Modeling the impact of battery degradation within lifecycle cost based design optimization of heavy-duty hybrid electric vehicles
Date of Award
Doctor of Philosophy (PhD)
Gregory M. Shaver
Peter H. Meckl
Gregory M. Shaver
Peter H. Meckl
Committee Member 1
Galen B. King
Committee Member 2
The optimal design of hybrid electric vehicle (HEV) powertrains from a systems perspective is critical to realize the maximum benefits for a given application. This is particularly true in the heavy-duty vehicle space where the major challenges are: (i) greater emphasis on economic viability, (ii) reluctance to take on risk associated with new technologies, and (iii) numerous diverse applications that preclude a one-size-fits-all approach to hybrid-electric powertrain design. Past studies on HEV powertrain design have either ignored battery degradation, or failed to holistically capture its impact from a lifecycle cost perspective. The focus of this effort is the development of a model-based framework that enables parametric optimization of the design and control of hybrid electric vehicles while accounting for the degradation of the lithium-ion battery and its impact on the total cost-of-ownership of the vehicle.
Two different implementations of such a framework are described. The first implementation explores a very high-fidelity approach to enable engineering design optimization across a small parameter space. It captures the impact of battery degradation on fuel consumption and battery replacements over the vehicle life by incorporating a high-fidelity electrochemical battery model capable of predicting degradation, and degraded performance, into the powertrain simulation. An electric motor and battery size optimization problem is studied for a parallel HEV transit bus application. Results show that different optimal component sizes are obtained when different optimization objectives, such as net present value, payback period, internal rate of return, or simply the "day 1" fuel consumption, are considered. Accounting for the battery degradation in the powertrain simulations shows fuel consumption increasing by up to 10% from "day 1" to end-of-life of the battery. These results highlight the utility of the proposed implementation in enabling better design decisions as compared to methods that do not capture the evolution of vehicle performance and fuel consumption as the battery degrades. However, the high-fidelity electrochemical battery degradation model and the interval-by-interval simulation approach used in this implementation are computationally too expensive for a large-scale design study.
In contrast, the second implementation uses a simpler empirical battery model to enable a large-scale study over a 10-parameter design space, over multiple architectures and vehicle applications. This implementation is designed to aid heavy-duty vehicle and powertrain component manufacturers in identifying market opportunities and planning future products. The design space explored in this work includes three powertrain component sizing parameters, four control strategy parameters and three vehicle uncertainty parameters. Multiple drive cycles were simulated across the Class 5-7 medium-duty truck and Class 7-8 transit bus applications for both parallel and series plug-in hybrid electric vehicle (PHEV) powertrain architectures with charge depleting and charge sustaining modes of operation. These simulation results were then evaluated for real-world economic viability under different economic assumptions corresponding to the 2015, 2020, 2025 and 2030 time frames. Sensitivity of the economic viability of solutions was also studied with respect to the vehicle uncertainty parameters, economic assumptions and vehicle utilization assumptions. (Abstract shortened by ProQuest.)
Vora, Ashish P., "Modeling the impact of battery degradation within lifecycle cost based design optimization of heavy-duty hybrid electric vehicles" (2016). Open Access Dissertations. 875.
Automotive Engineering Commons, Electrical and Computer Engineering Commons, Mechanical Engineering Commons