The fast-growing electric vehicles (EVs) market demands huge efforts from car manufacturers to develop and improve their current products’ systems. A fast charge of the battery pack is one of the challenges encountered due to the battery limitations regarding behaviour and additional degradation when exposed to such a rough situation. In addition, the outcome of a study performed on a battery does not apply to others, especially if their chemistries are different. Hence, extensive testing is required to understand the influence of design decisions on the particular energy storage device to be implemented. Due to batteries’ nonlinear behaviour that is highly dependent on external variables such as temperature, the dynamic load and aging, another defying task is the widely studied state of charge (SOC) estimation, commonly considered one of the most significant functions in a battery management system (BMS). This thesis presents an extensive battery fast charging aging test study equipped with promising current charging profiles from published literature to minimize aging. Four charging protocols are carefully designed to charge the cell from 10 to 80% SOC within fifteen minutes and have their performances discussed. A dual state estimation algorithm is modelled to estimate the SOC with the assistance of a capacity state of health (SOHcap) estimation. Finally, the dual state estimation model is validated with the fast charging aging test data. / Thesis / Master of Science in Mechanical Engineering (MSME)

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.

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.

Battery Management System (BMS) is an essential part in battery powered applications where large battery packs are in use. BMS ensures protection, controlling, supervision and accurate state estimation of battery pack to provide efficient energy management. However the particular application determines the accuracy and requirements of BMS where it has to implement; in electric vehicles (EVs) accuracy cannot be compromised. The software part of BMS estimates the states of the battery pack and takes the best possible decision. In EVs one of the key tasks of BMS’s software part is to provide the actual state of charge (SOC), which represents a crucial parameter to be determined, especially in lithium iron phosphate (LiFePO4) batteries, due to the presence of the high hysteresis behavior in the open circuit voltage than other kind of lithium batteries. This hysteresis phenomena appears with two different voltage curves during the charging and discharging process. The value of the voltage that the battery is going to assume during the off-loading operation depends on several factors, such as temperature, loop direction and ageing. In this research work, hybrid method is implemented in which advantages of several methods are achieved by implementing one technique combined with another. In this work SOC is calculated from coulomb counting method and in order to correct the error of SOC, an hysteresis model is developed and used due to presence of hysteresis effect in LiFePO4 batteries. An hysteresis model of the open circuit voltage (OCV) for a LiFePO4 cell is developed and implemented in MATLAB/Simulink© in order to reproduce the voltage response of the battery when no current from the cell is required (no load condition). Then the difference of estimated voltage and measured voltage is taken in order to correct the error of SOC calculated from coulomb counting or current integration method. To develop the hysteresis model which can reproduce the same voltage behavior, lot of experiments have been carried out practically in order to see the hysteresis voltage response and to see that how voltage curve change with the variation of temperature, ageing and loop direction. At the end model is validated with different driving profiles at different ambient temperatures.

Lithium-ion (Li-ion) batteries have become a crucial factor in the recent electro-mobility trend. People's increased interest in electric vehicles (EVs) has motivated several automotive manufacturers and research organizations to develop suitable drivetrain designs involving batteries. Especially the development of the 48V Li-ion battery has been of great importance to reduce CO2 emissions and meet emission standards. However, accurately modeling Li-ion batteries is a difficult task since multiple factors have to be considered. Conservative Methods are using pyhsico-chemical models or electrical circuits in order to mimic the battery behavior. This thesis deals with developing a Li-ion battery model using artificial neural network (ANN) algorithms to predict the state of charge (SOC) as one of the key battery management system states. Due to the rising power of GPUs and the amount of available data, ANNs became popular in recent years. ANNs are also applicable to different areas of battery technology. Using battery data like the battery voltage, temperature, and current as input features, a neural network is trained that predicts battery SOC. A novel approach based on ANNs and one of the most commonly used SOC estimation methods are presented in this thesis to model the battery behavior. Furthermore, an approach for dealing with the highly unbalanced data by creating multidimensional bins and compare different neural network architectures for time series forecasting is introduced. By creating the model, our main priority is to reduce the model's errors in extreme operating areas of the battery. According to our results, long short-term memory (LSTM) architectures appear to be the best fit for this task. Finally, the developed ANN model can successfully learn battery behavior, however the model's accuracy under harsh operating circumstances is highly dependent on the data quality gathered.

