Leaf i3 e6 Model S 0 0.2 0.4 0.6 0.8 1 Total Time of Attack [hr] AC-High+Lights+Power-Steering+Wipers AC-Low+Power-Steering+Wipers AC-High AC-Low Wipers Power Steering Lights Fan 0 6 12 18 Fraction of Total Range Lost [%] 0 6 12 18 0 3 6 9 0 2 4 6 Charging at Work Charging at Home Oslo San Francisco Beijing Delhi Phoenix 1 1.5 2 2.5 3 3.5 R * (in 400 days) T = 40 o C 0 1 2 3 4 5 6 Power [kW] 10.8 11 11.2 11.4 11.6 %Damage to Vital Capacity AC-High+Lights+Power-Steering+Wipers AC-Low+Power-Steering+Wipers AC-High AC-Low Wipers Power Steering Lights Fan 0 10 20 30 40 Ambient Temperature [ o C] 0 2 4 6 8 10 12 % Damage to Vital Capacity 39.8 39.9 40 10.5 11 11.5 T = 40 o C 0 5 10 15 20 25 Total Time of Attack [days] 0 5 10 15 % Damage to Vital Capacity 18 18.5 19 10 10.5 11 Baseline Load Attack Load Home Work Home Attack (a) -20 0 20 40 60 Power [kW] Attack Attack (b) Baseline Load Attack Load 0:00 4:00 9:00 14:00 19:00 23:59 Time-of-day [24-hour clock] -20 0 20 40 60 Power [kW] Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months -10 0 10 20 30 40 Average Temperature [ o C] Phoenix San Francisco Oslo Beijing Delhi Vulnerabilities of EV Battery Packs to Cyber Attacks Shashank Sripad*, Sekar Kulandaivel ^ , Vyas Sekar ^ , Venkat Viswanathan* *Department of Mechanical Engineering, ^ Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213. ✉ [email protected] , [email protected] INTRODUCTION RESULTS FUTURE WORK Pictorial illustration of an attack scenario Parametric analysis of the different variables • Modern Automobiles are entirely controlled by electronic circuits which expose them to cyber threats. • Large-scale EV adoption is underway. • There are unique and critical risks involved with EV batteries which could be the target of cyber attacks. Short-term impact on available range • Auxiliary component-based attacks cause significant short- term damage by depleting the available range and potential long-term impact by enhancing the side reactions. • Inclusion of the charging infrastructure would facilitate a more comprehensive understanding of the risks and impact. • Our analysis highlights the importance of developing phenomenological models of the degradation process and safety models along similar lines will be used in future work. • The use of metrics like ∆R has implications on other areas like V2G scenarios. • Leveraging safety models to quantify the risk of events like thermal runaway in cyber attack scenarios. Comparison of the permanent damage in different locations Attack workloads and environmental conditions