Behaviour of Lithium-Ion Batteries in Electric Vehicles by Gianfranco Pistoia & Boryann Liaw

Behaviour of Lithium-Ion Batteries in Electric Vehicles by Gianfranco Pistoia & Boryann Liaw

Author:Gianfranco Pistoia & Boryann Liaw
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham


6.2 Battery Degradation Effects

In the following, we analyze the impact of increasing service area vicinities on the factors that influence battery degradation that are subject to routing decisions and are not controlled by the BMS, i.e., times in which batteries remain at a high SOC, large charging and discharging cycles, and the number of discharging cycles. Herein, we analyze the number of recharges and the deepest discharge for each vicinity and tour. Doing so, we are capable of deriving insights into the number of discharging cycles (that correlate with the number of recharges on routes). Furthermore, implications of large (dis-)charging cycles and high SOCs are derived based on information on the deepest discharge.

Figure 5 shows the distribution of the deepest discharges on routes for each service area vicinity. For each group of bars, the percentages vary from discharges between 0–10% and 90–100%. As can be seen, the share of routes with a discharge of more than 90% of the battery’s capacity increases significantly with increasing service area vicinities, starting at 2.17% (80 km) and ending with up to 53.13% (190 km). One could assume that similar trends exist for the other ranges of deepest discharges. However, this is not the case as can be seen for discharges between 20 and 90% of the battery’s capacity. To explain this effect, we have to take into account the relationship between the number of stores and their distribution in the service area (cf. Figs. 1 and 2). As can be seen, the number of stores that is added to the service area decreases with increasing vicinities, while the added customers are still distributed over the whole plane. As the objective of our MIP is to minimize total costs [cf. (4)] (which is the most likely objective used in practice), the added stores are integrated into existing tours, i.e., tours that were designed for a smaller vicinity are split up to integrate new stores. This means that tours get longer and, thus, the proportion of tours where the maximum battery capacity is used increases. Thus, a shift to deeper discharges and a trend for deepest discharges above 90% can be observed, but other levels of discharge vary irregularly. Shares remain below 5% for deepest discharges between 0–10% or 10–20% independent of the service area vicinity. Note that results would change if another objective function would be used, i.e., minimizing the number of tours with deep discharges.

Fig. 5Distribution of deepest discharges on routes with respect to the service area vicinity



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