The functionality to both charge and discharge energy from and to the power grid to a Battery Electric Vehicle (BEV) is referred to as Vehicle-to-Grid (V2G). This allows the customer to buy energy when the spot price is low and sell energy when the price is high to make a profit, called energy arbitrage. However, when the battery is charging, discharging, or idling for storage, battery degradation occurs due to chemical properties and reactions. This thesis developed a mathematical optimisation model in Python, using the modelling language Pyomo. Mathematical equations are used to integrate energy arbitrage and degradation data to reduce the total cost in terms of degradation and energy by finding an optimised charge and discharge pattern. The model allows different user scenarios to be analysed by changing inputs such as charger power, battery cost or daily driving distance. When using V2G technology, the State-of-Charge (SoC) level of BEVs battery packs can be adjusted to find SoC levels which minimise the battery degradation, while allowing the user to make a profit from energy arbitrage. The result shows that the V2G charging protocol, compared to protocols without a bidirectional charger could be beneficial for the simulated time periods, by both reducing degradation and the total energy cost. The results also indicate that the degradation cost of the battery is often the determining factor in the decision of when to charge or discharge, i.e., the substantial cost-saving strategy is to control the storage and cycle degradation to reduce the total degradation, rather than controlling the energy arbitrage. The model and the result of this thesis can be used by car manufacturers to learn more about how battery cell types behave in V2G mode and influence further work on V2G control.

The electric vehicle industry is experiencing a boom infunding and public interest, and the formula student movementis following suit; an electric race car is currently being developedby the KTH Formula Student organisation (KTHFS) which is thecause of this work.Consumers desire increased speed and range, and are unwillingto compromise one quality for the other. This necessitates the useof lithium ion cells, which may explode and exhume toxic gasesif over-strained with respect to current, charge or temperature.A robust, reliable and provably safe battery management systemshould therefore be developed. There are numerous methods tofurther increase the mileage to get an edge on competitors, suchas cell balancing and live estimation of the State of Charge(SOC). It is also vital that old and/or deteriorated cells should beidentified and disposed off in due time, and State of health (SOH)estimation provides a means to do this. In this paper a completebattery management system software solution is developed andpresented, utilising methods like simulation and code generationto create a program that runs on a real time operating system(RTOS). Some real world test were conducted and some resultsare simulated. The finished BMS performed well in tests, meets allgoals and meets all timing constraints. The project can thereforebe considered as successful. / Intresset för elbilsindustrin har på sistone‌ vuxit något markant, och formula student-rörelsen har anpassat sig efter dessa trender; en elektriskt bil tillverkas just nu av KTH Formula Student organisationen (KTHFS) vilket ger upphov till detta arbete. Marknaden vill ha snabbare bilar som dessutom har förbättrad räckvidd, men vägrar offra den ena egenskapen för det andra. Lösningen är att använda litiumjonceller. Dessa har dock en säkerhetsrelaterad nackdel; om cellerna utsätts för alldeles för höga eller låga temperaturer, strömmar eller laddningsnivåer kan de explodera och utsöndra giftig gas i luften. Därför är det lämpligt att skapa ett batterimonitoreringssystem vars funktion och säkerhet kvalitativt kan utvärderas och bevisas. Det finns flera metoder för att få förbättrad prestanda ur sin ackumulator (batteriensemble); cellnivåbalansering och laddningsnivåestimering (SOC) implementeras i detta projekt. Föråldrade/utslitna celler bör identifieras och avskrivas i god tid. Celldeklineringsestimering (SOH) är ett sätt att lösa detta problem. I denna rapport presenteras en fullständig implementation av mjukvaran för ett batterimonitoreringssystem, där metoder som kodgenerering och simulering utnyttjas för att skapa ett program som kan köras på ett realtidsoperativsystem (RTOS). Vissa test gjordes i verkligheten och vissa resultat simulerades. Det färdiga batterimonitoreringssystemet presterade väl i test, alla mål samt mötte alla tidskrav. Projektet kan därför anses som lyckat. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

