Telematics-based method and system of battery parasitic load validation for a vehicle fleet
View Patent ↗Parasitic loads on a vehicular battery are evaluated based on an estimated reduction in the state of charge of the battery over the course of an engine-off state.
1. A process for evaluating parasitic load on an electrical storage battery of a vehicle having an engine and an on-board electrical storage battery, said vehicle operative in alternating engine-on and engine-off states, comprising:
for each of a plurality of engine-off states to obtain a plurality of equations:
determining a first estimate of the state of charge of the battery at a first time substantially just prior to the beginning of an engine-off state or during said engine-off state;
determining a second estimate of the state of charge of the battery at a second time during said engine-off state subsequent to the first estimate of the state of charge determination;
determining an elapsed time between said first time and said second time; and
determining an estimate of the parasitic load of the battery based on the difference between the first and second estimates of state of charge of the battery and said elapsed time;
each equation having the form:
δ=( S off −S on )× C=P×T+ε
in which S off is the respective estimate of the state of charge of the battery substantially contemporaneous with the beginning of an engine-off state, and S on is the respective estimate of the state of charge of the battery substantially just prior to the beginning of the engine-on state that immediately follows the engine-off state, C is the battery reserved capacity in ampere*hours, P is the estimated parasitic load, T is an elapsed time between S off and S on , δ is state of charge loss of the battery during the elapsed time T, and ε is a constant, so as to provide a set of linear equations of the form:
[
δ
1
δ
2
⋮
δ
M
]
=
[
t
1
1
t
2
1
⋮
⋮
t
M
1
]
·
[
P
ɛ
]
in which M is the number of observations, t i (i=1 . . . M) is the instance of elapsed time and δ i (i =1 . . . M) is the instance of state of charge loss; and
solving the set of linear equations to provide a statistical estimation of the parasitic load across the plurality of engine-off states.
2. The process according to claim 1 wherein determining a first estimate of the state of charge of the battery during said engine-off state is based upon battery open circuit voltage.
3. The process according to claim 1 wherein determining said second estimate of the state of charge of the battery comprises:
measuring at least one parameter selected from the group consisting of: the battery open circuit voltage, and an indication of the temperature of said battery; and
storing said measured at least one parameter in a computer memory.
4. The process according to claim 3 wherein said computer memory is disposed on-board of said vehicle.
5. The process according to claim 3 wherein said vehicle further includes a wireless transmitter, and wherein said measured at least one parameter is transmitted to computer memory that is disposed at a location which is remote with respect to said vehicle.
6. The process according to claim 1 wherein determining estimates of state of charge of the battery during said engine-off state comprises determining an indication of the temperature of said battery.
7. The process according to claim 1 , further comprising:
providing said vehicle with a wireless transmitter; and
transmitting from said vehicle to computer memory that is remotely-located with respect to said vehicle at least one of the first estimate of the state of charge of the battery, the second estimate of the state of charge of the battery, an estimate of the state of charge of said battery determined during an engine-on state, and the estimate of the parasitic load of the battery.
8. A process for evaluating parasitic load on an electrical storage battery of a vehicle having an engine and an on-board electrical storage battery, said vehicle operative in alternating engine-on and engine-off states, comprising:
for each of a plurality of engine-off states to obtain a plurality of equations:
determining a first estimate of the state of charge of the battery at a first time substantially just prior to the beginning of an engine-off state or during said engine-off state;
determining a second estimate of the state of charge of the battery at a second time during said engine-off state subsequent to the first estimate of the state of charge determination; and
determining an estimate of the parasitic load of the battery based on the difference between the first and second estimates of state of charge of the battery and the elapsed time, wherein said elapsed time represents a difference between said first time and said second time;
each equation having the form:
δ=( S off −S on )× C=P×T+ε
in which wherein S off is the respective estimate of the state of charge of the battery substantially contemporaneous with the beginning of an engine-off state, and S on is the respective estimate of the state of charge of the battery substantially just prior to the beginning of the engine-on state that immediately follows the engine-off state, C is the battery reserved capacity in ampere*hours, P is the estimated parasitic load, T is an elapsed time between S off and S on ,δ is state of charge loss of the battery during the elapsed time T, and ε is a constant, so as to provide a set of linear equations of the form:
[
δ
1
δ
2
⋮
δ
M
]
=
[
t
1
1
t
2
1
⋮
⋮
t
M
1
]
·
[
P
ɛ
]
in which M is the number of observations, t i (i=1 . . . M) is the instance of elapsed time and δ i (i =1 . . . M) is the instance of state of charge loss; and
solving the set of linear equations to provide a statistical estimation of the parasitic load across the plurality of engine-off states; and
wirelessly transmitting the estimate of the parasitic load of the battery from said vehicle to computer memory that is remotely-located with respect to said vehicle.
