Browse Publications Technical Papers 2022-24-0009
2022-09-16

Battery Selection and Optimal Energy Management for a Range-Extended Electric Delivery Truck 2022-24-0009

Delivery trucks and vans represent a growing transportation segment which reflects the shift of consumers towards on-line shopping and on-demand delivery. Therefore, electrification of this class of vehicles is going to play a major role in the decarbonization of the transportation sector and in the transition to a sustainable mobility system. Hybrid electric vehicles can represent a medium-term solution and have gained an increasing share of the market in recent years. These vehicles include two power sources, typically an internal combustion engine and a battery, which gives more degrees of freedom when controlling the powertrain to satisfy the power request at the wheels. Components sizing and powertrain energy management are strongly coupled and can make a substantial impact on the final energy consumption of a hybrid vehicle. An optimal control problem can be formulated and solved off-line, to evaluate the most energy efficient utilization of fuel and battery energy for the specific vehicle design. These off-line solutions can be used as benchmarks to compare different architectures and design options, and rules can be derived from them to design on-line implementable heuristic control strategies. In this work a plug-in series architecture with a range-extender engine is considered for a Class 6 beverage delivery truck and the optimal control problem is solved using dynamic programming. To study the strong coupling that exists between components sizing and energy management a simulation design space exploration is performed with four different cell chemistries and three different battery pack sizes. A drive cycle with variable payload, which is representative of delivery operations, is developed and used as a test case. The results show how the selection of the cell chemistry and the constraints on the battery weight can affect the battery performance and the impact on the optimal energy management strategy.

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