[pt] Por conta dos rápidos avanços tecnológicos, percebe-se uma mudança nos hábitos e das necessidades das pessoas. Há uma dependência cada vez maior de aparelhos eletrônicos como smartphones, notebooks etc. Construir baterias com grande capacidade energética é um dos desafios atuais para aumentar a autonomia dos aparelhos eletrônicos. Entretanto, uma alternativa que pode ajudar a manter aparelhos eletrônicos por mais tempo longe das tomadas é o compartilhamento de baterias. Existem na literatura muitos estudos envolvendo o compartilhamento de baterias no contexto de veículos elétricos, porém não são encontradas aplicações em smartphones. Um parâmetro importante a ser monitorado neste contexto é o estado de saúde (SoH). Até o momento, não há um consenso na literatura acerca do melhor modelo para estimar o SoH de baterias devido à falta de métodos bem estabelecidos. Assim, o objetivo geral desta dissertação foi construir um modelo para estimar a curva de estado de saúde, por meio do estado de carga, com vistas a estimar a saúde de baterias de íon-lítio. O modelo proposto foi baseado em redes neurais recorrentes. Para treinar e validar o modelo, foi construído um sistema para a realização de ensaios destrutivos, sendo possível estudar o comportamento de baterias de íon-lítio ao longo de toda vida útil. O modelo proposto foi capaz de estimar o SoH das baterias estudadas com boa exatidão, sob diferentes parâmetros de carga/descarga. O diferencial do modelo são baixa complexidade computacional, mesmo envolvendo modelos de redes neurais, e serem adotados parâmetros de entrada de fácil medição. / [en] Because of the fast technological advances, there is a change in people s habits and needs. There is an increasing dependence on electronic devices such as smartphones, notebooks etc. Building batteries with great energy capacity is one of the current challenges to increase the autonomy of electronic devices. However, an alternative that can help keep electronic devices longer away from sockets is battery swap. There are many studies in the literature involving the sharing of batteries in the context of electric vehicles, but no applications are found in smartphones. An important parameter to be monitored in this context is state of health (SoH). To date, there is no consensus in the literature about the best model for estimating battery SoH due to the lack of well-established methods. Thus, the objective of this dissertation is to build a model to estimate the state of health curve, with a view to classifying the health of lithium-ion batteries, through state of charge curve, for applications involving battery swap aiming to use in smartphones. The proposed model was based on recurrent neural networks. To train and validate the model, a system was built to perform destructive tests, being possible to study the behavior of lithium-ion batteries throughout its useful life. The proposed model was able to estimate the SoH of the batteries studied with good precision, under different charge / discharge parameters. The distinction of the model is low computational complexity, even involving neural network models, and easy-to-measure input parameters are adopted.

With the continuing transition to renewable inherently intermittent energy sources like solar- and wind power, electrical energy storage will become progressively more important to manage energy production and demand. A key technology in this area is Li-ion batteries. To operate these batteries efficiently, there is a need for monitoring of the current battery state, including parameters such as state of charge and state of health, to ensure that adequate safety and performance is maintained. Furthermore, such monitoring is a step towards the possibility of the optimization of battery usage such as to maximize battery lifetime and/or return on investment. Unfortunately, possible online measurements during actual operation of a lithium-ion battery are typically limited to current, voltage and possibly temperature, meaning that direct measurement of battery status is not feasible. To overcome this, battery modeling and various regression methods may be used. Several of the most common regression algorithms suggested for estimation of battery state of charge and state of health are based on Kalman filtering. While these methods have shown great promise, there currently exist no thorough analysis of the impact of so-called filter tuning on the effectiveness of these algorithms in Li-ion battery monitoring applications, particularly for state of health estimation. In addition, the effects of only adjusting the cell capacity model parameter for aging effects, a relatively common approach in the literature, on overall state of health estimation accuracy is also in need of investigation. In this work, two different Kalman filtering methods intended for state of charge estimation: the extended Kalman filter and the extended adaptive Kalman filter, as well as three intended for state of health estimation: the dual extended Kalman filer, the enhanced state vector extended Kalman filer, and the single weight dual extended Kalman filer, are compared from accuracy, performance, filter tuning and practical usability standpoints. All algorithms were used with the same simple one resistor-capacitor equivalent circuit battery model. The Li-ion battery data used for battery model development and simulations of filtering algorithm performance was the “Randomized Battery Usage Data Set” obtained from the NASA Prognostics Center of Excellence.  It is found that both state of charge estimators perform similarly in terms of accuracy of state of charge estimation with regards to reference values, easily outperforming the common Coulomb counting approach in terms of precision, robustness and flexibility. The adaptive filter, while computationally more demanding, required less tuning of filter parameters relative to the extended Kalman filter to achieve comparable performance and might therefore be advantageous from a robustness and usability perspective. Amongst the state of health estimators, the enhanced state vector approach was found to be most robust to initialization and was also least taxing computationally. The single weight filter could be made to achieve comparable results with careful, if time consuming, filter tuning. The full dual extended Kalman filter has the advantage of estimating not only the cell capacity but also the internal resistance parameters. This comes at the price of slow performance and time consuming filter tuning, involving 17 parameters. It is however shown that long-term state of health estimation is superior using this approach, likely due to the online adjustment of internal resistance parameters. This allows the dual extended Kalman filter to accurately estimate the SoH over a full test representing more than a full conventional battery lifetime. The viability of only adjusting the capacity in online monitoring approaches therefore appears questionable. Overall the importance of filter tuning is found to be substantial, especially for cases of very uncertain starting battery states and characteristics.

