Device and method for handling data associated with energy consumption of a vehicle
A device obtains modelling data associated with energy consumption of a model vehicle. The modelling data are generated by a digital model of the vehicle in operation. The device obtains operating data associated with energy consumption of the vehicle in operation. The device compares the operating data to the modelling data. Based on a result of the comparing, the device detects a discrepancy between the operating data and the modelling data and associated with the energy consumption. The device evaluates the detected discrepancy associated with the energy consumption. The device triggers an operation when the discrepancy has been detected.
1 . A method performed by a device for handling data associated with energy consumption of a vehicle in operation, the method comprising:
obtaining modelling data associated with energy consumption of a model vehicle, wherein the modelling data are generated by a digital model of the vehicle in operation, using a machine learning algorithm trained on historic operating data obtained from a fleet of vehicles;
obtaining operating data associated with energy consumption of the vehicle in operation;
comparing the operating data to the modelling data;
based on a result of the comparing, detecting a discrepancy between the operating data and the modelling data and associated with the energy consumption;
evaluating the detected discrepancy associated with the energy consumption, wherein the evaluating comprises performing a root cause analysis to determine a source of the detected discrepancy; and
triggering an operation when the discrepancy has been detected.
2 . The method according to claim 1 , wherein the evaluating the detected discrepancy associated with the energy consumption comprises one or more of:
evaluating energy consumption of the vehicle in operation;
detecting malfunction of the vehicle in operation;
determining a reason for the discrepancy;
determining a vehicle configuration change; and
determining a vehicle operation change.
3 . The method according to either of claim 1 , wherein the operation comprises one or more of:
providing information associated with the discrepancy;
triggering an alert;
initiating scheduling of a service operation; and
requesting input from a user of the vehicle in operation.
4 . The method according to claim 1 , wherein the modelling data and the operating data are both based on static data and/or dynamic data.
5 . The method according to claim 1 , comprising:
obtaining a statistical distribution of the modelling data; and
wherein the comparing the operating data to the modelling data comprises:
comparing operating data to the statistical distribution of the modelling data to determine if the operating data is according to the statistical distribution or not.
6 . The method according to claim 1 , wherein the evaluating the detected discrepancy associated with the energy consumption comprises:
determining a user anticipation score for a user of the vehicle in operation, wherein the user anticipation score is:
user anticipation score ˜
f
(
w_overall
W
α
,
B
δ
,
S
ζ
)
,
where
W: weight impact ˜α*w light +β*w medium +γ*w full or an actual weight transported
B: brake impact
max
(
brakes
-
stops
stops
,
brake_max
)
S: speed impact ˜
v
-
v
min
v
max
-
v
min
α, β, γ, δ, ε, ζ, w overall , brake_max: real value scalar parameters
w_overall: overall weight
brake_max: max limit for a brake impact
θ: a normalized sigmoid function that maps any real value to a value between 0 and 1
w light , w medium , w full : a ratio of km driven with light, medium and full weight load respectively:
ν: an average speed and speeds outside ν min or ν max will be clipped to those values.
7 . The method according to claim 1 , wherein the evaluating the detected discrepancy associated with the energy consumption comprises:
determining a user eco score for a user of the vehicle in operation, wherein the user eco score is:
eco score ˜
f
(
w
overall
*
W
η
O
θ
*
N
κ
*
S
μ
)
,
where
W: weight impact ˜α*w light +β*w medium +γ*w full or actual weight transported
O: overload impact ˜
1
φ
*
t_overload
t_total
and/or topography impact,
N: not in green zone impact ˜
1
ξ
*
(
max
(
min
(
l
notgreen
-
l
avg
l
avg
,
0
)
,
1
)
)
S: speed impact ˜
v
-
v
min
v
max
-
v
min
η, θ, κ, μ, φ:real value scalar parameters
W overall : an overall weight
φ, ξ: normalization factors
ƒ: a normalized sigmoid function that maps any real value to a value between 0 and 1
w light , w medium , w full : a ratio of km driven with light, medium and full weight load respectively
t_overload
t_total
:
a ratio of time spent in overload
l_notgreen: liters per 100 km spent above the green zone:
l avg : liters per 100 km:
ν: an average speed and speeds outside ν min or ν max will be clipped to those values.
8 . The method according to claim 1 , wherein the digital model is implemented on a remote server or in the vehicle.
9 . The method according to claim 1 , wherein the digital model is configured based on historic operating data obtained from a fleet of vehicles.
10 . The method according to claim 9 , wherein vehicles comprised in the fleet of vehicles have similar mission and configuration.
11 . The method according to claim 9 , wherein the vehicles comprised in the fleet of vehicles are selected from a main fleet of vehicles comprising vehicles having both similar and different mission and configuration.
12 . A device for handling a data associated with energy consumption of vehicles, the device being configured to perform the steps of the method according to claim 1 .
13 . A vehicle comprising a device according to claim 12 .
14 . A non-transitory computer readable medium carrying a computer program comprising program code for performing the steps of claim 1 when the computer program is run on a computer.