In battery management systems, the main figure of merit is the battery's SOC, typically obtained from voltage and current measurements. Present estimation methods use simplified battery models that do not fully capture the electrical characteristics of the battery, which are useful for system design. This thesis studied SOC estimation for a lithium-ion battery using a nonlinear, electrical-circuit battery model that better describes the electrical characteristics of the battery. The extended Kalman filter, unscented Kalman filter, third-order and fifth-order cubature Kalman filter, and the statistically linearized filter were tested on their ability to estimate the SOC through numerical simulation. Their performances were compared based on their root-mean-square error over one hundred Monte Carlo runs as well as the time they took to complete those runs. The results show that the extended Kalman filter is a good choice for estimating the SOC of a lithium-ion battery.

Optimal control of energy flows in a Hybrid Electric Vehicle (HEV) is crucial to maximising the benefits of hybridisation. The problem is complex because the optimal solution depends on future power demands, which are often unknown. Stochastic Dynamic Programming (SDP) is among the most advanced control optimisation algorithms proposed and incorporates a stochastic representation of the future. The potential of a fully developed SDP controller has not yet been demonstrated on a real vehicle; this work presents what is believed to be the most concerted and complete attempt to do so. In characterising typical driving patterns of the target vehicles this work included the development and trial of an eco-driving driver assistance system; this aims to reduce fuel consumption by encouraging reduced rates of acceleration and efficient use of the gears via visual and audible feedback. Field trials were undertaken using 15 light commercial vehicles over four weeks covering a total of 39,300 km. Average fuel savings of 7.6% and up to 12% were demonstrated. Data from the trials were used to assess the degree to which various legislative test cycles represent the vehicles’ real-world use and the LA92 cycle was found to be the closest statistical match. Various practical considerations in SDP controller development are addressed such as the choice of discount factor and how charge sustaining characteristics of the policy can be examined and adjusted. These contributions are collated into a method for robust implementation of the SDP algorithm. Most reported HEV controllers neglect the significant complications resulting from extensive use of the electrical powertrain at high power, such as increased heat generation and battery stress. In this work a novel cost function incorporates the square of battery C-rate as an indicator of electric powertrain stress, with the aim of lessening the affliction of real-world concerns such as temperatures and battery health. Controllers were tested in simulation and then implemented on a test vehicle; the challenges encountered in doing so are discussed. Testing was performed on a chassis dynamometer using the LA92 test cycle and the novel cost function was found to enable the SDP algorithm to reduce electrical powertrain stress by 13% without sacrificing any fuel savings, which is likely to be beneficial to battery health.

Data-driven prediction of battery health has gained increased attention over the past couple of years, in both academia and industry. Accurate early-stage predictions of battery performance would create new opportunities regarding production and use. Using data from only the first 100 cycles, in a data set of 124 cells where lifetimes span between 150 and 2300 cycles, this work combines parametric linear models with non-parametric Gaussian process regression to achieve cycle lifetime predictions with an overall accuracy of 8.8% mean error. This work presents a relevant contribution to current research as this combination of methods is previously unseen when regressing battery lifetime on a high dimensional feature space. The study and the results presented further show that Gaussian process regression can serve as a valuable contributor in future data-driven implementations of battery health predictions. / Datadriven prediktion av batterihälsa har fått ökad uppmärksamhet under de senaste åren, både inom akademin och industrin. Precisa prediktioner i tidigt stadium av batteriprestanda skulle kunna skapa nya möjligheter för produktion och användning. Genom att använda data från endast de första 100 cyklerna, i en datamängd med 124 celler där livslängden sträcker sig mellan 150 och 2300 cykler, kombinerar denna uppsats parametriska linjära modeller med ickeparametrisk Gaussisk processregression för att uppnå livstidsprediktioner med en genomsnittlig noggrannhet om 8.8% fel. Studien utgör ett relevant bidrag till den aktuella forskningen eftersom den använda kombinationen av metoder inte tidigare utnyttjats för regression av batterilivslängd med ett högdimensionellt variabelrum. Studien och de erhållna resultaten visar att regression med hjälp av Gaussiska processer kan bidra i framtida datadrivna implementeringar av prediktion för batterihälsa.

Hlavní cílem dizertace je poskytnutí uceleného pohledu na problematiku technické prognostiky, která nachází uplatnění v tzv. prediktivní údržbě založené na trvalém monitorování zařízení a odhadu úrovně degradace systému či jeho zbývající životnosti a to zejména v oblasti komplexních zařízení a strojů. V současnosti je technická diagnostika poměrně dobře zmapovaná a reálně nasazená na rozdíl od technické prognostiky, která je stále rozvíjejícím se oborem, který ovšem postrádá větší množství reálných aplikaci a navíc ne všechny metody jsou dostatečně přesné a aplikovatelné pro embedded systémy. Dizertační práce přináší přehled základních metod použitelných pro účely predikce zbývající užitné životnosti, jsou zde popsány metriky pomocí, kterých je možné jednotlivé přístupy porovnávat ať už z pohledu přesnosti, ale také i z pohledu výpočetní náročnosti. Jedno z dizertačních jader tvoří doporučení a postup pro výběr vhodné prognostické metody s ohledem na prognostická kritéria. Dalším dizertačním jádrem je představení tzv. částicového filtrovaní (particle filtering) vhodné pro model-based prognostiku s ověřením jejich implementace a porovnáním. Hlavní dizertační jádro reprezentuje případovou studii pro velmi aktuální téma prognostiky Li-Ion baterii s ohledem na trvalé monitorování. Případová studie demonstruje proces prognostiky založené na modelu a srovnává možné přístupy jednak pro odhad doby před vybitím baterie, ale také sleduje možné vlivy na degradaci baterie. Součástí práce je základní ověření modelu Li-Ion baterie a návrh prognostického procesu.