Fast charging of electric vehicles remains a compromise between charging time and degradation penalty. Conventional battery management systems use experience-based charging protocols that are expected to meet vehicle lifetime goals. Novel electrochemical model-based battery fast charging uses a model to observe internal battery states. This enables control of charging rates based on states such as the lithium-plating potential but relies on an accurate model as well as accurate model parameters. However, the impact of battery degradation on the model's accuracy and therefore the fitness of the estimated optimal charging procedure is often not considered. In this work, we therefore investigate electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells. First, an electrochemical model is identified at the beginning of life for 6 automotive prototype cells and the electrochemically constrained fast-charge is designed. The model parameters are then periodically re-evaluated during a cycling study and the charging procedure is updated to account for cell degradation. The proposed method is compared with two reference protocols to investigate both the effectiveness of selected electrochemical constraints as well as the benefit of aging-adaptive usage. Finally, post-mortem characterization is presented to highlight the benefit of aging-adaptive battery utilization.

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging-sensitive battery management is necessary. This refers to a management strategy that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application. 

In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery since it reflects the changing properties of an aging battery. This thesis investigates identification of electrochemical and empirical battery models from operational EV data. In addition, it studies model-based strategies for optimal and adaptive fast charging. The work is divided into four main studies.

1) Empirical linear-parameter-varying (LPV) dynamic models were identified on driving data. Model parameters were formulated as functions of the measured temperature, current magnitude and estimated open circuit voltage (OCV). To handle the time-scale differences in battery voltage response, continuous-time system identification was employed. We concluded that the proposed models had superior predictive abilities compared to discrete and time-invariant counterparts.

2) A global sensitivity analysis was performed on the parameters of a high-order electrochemical model. Measured current profiles from real EVs were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the data set from an electric truck with few battery packs. Data sets from vehicles with more packs (electric bus) and limited SOC operating window (plug-in hybrid truck) excited fewer model parameters.

3) Instead of using driving data to parametrize models, we also investigated the possibility to design the charging current in order to increase its information content about model parameters. This was formulated as an optimal experiment design problem in frequency domain. An aging-sensitive fast-charge procedure was optimized based on equivalent circuit model (ECM) states. Finally, different methods for combining the optimal fast charge and the optimal experiment were evaluated with regard to the resulting charging time and model performance.  

4) Finally, aging-adaptive fast charging of automotive lithium-ion cells was studied. An electrochemical model was identified at the beginning of life and an electrochemically constrained fast charge was designed. The model parameters were then periodically re-evaluated during a cycling study and the charging procedure was updated to account for cell degradation. The study showed that adaptation of charge protocols increased the cell utilization compared to static protocols, but that heterogeneous degradation reduced the validity of the model and the adherence to electrochemical constraints.

This paper presents informative battery charging, a novel approach for battery model parameter estimation during fast charge. Our solution comprises three distinct contributions: first, we develop a semi-explicit solution to an optimal fast charging problem for equivalent circuit models with health-conscious voltage constraints; second, we design optimal experiments for battery model parameter estimation; and third, we suggest a strategy for how the fast charging and experimentation currents can be combined while still satisfying constraints and maintaining acceptable charging times. Numerical results show that model parameters can be identified with lower variance if an optimal experiment is added to the charging procedure.

Li-ion batteries in electric vehicles must be utilized more efficiently to lower their economic and environmental cost. To achieve this increase in efficiency, it is of large interest to develop more thorough battery management that can predict internal states in online settings and update usage and control accordingly. Electrochemical models are an important tool in achieving this, and their implementation in battery management systems is the topic of ongoing research. However, electrochemical battery management relies on accurate parametrization and thus requires re-parametrization as a battery ages. We therefore studied viability of re-parametrization for electrochemical model-based battery management. To this end, we performed global sensitivity analysis on selected Doyle-Fuller-Newman model parameters using on-board current measurements. Representative driving data was collected from several types of heavy-duty vehicles. This elucidated which model parameters should be updated periodically to conserve model accuracy and which parameters are sensitive enough to be estimated from the on-board data. Additionally, we studied how parameter uncertainty affects estimation of internal states and highlight how model-based state estimation relying on a beginning-of-life parametrization degrades as electrochemical parameters change with aging.

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application. 

In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies.

1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the data set from an electric truck with few battery packs. Data sets from vehicles with more packs (electric bus) and limited SOC operating window (plug-in hybrid truck) excited fewer model parameters.

2) Empirical linear-parameter-varying (LPV) dynamic models were identified on driving data. Model parameters were formulated as functions of the measured temperature, current magnitude and estimated open circuit voltage (OCV). To handle the time-scale differences in battery voltage response, continuous-time system identification was employed. We concluded that the proposed models had superior predictive abilities compared to discrete and time-invariant counterparts. 

3) Instead of using driving data to parametrize models, we also investigated the possibility to design the charging current in order to increase its information content about model parameters. This was formulated as an optimal control problem with charging speed and information content as objectives. To also take battery degradation into account, constraints on polarization was included. The results showed that parameter information can be increased without significant increase in charge time nor aging related stress.

