Modelling, parameter identification and aging-sensitive management of lithium-ion batteries in heavy-duty electric vehicles

Sammanfattning: 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.