Battery and workstation monitoring system and display
An Asset Management System and a method for managing a fleet of assets is provided. The system is capable of determining use states and high-use periods of a fleet of mobile workstations. Use states are determined by sensors resident on mobile workstations, the sensors operable to detect the occurrence of a specified event. The Asset Management System is able to interpret data sent by the sensors and determine a type of use and use state for each mobile workstation based on the data or lack of data sent by the sensors. The Asset Management System is operable to determine periods of high-use across the fleet of mobile workstations. The Asset Management System is also operable to determine a return-on-investment level of each mobile workstation in the fleet and generate a heat map based on those levels.
1 . A mobile workstation comprising:
a cart movable between locations in a facility;
a transceiver configured to wirelessly send to and wirelessly receive data from, via a wireless network, at least one remote server;
a first battery of a fleet of batteries, wherein the first battery is disposed on the cart;
a first sensor configured to generate asset location data, wherein the asset location data includes a location of the mobile workstation; and
a second sensor configured to generate data relating to the fleet of batteries,
wherein the transceiver wirelessly sends the data relating to the fleet of batteries to the at least one remote server, and
wherein the at least one remote server performs the steps of:
receiving the asset location data and the data relating to the fleet of batteries; and
providing, to a user interface, and based on the asset location data and the data relating to the fleet of batteries, a recommendation for locating, as a replacement for the first battery, a replacement battery of the fleet of batteries when a battery characteristic of the first battery is below a threshold level dynamically updated based on a predictive decay curve, wherein the predictive decay curve is obtained by adjusting a known decay curve based on data collected from the fleet of batteries having a common use pattern with the first battery.
2 . The mobile workstation of claim 1 , wherein the data relating to the fleet of batteries includes data relating to one or more of a charge level of each battery of the fleet of batteries, a battery health level of each battery of the fleet of batteries, and a location of each battery of the fleet of batteries.
3 . The mobile workstation of claim 2 ,
wherein the at least one remote server further performs the step of making a first determination, based on the asset location data and the data relating to the location of the replacement battery, that the replacement battery is within a threshold proximity of the mobile workstation, and
wherein the recommendation is further based on the first determination.
4 . The mobile workstation of claim 3 ,
wherein the at least one remote server further performs the step of making a second determination, based on the data relating to the charge level of the first battery, that the charge level of the first battery is below a threshold level, and
wherein the recommendation is further based on the second determination.
5 . The mobile workstation of claim 4 ,
wherein the at least one remote server further performs the step of making a third determination, based on the data relating to the charge level of the replacement battery and the data relating to the charge level of the first battery, that the charge level of the replacement battery is greater than the charge level of the first battery, and
wherein the recommendation is further based on the third determination.
6 . The mobile workstation of claim 5 ,
wherein the at least one remote server further performs the steps of:
making a fourth determination, based on the asset location data and the data relating to the location of each battery of the fleet of batteries, that a plurality of batteries of the fleet of batteries is within the threshold proximity of the mobile workstation, wherein the plurality of batteries includes the replacement battery; and
making a fifth determination, based on the data relating to the charge level of the plurality of batteries, that the charge level of the replacement battery is the highest among the plurality of batteries,
wherein the recommendation is further based on the fourth determination and the fifth determination.
7 . The mobile workstation of claim 6 ,
wherein the at least one remote server further performs the step of updating a plurality of battery profiles based on the data relating to the fleet of batteries,
wherein each battery profile of the plurality of battery profiles corresponds to a battery in the fleet of batteries,
wherein each battery profile includes data relating to the battery charge level of the corresponding battery and data relating to the location of the corresponding battery,
wherein the at least one remote server further performs the step of displaying, via the user interface, a heat map,
wherein the heat map includes a plurality of battery representations displayed on the user interface including dynamically updated predictive battery health and charge level thresholds for a period of time from the current date,
wherein each battery representation corresponds to a battery profile of the plurality of battery profiles, and
wherein each battery representation is based on data included in the corresponding battery profile.
8 . A non-transitory computer readable medium having stored therein instructions that, when executed by a computing device, cause the computing device to perform functions comprising:
receiving, from a first sensor residing on a mobile workstation, asset location data, wherein the asset location data includes a location of the mobile workstation;
receiving, from a second sensor residing on the mobile workstation, data relating to a fleet of batteries, wherein the fleet of batteries includes a first battery residing on the mobile workstation; and
providing, to a user interface, and based on the asset location data and the data relating to the fleet of batteries, a recommendation for locating, as a replacement for the first battery, a replacement battery of the fleet of batteries when a battery characteristic of the first battery is below a threshold level dynamically updated based on a predictive decay curve, wherein the predictive decay curve is obtained by adjusting a known decay curve based on data collected from the fleet of batteries having a common use pattern with the first battery.
9 . The non-transitory computer readable medium of claim 8 , wherein the data relating to the fleet of batteries includes data relating to one or more of a charge level of each battery of the fleet of batteries, a battery health level of each battery of the fleet of batteries, and a location of each battery of the fleet of batteries.
