An accurate estimation of the charging time of an automotive traction battery is possible only with the knowledge of different parameters of the battery and the vehicle. If this information is not available to the driver, the full time needed for charging of the battery may have to be assessed only from experience. A long route planning and estimation of required service life of the vehicle are therefore only roughly possible. Furthermore, with a better knowledge of estimated charging time, better management of public charging stations and better utilization of charging equipment can be achieved. An algorithm based on Matlab/Simulink model is made in the present thesis to estimate the charging time of a Li-ion battery pack which consists of 32 cells with 40 Ah each, as well as to investigate the impact of different cell balancing methods and different charging strategies on charging process. The theoretical background of the battery and charging modelling is investigated and different battery models are compared to get the best trade-off between the model accuracy and computation complexity. In the end, an electrical equivalent circuit model from reference [1], consists of a series resistor and two ZARC elements, is chosen to represent the battery cell. The parameters of the equivalent circuit are updated according to the SOC, current and temperature changes during the charging process. The whole simulation model of the algorithm consists of a charging controller (implementing the charging strategy), cell balancing logic controller, and cell balancing hardware simulation circuit and battery cell models. Different balancing criteria: based on SOC (with PWM drive) and based on terminal voltage (with/without advance) are implemented in the cell balancing logic controller, as well as different balancing windows, to investigate their impact on charging time. As for charging strategy, traditional CCCV is investigated, further investigation is conducted into improved CCCV method. The impact of initial SOC, charging rate and aging factor on charging behavior are investigated as well. Experiment results are validated by the comparison of the results with the ones got from a Hardware-in-the-loop simulation system. / En noggrann estimering av laddtiden hos batterier avsedda för traktionsapplikationer kräver kunskap kring batteriets och dess tillhörande laddsystems parametervärden. Utan tillgång till denna information kan laddtiden endast uppskattas från fordonsägarens tidigare erfarenheter vilket försvårar t.ex. ruttplanering. En estimering av laddtiden med tillräcklig noggrannhet kan även möjliggöra bättre utnyttjade av laddutrusting inklusive nyttjandet av publika laddstationer. I detta examensarbete har en algoritm, implementerad i Matlab/Simulink, för att estimera laddtiden hos ett litiumjonbatteripack bestående av 32 celler på vardera 40 Ah tagits fram. Med hjälp av modellen har olika laddstrategier och metoder för att balansera cellerna studerats. Ett antal olika batterimodeller har jämförts i termer av noggrannhet och krav på beräkningsprestanda. En elektriskt ekvivalent krets från referens [1], bestående av en serieresistans samt två ZARC-element, valdes slutligen för att representera battericellen. Den ekvivalenta kretsens parametrar uppdateras vid förändringar i SOC, ström och temperatur. Hela simuleringsmodellen består av en laddregulator (i vilken laddstrategin är implementerad), cellbalanseringregulator och modeller för cell och cellbalanseringens hårdvara. Ett antal metoder för att balanser cellerna har jämförts med hänsyn till påverkan på den resulterande laddtiden. En traditionell samt modifierad CCCV laddstrategi har implementerats och jämförts med avseende på variationer i inledande SOC, total laddtid samt åldring. Experimentella resultat från en hardware-in-the-loop simulering har använts för att delvis kunna verifiera de framtagna resultaten.

Energy storage systems with many Lithium Ion battery cells per string require sophisticated balancing hardware due to individual cells having manufacturing inconsistencies, different self discharge rates, internal resistances and temperature variations. For capacity maximization, safe operation, and extended lifetime, battery balancing is required. Redistributive Non-Dissipative balancing further improves the pack capacity and efficiency over a Dissipative approach where energy is wasted as heat across shunt resistors. Redistribution techniques dynamically shuttle charge to and from weak cells during operation such that all of the stored energy in the stack is utilized. This thesis identifies and develops different balancing control methods. These methods include a unconstrained optimization problem using a Linear Quadratic Regulator (LQR) and a constrained optimization problem using Model Predictive Control (MPC). These methods are benchmarked against traditional rule based (RB) balancing. The control systems are developed using MATLAB/Simulink and validated experimentally on a multiple transformer individual cell to stack topology. The implementation uses a DC2100A Demo-board from Linear Technology with bi-directional flyback converters to transfer the energy between the cells. The results of this thesis show that the MPC control method has the highest balancing efficiency and minimum balancing time. / Thesis / Master of Applied Science (MASc)