna KASAM (Känsla av sammanhang) och systemteori för att förstå respondenternas erfarenheter och belysa vikten av en holistisk syn på återhämtning från missbruk. Resultaten ger insikt i respondenternas emotionella och sociala erfarenheter samt betonar vikten av samhörighet och gemenskap inom öppenvården. Studien understryker behovet av att inte bara behandla beroendet utan även att skapa starka sociala nätverk för långsiktig återhämtning från missbruk. Ångest inför framtiden och risken för återfall är centrala teman, kopplade till arbetslivet och behovet av balans för att undvika överprestation och stress. / This study aims to explore the challenges faced by clients in substance abuse outpatient care after completing treatment. Additionally, the study seeks to highlight the support that clients emphasize as crucial in transitioning to a stable and drug-free life. To address the research purpose and questions, a qualitative study with semi-structured interviews was conducted with respondents from substance abuse outpatient care. The collected data were analyzed using the theories of SOC (Sense of Coherence) and systems theory to understand respondents' experiences and emphasize the importance of a holistic approach to recovery from substance abuse. The results provide insight into respondents' emotional and social experiences, emphasizing the significance of solidarity and community within outpatient care. The study underscores the need not only to address addiction but also to build strong social networks for long-term recovery from substance abuse. Anxiety about the future and the risk of relapse are central themes, linked to the workplace and the need for balance to avoid overexertion and stress.

The rise in overall powertrain complexity and the stringent performance requirements of a hybrid electric vehicle (HEV) have elevated the role of its powertrain control strategy to considerable importance. Iterative modeling and simulation form an integral part of the control strategy design process and industry engineers rely on proprietary ?legacy? models to rapidly develop and implement control strategies. However, others must initiate new algorithms and models in order to develop production-capable control systems. This thesis demonstrates the development and validation of a charge-sustaining control algorithm for a through-the-road (TTR) parallel hybrid (diesel-electric) powertrain. Some unique approaches used in powertrain-level control of other commercial and prototype vehicles have been adopted to incrementally develop this control strategy. The real-time performance of the control strategy has been analyzed through on-road and chassis dynamometer tests over several standard drive cycles. Substantial quantitative improvements in the overall HEV performance over the stock configuration, including better acceleration and fuel-economy have been achieved.

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<p>Lithium-ion (Li-ion) cells are increasingly used in many applications affecting our</p> <p>daily life, such as laptops computers, cell phones, digital cameras, and other portable</p> <p>electronic devices. Lithium-ion batteries are increasingly being considered for their use in Electric Vehicles (EV), Hybrid Electric Vehicles (HEV) and Plug-in Hybrid Electric Vehicles (PHEV) due to their high energy density, slow loss of charge when not in use, and for lack of hysteresis effect. New application domains for these batteries has placed greater emphasis on their energy management, monitoring and control strategies.</p> <p>In this thesis, a comparative study between different models and state of charge (SOC) estimation strategies is performed. Battery models range from black-box representation to detailed electrochemical reaction models that consider the underlying physics. The state of charge is estimated using the Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF). The models and SOC estimation strategies are applied to experimental results from BMW Electrical and Hybrid Research and Development center and validated using a simulation model from AVL CRUISE software.</p> <p>Overall, different models and SOC estimation scenarios were studied. An average improvement of 30% in the estimation accuracy was shown by the SVSF SOC method when compared with the EKF SOC strategy. In general, the SVSF SOC estimation technique demonstrates excellent capability and a fast speed of convergence.</p> / Master of Applied Science (MASc)