The decarbonization of the transport sector, and thus of road-based transport logistics, through electrification, is essential to achieve European climate targets. Battery electric trucks offer the greatest well-to-wheel potential for CO₂ saving. At the same time, however, they are subject to restrictions due to charging events because of their limited range compared to conventional trucks. These restrictions can be kept to a minimum through optimal charging stop strategies. In this paper, we quantify these restrictions and show the potential of optimal strategies. The modeling of an optimal charging stop strategy is described mathematically as an optimization problem and solved by a genetic algorithm. The results show that in the case of long-distance transport using trucks with battery capacities lower than 750 kWh, a time loss is to be expected. However, this can be kept below 20 min for most battery capacities by optimal charging stops and sufficient charging infrastructure.

Battery electric trucks (BETs) represent a well-suited option for decarbonizing road freight transport to achieve climate targets in the European Union. However, lower ranges than the daily distance of up to 700 km make charging stops mandatory. This paper presents an online algorithm for optimal dynamic charging strategies for long-haul BET based on a dynamic programming approach. In several case studies, we investigate the advantages optimal strategies can bring compared to driver decisions. We further show which charging infrastructure characteristics in terms of charging power, density, and charging station availability should be achieved for BETs in long-haul applications to keep the additional time required for charging stops low. In doing so, we consider the dynamic handling of occupied charging stations for the first time in the context of BET. Our findings show that, compared to driver decisions, optimal charging strategies can reduce the time loss by half compared to diesel trucks. To keep the time loss compared to a diesel truck below 30 min a day, a BET with a 500 kWh battery would need a charging point every 50 km on average, a distributed charging power between 700 and 1500 kW, and an average charger availability above 75%. The presented method and the case studies’ results’ plausibility are interpreted within a comprehensive sensitivity analysis and subsequently discussed in detail. Finally, we transformed our findings into concrete recommendations for action for the efficient rollout of BETs in long-haul applications.

In recent years, topics of digitalisation, urbanisation and sustainability are shaping future developments in the automotive industry and pose new challenges to the individual areas of vehicle development. In the design and simulation of multiple components in the vehicle, especially in powertrain, generically generated driving cycles play an important role since they reflect representative user behavior. Nowadays, driving cycles are mainly associated with the worldwide fuel consumption and emission tests, but they are nevertheless used in many fields of vehicle development. A design based on the currently used consumption and emission cycles proves to be unsuitable, especially for electric vehicles. This is reflected in the current discussion about the large discrepancy between the driving ranges achieved when using emission cycles and those under real driving conditions. In order to minimize the energy consumption of a vehicle, the main requirement is a good efficiency of the electric machine. Here, not the maximum efficiency of the machine is decisive, but the averaged overall system efficiency during real driving behavior. The electric machine must therefore be designed for the driving cycle. To optimize the use of electric machines in the future, the actual power requirements of future vehicle models must first be determined. In the course of this paper, a compressed vehicle class specific driving cycle will be created based on real driving data using Markov chains, which can be used for powertrain dimensioning.

With growing demands to save greenhouse gases, the rapid market introduction of battery-electric trucks (BETs) will become increasingly important, with truck manufacturers announcing various models entering the market in the near future. Soon, truck operators will be faced with deciding which battery capacity and cell chemistry to choose in their next purchase. In this study, we evaluate the choice of battery capacity, regarding feasibility and cost-effectiveness, for trucks using NMC and LFP cell chemistry. Our results show that higher energy density allows larger NMC batteries to be installed, resulting in the ability to transport higher payloads at low charging powers. The LFP chemistry has to rely on higher charging powers of up to 700 $\mathrm{kW}$ to transport the same payloads. When asked to choose a battery capacity for the individual use case, the smallest battery size should always be selected when only charging powers up to 300 $\mathrm{kW}$ are available. However, the reduction in publicly charged energy can lead to cost advantages of larger battery capacities at higher charging powers. When deciding between the two cell chemistries, the LFP chemistry shows advantages in most cases. Only at high payloads and low charging powers the NMC chemistry shows cost advantages.

To establish battery electric vehicles as an attractive alternative to internal combustion vehicles, charging times of 15 min or less are increasingly demanded. This is especially challenging for lower battery temperatures, as this exacerbates the risk of accelerated battery degradation due to lithium plating. Therefore, active battery heating is utilized in state-of-the-art electric vehicles. To evaluate the impact of such heating strategies at vehicle level, we deployed an electrochemical battery model coupled with a longitudinal vehicle dynamics model. Using anode potential control to prevent lithium plating, we assess the time-saving potential versus the energy cost of different preconditioning and fast-charging strategies. The results reveal substantial energy saving and charge speed increase potential through optimal charge-stop planning, preconditioning timing, cost-adjusted thermal management thresholds, and considering driving behavior. This emphasizes the need for advanced operation strategies, taking into account both battery-level electrical and thermal restrictions, as well as vehicle integration and route planning.