4.2.1. Improvement of the SOP Prediction

Present SOP prediction strategies are based primarily on current, voltage, and SOC limits [37,40]. The thermal battery state is largely neglected [69]. Taking the example of a common scenario of highway driving followed by fast charging, the importance of taking the temperature into account is presented. Figure 7 depicts the current profile and the related temperature increase in the target data of the reference system. While driving, both thermal models are used to estimate the core temperature of a cell. Both models conform very well to the target data with a small deviation of less than 0.1 K for the ANN and a maximum of 0.8 K for the TECM. The maximum deviation appears in both cases during fast charging at the highest thermal stress of the cell. Subsequently, a conventional power prediction is performed without the observer for the upcoming continuation of the driving and the expected thermal behavior is predicted for different permissible current scenarios. A detailed view of the mid-term prediction scenario is displayed on the right side of Figure 7. As revealed by the results of both models, the only electrical-based conventional calculation of the SOP will exceed the safety temperature limit of 60 °C. In addition, predicting the point in time for reaching the voltage or SOC limit is not performed correctly without taking into account the temperature dependency of the corresponding electrical parameters. Bearing in mind the temperature limit, one may reduce the total permissible output current so that the maximum allowable temperature is not reached during the total discharging (see dashed lines in both cases). This, in turn, restricts the available power in the early stage.

**Figure 7.** Highway driving and fast charging scenario with in-use SOP prediction in various ways: ANN/TECM approaches are used for the temperature estimation while driving, as well as the prediction of the temperature during SOP determination.

An optimized SOP prediction by an individual output current limitation based on temperature prediction is, therefore, desirable. Multiple output current scenarios can be calculated in advance and the resulting temperature is predicted, which enables maximized performance, while staying within the battery safety limits. Both modeling approaches reveal comparable predicted temperature results. The differences arise from the model's specific absolute temperature predictions.

On the basis of the detailed models, multiple different reference temperatures can be combined with adaptive and intelligent algorithms for safe prediction optimization. Nevertheless, concrete algorithms are beyond the scope of this work and form part of future investigations. In the present investigation, a simple optimization of the point in time for limiting the output current and the current value after limitation is performed (Figure 7). Once a thermally safe scenario has been found, the associated current profile can be used to calculate the available power. Thus, the investigation clearly shows that considering the temperature development is necessary for an accurate, safe and maximized performance SOP prediction.

#### 4.2.2. Predictive Thermal Management

In addition to advanced state monitoring and prediction, detailed thermal models also enable the specific control of the cooling system. Conventional cooling system approaches for BEVs normally react to measured temperatures, e.g., at the cell terminals, and do not act predictively to achieve target temperatures. There are already approaches in the literature for novel BTMS that use model-based temperature prediction in order to react to varying conditions [10,11]. At this point, we add another approach to the existing ones, which relates to the thermal pre-conditioning of batteries. In their work, Collin et al. [50] propose to elevate the battery system temperature in order to enable an optimized fast charging. To meet this goal, the future cooling regulation does not only depend on static temperature rules but also needs to be adjusted.

For the same BEV driving scenario as for the SOP prediction, the step-wise cooling system behavior (see Figure 3) and the resulting terminal temperature are shown in Figure 8. The current profile, the related temperature increase and the cooling system behavior of the target data are depicted in black until the end of fast charging at 3200 s.

**Figure 8.** Temperature estimation and predictive cooling system regulation based on ANN/TECM thermal model integrated with an observer in a BEV environment.

The models show comparable estimation behavior with good representation of the transient temperature development. In both cases, the largest deviation arises at the beginning of fast charging. The terminal temperature is underestimated for the initial fast charging section with a maximum deviation of 0.7 K for the TECM and 0.5 K for the NARX. Afterwards, the NARX converges to the target temperature more quickly, while the TECM requires the period towards the end of charging. The increased deviation for the terminal temperature estimation compared to the core temperature estimation in Figure 7 corresponds with the findings from the validation. Subsequent to the fast charging, the models are used to predict the temperature for the basic cooling system behavior of Figure 3 for continued highway driving after fast charging stops.

Here, it should be mentioned that a key aspect of thermal prediction is load prediction. However, the prediction of the future current is a stand-alone field of current research and, for example, performed based on navigation profiles and geographical data [70]. In this work, we assume that the future vehicle velocity profile on a motorway contains comparable elements in the same order of magnitude as the previous highway driving. Based on this velocity profile and a simplified vehicle model, a load profile is calculated. In a real BEV application, this simple prediction can be optimized, e.g., by navigation information or driver behavior models.

It can be seen in the case of both models, that the present terminal temperatures lead to the activation of the cooling system and a transient temperature decrease towards 30 °C. However, if the destination is the next fast charging stop, raising the temperature towards the end could be more suitable [48]. Using the thermal models, in a simple optimization procedure, temperature development during dynamic driving towards the next charging stop can be predicted while testing different pre-conditioning timelines. The resulting differences in the transient temperature development of the core temperature are visualized in gray color. Using the simple battery cooling model in combination with model-based temperature prediction, the resulting temperature at the next charging station can be increased. In such a case, the range of the variation depends on concrete application, including the thermal mass of the battery, the ambient temperature and the distance to the next charging stop. It is shown that, depending on the respective cooling step and the switching time, the resulting cell temperature can be varied. Using thermal models for predictive thermal management in applications of this type has several advantages. The favored target temperatures can be reached precisely. In addition, unnecessary cooling power to temporarily reduce the cell temperature is available as electrical energy [44] and, therefore, extends, e.g., the vehicle's range. Furthermore, the thermal conditions for fast charging procedures can be optimized to improve the performance and extend the lifetime [50].

A possible enhanced BTMS needs to evaluate and weight the different influencing factors, e.g., battery temperature, future driving state and effect on driving range, to choose an optimal strategy for cooling system power regulation. In contrast to the already described approaches in the introduction, monitoring the cells' individual temperatures also enables a new degree of freedom for reacting to maximum temperatures inside the battery system and specific cooling regulation.
