Presented below are illustrative examples showcasing how other companies have employed the TWAICE Battery Simulation Model to get a better understanding of the influence of operational stress factors on battery aging.

In modern transportation, electric vehicle (EV) fleets have emerged as pioneering symbols of sustainability and innovation. However, amidst the buzz of zero-emission travel and cutting-edge technology, a critical challenge looms large. It revolves around a seemingly simple yet complex aspect of EV operation: the influence of operational stress factors on the lifespan of the battery. One prominent example constitutes recharging. While the act of recharging seems simple, the hidden complexities of how a battery degrades during recharging have profound consequences for EV fleet operators in both technical and financial terms, e.g., vehicle availability or operational costs.

Decision-making based on simulations has significantly helped fleet operators overcome the challenges associated with understanding and managing aging. Simulations provide a controlled and scalable environment to model various scenarios and assess the impact of different operational stress factors on battery performance and aging. For instance, simulations replicate various recharging scenarios, considering factors like charging frequency, charging current, and battery state of charge. This helped fleet operators determine optimal charging strategies that balance battery health, vehicle availability, and operational costs.

In this example, the influence of operational stress factors on battery aging is illustrated by considering four different recharging scenarios:

Once the simulation is finished, view the results in Simulink by clicking on the respective scopes in the simulation results section (this step can be skipped).

To conduct the sensitivity analysis, the same simulation needs to be conducted with other input profiles. To streamline this, the outcomes have been pre-generated in advance. You can access all these results by executing the Matlab script “RunMe_Use_Case_Visualization.m” in the “Examplary_Use_Cases” folder.

The provided graphs show a comparison between the various recharging scenarios. After simulating for 8 years and ~6300Ah, the results show that the battery SOHc is 85.3% for “AC before next operation”, 81.2% for “AC immediately after operation”, 82.3% for “DC before next operation”, and less than 80% for “DC immediately after operation”. The simulations suggest that initiating the recharging process right away results in more significant degradation compared to postponing it until there is still adequate time for recharging. Therefore, it can be concluded that the state in which the battery is being rested significantly affects battery degradation. Furthermore, a higher charging current as applied during DC charging reveals a higher degradation rate.

These findings suggest that it is essential to consider both the resting SOC level and the C-rate as significant stress factors when devising a charging strategy for an EV fleet. This information allows for optimizing the balance between battery health, vehicle availability, and operational costs.

The OCV is a key element in electric models of lithium-ion batteries and describes the thermodynamic voltage at equilibrium. Its specific shape is defined by the materials in the anode and cathode as well as the electrode’s balancing. Since battery materials age dependent on external stress factors, the OCV changes over lifetime. Unfortunately, the change of this OCV curve over aging is often neglected, resulting in major challenges regarding accuracy of battery models over lifetime.

In this example, the change of the OCV curve with aging is illustrated by considering the above-mentioned recharging scenario “DC fast charging immediately after operation”.

Once the simulation is finished (instructions see above), the OCV curve can be analyzed at different SOH levels. To streamline this, the outcomes have been pre-generated in advance. You can access all these results by executing the Matlab script “RunMe_OCV_Evaluation.m” in the “Examplary_Use_Cases” folder.

The provided graphs show the change of the OCV curve with aging and the resulting error between a new and an aged cell. As can be seen, the difference in OCV between the new and the aged cell exceeds 3%, particularly at mid and low SOCs. The simulation indicates that an OCV error of this nature may lead to a deviation of up to 6% in estimating the SOC, either underestimating or overestimating in the worst-case scenario. Consequently, it can be inferred that incorporating aging-related updates to the OCV is highly advantageous for achieving precise cell state estimations. This approach not only enhances the accuracy of the electric model, particularly for cells experiencing degradation, but also enables the utilization of the updated OCV for tasks such as parameterizing state estimation filters like Kalman filters for estimating State of Charge and State of Health.