Load optimization for reducing the cost of an electric vehicle’s battery pack

Cárdenas, Bruno; Garvey, Seamus D.

Abstract

This paper presents a study on the cost-optimization of the battery pack of a Nissan Leaf. The optimization is based on decomposing the load that the battery pack experiences into two components (low and high frequency), each of which will be handled by an independent battery. The reduction in cost comes from the possibility of manufacturing batteries of different specifications whose cost per unit energy ($/kWh) and per unit power ($/kW) differ considerably from each other. The battery used for the low frequency part of the load will have a low cost per unit energy capacity and a higher cost per unit power whilst the fast-frequency battery is the reverse case.

Two case studies have been carried out. The first one uses the load profile seen by the battery pack when the car is subjected to the EPA-LA92 driving cycle. The second case study considers a modified profile with a much higher crest factor. A sign-preserving filter is used in the study to perform the signal splitting. A two-dimensional search space is created with the two control parameters of the filter and numerous different “splits” are explored.

Results show than an important reduction in the cost of the battery pack can be achieved. In the optimum configuration found—for the case study carried out with the modified profile—the low-frequency battery supplies 80.14% of the total capacity of the car (24 kWh) and sees a maximum peak power of 37.17 kW; whereas the fast-frequency battery has a smaller capacity of 4.77 kW h but sees a much larger peak power of 88.56 kW. The total cost of this hybrid system is estimated at $5939, which represents a 12.7% reduction in cost with respect to the original battery pack of the vehicle.

Citation

Cárdenas, B., & Garvey, S. D. (2018). Load optimization for reducing the cost of an electric vehicle’s battery pack. Journal of Energy Storage, 20, 254-263. https://doi.org/10.1016/j.est.2018.09.018

• Journal Article Type: Article
• Acceptance Date: Sep 25, 2018
• Online Publication Date: Oct 6, 2018
• Publication Date: Dec 31, 2018
• Deposit Date: Mar 13, 2019
• Publicly Available Date: Oct 7, 2019
• Journal: Journal of Energy Storage
• Electronic ISSN: 2352-152X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Pages: 254-263
• DOI: https://doi.org/10.1016/j.est.2018.09.018
• Public URL: https://nottingham-repository.worktribe.com/output/1636117
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S2352152X18302408?via%3Dihub
• Contract Date: Mar 13, 2019