Synthesis of Statistically Representative Driving Cycle for Tracked Vehicles

Drive cycles are a core piece of vehicle development testing methodology. The control and calibration of the vehicle is often tuned over drive cycles as they are the best representation of the real-world driving the vehicle will see during deployment. To obtain general performance numerous drive cycles must be generated to ensure final control and calibration avoids overfitting to the specifics of a single drive cycle. When real-world driving cycles are difficult to acquire methods can be used to create statistically similar synthetic drive cycles to avoid the overfitting problem. This subject has been well addressed within the passenger vehicle domain but must be expanded upon for utilization with tracked off-road vehicles. Development of hybrid tracked vehicles has increased this need further. This study shows that turning dynamics have significant influence on the vehicle power demand and on the power demand on each individual track. Hybrid tracked vehicle development must consider both power demands as they are a key factor when deciding location and sizing of electrified powertrain components. This study identifies four key parameters that must be included in drive cycle development for tracked vehicles and proposes a Markov chain model framework to generate synthetic drive cycles from limited reference data.