The energy crisis and environmental pollution have become increasingly prominent in recent years. Lithium batteries have attracted extensive attention due to their high energy density, safety, and low pollution. To further study how the battery works, it is necessary to establish an accurate model conforming to the battery characteristics. As the core function of a battery management system(BMS), accurate state of charge(SoC) estimation dramatically improves battery life and performance. This thesis selects a ternary lithium battery in the centre for advanced life cycle engineering(CALCE) dataset for a study of cell modeling and SoC estimation. The second-order Thevenin equivalent circuit model is selected as the cell model due to a trade-off between model complexity and accuracy. The parameters to identify include OCV, internal ohmic resistance, polarized internal resistance and capacitance. They were obtained with the MATLAB toolbox at various SoC state points under different temperatures. The ‘terminal voltage comparison’ method is utilized to verify the identification's accuracy. The simulation results turn out to be satisfactory. Then cell SoC can be estimated after cell modeling. First, the principles of the Coulomb counting method, OCV method and EKF method are analyzed. The state space equations required in SoC estimation are determined by discretizing the non-linear equivalent circuit model. The simulation results are compared with the experimental results in the HPPC discharge experiment. Furthermore, the robustness of the EKF algorithm is further investigated. The results prove that the EKF algorithm has high precision, fast convergence speed and strong anti-interference capability. Last but not least, the research on battery pack SoC estimation was continued. How to expand a single cell into a battery pack is analyzed, including aggregating cells into a pack and scaling a cell model to a pack. In addition, battery pack SoC is individually estimated by the 'Big cell' method and 'Short board effect' method. The result is not so good, indicating that further work can be done to improve the SoC estimation accuracy. / Energikrisen och miljöföroreningarna har blivit allt mer framträdande de senaste åren. Litiumbatteri har väckt stor uppmärksamhet på grund av sin höga energitäthet, säkerhet och låga föroreningar. För att ytterligare studera hur batteriet fungerar är det nödvändigt att etablera en exakt modell som överensstämmer med batteriets egenskaper. Som kärnfunktionen hos BMS förbättrar noggrann SoC-uppskattning dramatiskt batteriets livslängd och prestanda. Denna avhandling väljer ett ternärt litiumbatteri i CALCE-datauppsättningen för forskning. Dessutom slutförs cellmodellering och SoC-uppskattning baserat på det. Den andra ordningens Thevenins ekvivalenta kretsmodell väljs som cellmodell på grund av en avvägning mellan modellens komplexitet och noggrannhet. Parametrarna som måste identifieras inkluderar OCV, intern ohmsk resistans, polariserad intern resistans och kapacitans. De erhölls med MATLAB-verktygslådan vid olika SoC-tillståndspunkter under olika temperaturer. Metoden "terminalspänningsjämförelse" används för att verifiera identifieringens noggrannhet. Simuleringsresultaten visar sig vara tillfredsställande. Sedan kan cell SoC uppskattas efter cellmodellering. Först analyseras principerna för Coulomb-räknemetoden, OCV-metoden och EKF-metoden. Tillståndsrymdsekvationerna som krävs vid SoC-uppskattning bestäms genom att diskretisera den icke-linjära ekvivalenta kretsmodellen. Simuleringsresultaten jämförs med de experimentella resultaten i HPPC-utsläppsexperimentet. Dessutom, robustheten hos EKF-algoritmen undersöks ytterligare. Resultaten bevisar att EKF-algoritmen har hög precision, snabb konvergenshastighet och stark anti-interferensförmåga. Sist men inte minst fortsatte forskningen kring SoC-uppskattning av batteripaket. Hur man expanderar ett enskilt batteri till ett batteripaket analyseras, inklusive aggregering av celler till ett paket och skalning av en cellmodell till ett paket. Dessutom uppskattas batteripaketets SoC individuellt med "Big cell"-metoden och "Short board effect"-metoden. Resultatet är inte så bra, vilket indikerar att ytterligare arbete kan göras för att förbättra SoC-uppskattningens noggrannhet.