Concurrent Design Optimization of Powertrain Component Modules in a Family of Electric Vehicles

TL;DR

Results show that, compared to an individually tailored design, the application of the concurrent design optimization framework achieves a significant reduction of acquisition price for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5%.

Abstract

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting one-size-fits-all problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules' sizes and the powertrain operation of the entire family, for given driving cycles and modules' multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5\%.

Figures (17)

• Figure 1: Family of battery electric vehicles designed leveraging a shared modular powertrain. Every vehicle type is equipped with $N_{\mathrm{m},i}^\star$ motor modules (EM) of size $S_{\mathrm{m}}^\star$ and $N_{\mathrm{b},i}^\star$ battery modules of size $S_{\mathrm{b}}^\star$, for a total maximum motor power of $\overline{P}_{\mathrm{tot},i}$ and maximum energy capacity of $\overline{E}_{\mathrm{tot},i}$.
• Figure 2: Concurrent design optimization methodology diagram.
• Figure 3: Efficiency map of an electric motor (left) compared with an internal combustion engine (right). Data from Solipuram2024.
• Figure 4: Flow diagram explaining the algorithm employed in computing the objective function (TCO) of our concurrent design optimization framework.
• Figure 5: Vehicle topologies considered in this framework: Front-wheel drive (FWD) for vehicles equipping one motor module, and All-wheel drive (AWD) for two.