Elekriska fordon utgör en allt större andel av världens fordonsflotta. Batteriet är en komponent med betydande påverkan både på fordonets kostnadoch dess miljö- och klimatpåverkan. Det är därför viktigt att försöka maximera batteriets utnytjandegrad. Användargränser upprätthålls av batterietsstyrsystem, såkallad BMS, för att garantera säker drift samt för att begränsabatteriets åldrande. Användargränserna tenderar att vara konservativa för attta höjd för osäkerhet i tillståndsestimeringen samt batteriets förändrade egenskaper under dess livstid. För att utöka utnyttjandegraden är ålderskänsligstyrning nödvändig. Med detta avses styrning som a) justeras under batterietslivstid och b) balancerar utnyttjande och prestanda på ett sätt som passar enspecifik applikation.

Ombord på fordon mäts typiskt tre signaler; ström, spänning och temperatur. Batteriets beteende bestäms dock av andra tillstånd som måste estimeras, såsom dess laddnivåeller lokala koncentrationer och potentialer. BMS:enförlitar sig därför på modeller för att estimera interna tillstånd och utföra styrning. För att uppfylla punkterna a) och b) måste modellerna som användsuppdateras ombord i takt med att batteriet åldras. En uppdaterad modellkan också fungera som ett diagnostiskt verktyg eftersom det speglar batteriets förändrade egenskaper. Den här avhandlingen undersöker identifieringav fysikbaserade och empiriska modeller från kördata. Arbetet delas in i treseparata studier.

1) En global känslighetsanalys utfördes på parametrarna i en fysikbaseradmodell av hög ordning. Som inputsignal användes uppmätt ström från riktigaelfordon i drift. Parametrarnas effekt på både cellspänning och interna batteritillstånd analyserades. Studien visade att alla modellparametrar exciteradesav strömmen från ett helelektriskt fordon. Anledningen var att batteriernaanvändes inom ett brett SOC spann samt att den dragna strömmen var stor.I fordon med snävare SOC span och lägre strömmar var inte alla parametrarkänsliga.

2) Dynamiska parametervarierande modeller formulerades och identifierades från kördata. Den uppmätta temperaturen, samt strömmens storlekoch den estimerade tomgångsspänningen (OCV) användes till parameterberoenden. För att hantera skillnader i tidsskala mellan spänningssvarets olikakomponenter användes systemidentifiering i kontinuerlig tid. Vi kunde draslutsatsen att de föreslagna modellerna var överlägsna motsvarande diskretaoch konstanta modeller.

3) Istället för att använda kördata för att parametrisera modeller undersökte vi också möjligheten att designa laddförloppet för att öka dess informationsinnheåll. Detta formulerades som ett optimeringsproblem med laddtidoch informationsinnehåll i kostnadsfunktionen. För att även ta batteriets åldrande i beaktning, ansattses bivillkor på polariseringsspänningen.

Physics-based battery models are important tools in battery research, development, and control. To obtain useful information from the models, accurate parametrization is essential. A complex model structure and many unknown and hard-to-measure parameters make parametrization challenging. Furthermore, numerous applications require non-invasive parametrization relying on parameter estimation from measurements of current and voltage. Parametrization of physics-based battery models from input-output data is a growing research area with many recent publications. This paper aims to bridge the gap between researchers from different fields that work with battery model parametrization, since successful parametrization requires both knowledge of the underlying physical system as well as understanding of theory and concepts behind parameter estimation. The review encompasses sensitivity analyses, methods for parameter optimization, structural and practical identifiability analyses, design of experiments and methods for validation as well as the use of machine learning in parametrization. We highlight that not all model parameters can accurately be identified nor are all relevant for model performance. Nonetheless, no consensus on parameter importance could be shown. Local methods are commonly chosen because of their computational advantages. However, we find that the implications of local methods for analysis of non-linear models are often not sufficiently considered in reviewed literature.

One approach for State-of-health estimation onboard electric vehicles is to train a data-driven virtual battery on operational data and use this model, rather than the actual battery, for performance tests. A temperature-dependent continuous-time output-error (OE) model is proposed as virtual battery and identified and validated on real operational data from electric buses. The proposed model is compared to discrete-time and parameter-invariant models and shows better performance on all data sets. In addition, the OE model structure is shown to be superior to a conventional Auto Regressive eXogenous (ARX) model for the purpose of modeling the battery voltage response. Finally, challenges regarding vehicle log data are identified and improvements to the model are suggested in order to capture observed un-modeled phenomena.

Fast charging of electric vehicles remains a compromise between charging time and degradation penalty. Conventional battery management systems use experience-based charging protocols that are expected to meet vehicle lifetime goals. Novel electrochemical model-based battery fast charging uses a model to observe internal battery states. This enables control of charging rates based on states such as the lithium-plating potential but relies on an accurate model as well as accurate model parameters. However, the impact of battery degradation on the model’s accuracy and therefore the fitness of the estimated optimal charging procedure is often not considered. In this work, we therefore investigate electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells. First, an electrochemical model is identified at the beginning of life for 6 automotive prototype cells and the electrochemically constrained fast-charge is designed. The model parameters are then periodically re-evaluated during a cycling study and the charging procedure is updated to account for cell degradation. The proposed method is compared with two reference protocols to investigate both the effectiveness of selected electrochemical constraints as well as the benefit of aging-adaptive usage. Finally, post-mortem characterization is presented to highlight the benefit of aging-adaptive battery utilization.