10 . The non-transitory computer readable medium of claim 9 ,
wherein the instructions further cause the computing device to perform functions comprising making a first determination, based on the asset location data and the data relating to the location of the replacement battery, that the replacement battery is within a threshold proximity of the mobile workstation, and
wherein the recommendation is further based on the first determination.
11 . The non-transitory computer readable medium of claim 10 ,
wherein the instructions further cause the computing device to perform functions comprising making a second determination, based on the data relating to the charge level of the first battery, that the charge level of the first battery is below a threshold level, and
wherein the recommendation is further based on the second determination.
12 . The non-transitory computer readable medium of claim 11 ,
wherein the at least one remote server further performs the step of making a third determination, based on the data relating to the charge level of the replacement battery and the data relating to the charge level of the first battery, that the charge level of the replacement battery is greater than the charge level of the first battery, and
wherein the recommendation is further based on the third determination.
13 . The non-transitory computer readable medium of claim 12 ,
wherein the instructions further cause the computing device to perform functions comprising:
making a fourth determination, based on the asset location data and the data relating to the location of each battery of the fleet of batteries, that a plurality of batteries of the fleet of batteries is within the threshold proximity of the mobile workstation, wherein the plurality of batteries includes the replacement battery; and
making a fifth determination, based on the data relating to the charge level of the plurality of batteries, that the charge level of the replacement battery is the highest among the plurality of batteries, and
wherein the recommendation is further based on the fourth determination and the fifth determination.
14 . The non-transitory computer readable medium of claim 13 ,
wherein the instructions further cause the computing device to perform functions comprising updating a plurality of battery profiles based on the data relating to the fleet of batteries,
wherein each battery profile of the plurality of battery profiles corresponds to a battery in the fleet of batteries,
wherein each battery profile includes data relating to the battery charge level of the corresponding battery and data relating to the location of the corresponding battery,
wherein the instructions further cause the computing device to perform functions comprising displaying, via the user interface, a heat map,
wherein the heat map includes a plurality of battery representations displayed on the user interface including dynamically updated predictive battery health and charge level thresholds for a period of time from the current date,
wherein each battery representation corresponds to a battery profile of the plurality of battery profiles, and
wherein each battery representation is based on data included in the corresponding battery profile.
15 . A method of managing a fleet of mobile workstations in a facility, comprising:
sensing, by a first sensor residing on a mobile workstation, asset location data, wherein the asset location data includes a location of the mobile workstation;
sensing, by a second sensor residing on the mobile workstation, data relating to a fleet of batteries, wherein the fleet of batteries includes a first battery residing on the mobile workstation;
transmitting, by a transceiver residing on the mobile workstation and operably coupled to the first sensor and the second sensor, to a remote server a data packet, wherein the data packet includes the asset location data and the data relating to the fleet of batteries; and
providing, by the remote server to a user interface, and based on the asset location data and the data relating to the fleet of batteries, a recommendation for locating, as a replacement for the first battery, a replacement battery of the fleet of batteries when a battery characteristic of the first battery is below a threshold level dynamically updated based on a predictive decay curve, wherein the predictive decay curve is obtained by adjusting a known decay curve based on data collected from the fleet of batteries having a common use pattern with the first battery.
16 . The method of claim 15 , wherein the data relating to the fleet of batteries includes data relating to one or more of a charge level of each battery of the fleet of batteries, a battery health level of each battery of the fleet of batteries, and a location of each battery of the fleet of batteries.
17 . The method of claim 16 , further comprising making, by the remote server, a first determination that the replacement battery is within a threshold proximity of the mobile workstation,
wherein the first determination is based on the asset location data and the data relating to the location of the replacement battery, and
wherein the recommendation is further based on the first determination.
18 . The method of claim 17 , further comprising making, by the remote server, a second determination that the charge level of the first battery is below a threshold level,
wherein the second determination is based on the data relating to the charge level of the first battery, and
wherein the recommendation is further based on the second determination.
19 . The method of claim 18 , further comprising making, by the remote server, a third determination that the charge level of the replacement battery is greater than the charge level of the first battery,
wherein the third determination is based on the data relating to the charge level of the replacement battery and the data relating to the charge level of the first battery, and
wherein the recommendation is further based on the third determination.
20 . The method of claim 19 , further comprising:
making, by the remote server, a fourth determination that a plurality of batteries of the fleet of batteries is within the threshold proximity of the mobile workstation, wherein the plurality of batteries includes the replacement battery; and
making, by the remote server, that the charge level of the replacement battery is the highest among the plurality of batteries,
wherein the fourth determination is based on the asset location data and the data relating to the location of each battery of the fleet of batteries,
wherein the fifth determination is based on the data relating to the charge level of the plurality of batteries, and
wherein the recommendation is further based on the fourth determination and the fifth determination.