9. The process according to claim 8 wherein determining a first estimate of the state of charge of the battery substantially just prior to the beginning of an engine-off state is based upon current-integration.
10. The process according to claim 8 wherein determining a first estimate of the state of charge of the battery during said engine-off state is based upon battery open circuit voltage.
11. The process according to claim 8 further comprising:
wirelessly transmitting the estimated reduction in the state of charge of the battery over the course of an engine-off state from said vehicle to computer memory that is remotely-located with respect to said vehicle; and
providing a notification of a state of charge condition based on the wirelessly transmitted estimated reduction in the state of charge of the battery.
12. The process according to claim 8 wherein a least-square curve fitting method is employed in solving the set of linear equations.
13. A process for evaluating parasitic load on an electrical storage battery of a vehicle having an engine and an on-board electrical storage battery, said vehicle operative in alternating engine-on and engine-off states, comprising:
for each of a plurality of vehicles:
measuring a plurality of vehicle parameters during engine-on and engine-off states;
wirelessly transmitting said plurality of vehicle parameters from said vehicle to computer memory that is remotely-located with respect to said vehicle;
remotely determining an estimate of the parasitic load on the battery based on the wirelessly transmitted plurality of vehicle parameters; and
wherein the estimate of the parasitic load on the battery is determined based upon: (1) a difference between a first estimate of a state of charge of the battery at a first time substantially contemporaneously with the beginning of one of the engine off-states and a second estimate of a state of charge of the battery at a second time subsequent just prior to the beginning of the engine on-state that immediately follows said prior engine-off state, and (2) an elapsed time between said first and second times;
wherein remotely determining an estimate of the parasitic load on the battery based on the wirelessly transmitted plurality of vehicle parameters comprises:
formatting the respective wirelessly transmitted plurality of vehicle parameters into a plurality of eciuations, each having the form:
δ=( S off −S on )× C=P×T+ε
in which S off is the respective estimate of the state of charge of the battery substantially contemporaneous with the beginning of an engine-off state, and S on is the respective estimate of the state of charge of the battery substantially just prior to the beginning of the engine-on state that immediately follows the engine-off state, C is the battery reserved capacity in ampere*hours, P is the estimated parasitic load, T is an elapsed time between S off and S on , δ is state of charge loss of the battery during the elapsed time T, and ε is a constant, so as to provide a set of linear equations of the form:
[
δ
1
δ
2
⋮
δ
M
]
=
[
t
1
1
t
2
1
⋮
⋮
t
M
1
]
·
[
P
ɛ
]
in which Mis the number of observations, t i (i=1 . . . M) is the instance of elapsed time and δ i (i =1 . . . M) is the instance of state of charge loss; and
solving the set of linear equations to provide a statistical estimation of the parasitic load across the plurality of vehicles.
14. The process according to claim 13 further including providing a notification of excessive parasitic load on the battery.
15. The process according to claim 13 further comprising:
remotely determining an undesirable state of charge condition of the battery based on the wirelessly transmitted plurality of vehicle parameters; and
providing a notification of the undesirable state of charge condition based on the wirelessly transmitted estimated reduction in the state of charge of the battery.
16. The process according to claim 13 wherein a least-square curve fitting method is employed in solving the set of linear equations.