Computer implemented methods may create unique hardware platforms suitable for providing and/or using testing protocols. A new protocol may be designed using a target protocol, such that the new protocol replicates one or more aspects of the target protocol. The new protocol may incorporate a certain diversity, which may make it more difficult for an entity being tested (e.g., a car, a truck, a person) to cheat the test (e.g., using knowledge of the target protocol). A new protocol may be calculated, validated, and provided on demand. A new protocol may be tailored to a particular set of test results (e.g., to highlight an apparent deviation between expected and actual results).

A vehicle charging management system including a transceiver and a processor is disclosed. The transceiver may be configured to receive user itinerary information associated with a user, a current state of charge (SoC) level associated with a vehicle, and information associated with expected vehicle charging parameters at different times of a day. The processor may be configured to determine an optimal vehicle charging time duration and an optimal vehicle discharging time duration for the vehicle at a predefined location based on the user itinerary information, the current SoC level and the information associated with expected vehicle charging parameters. The processor may further transmit information associated with the optimal vehicle charging time duration and the optimal vehicle discharging time duration to the vehicle and/or a charging station device associated with a charging station located at the predefined location.

Total charging time to charge an EV from a starting SOC to a target SOC is determined based on vehicle related data. Home energy usage of a home in an EV availability time window is also determined based on home related data. EV idle power consumptions for an EV charging mode, an EV discharging mode, and an EV idle mode with neither charging nor discharging are further determined. A genetic algorithm is set up with cost and penalty functions. These functions are built, for each candidate schedule in a solution space, based on the starting state of charge, the home energy usage and the EV idle power consumptions. The genetic algorithm is run to generate an optimized schedule that includes schedule values for the time slots in the EV availability time window to control whether the EV is to charge, discharge or idle in each of these time slots.

A vehicle-weather resistance parameter indicates a localized weather condition for a vehicle traveling a road segment of a travel route, based on forecasted generalized weather information for the road segment. The vehicle-weather resistance parameter may be generated by a function that is produced using previously collected vehicle data from vehicles of different vehicle types that traveled the road segment under various weather conditions. The vehicle-weather resistance parameter may be employed to estimate a segment energy consumption, which is an amount of energy consumed by a vehicle to travel the road segment in the weather conditions indicated by the forecasted generalized weather information. By comparing the estimated segment energy consumption to a stored energy level (e.g., charge) in a vehicle, one may determine whether there is an energy deficit and, if so, operation of electrical components of the vehicle may be adjusted to reduce or avoid the energy deficit.