Abstract: A materials handling vehicle includes a power unit with a traction motor controller coupled to a traction motor that drives at least one steered wheel of the materials handling vehicle, and a feature assistance system comprising a remote-control receiver pairs with a wireless remote-control device. An information linking device of the vehicle wirelessly communicates to a remote server computer.

Abstract: A process for implementing a materials handling vehicle feature monitor includes receiving wirelessly, from a fleet of materials handling vehicles, electronic vehicle records. Each electronic vehicle record comprises travel-related data recorded by an associated materials handling vehicle and an operator identification of the corresponding operator of the vehicle. The vehicle records are parsed for each operator to extract dashboard data, which can include a travel distance that the materials handling vehicle has traveled. An expected travel distance under remote control to total travel distance for the predetermined period of time is established. For each operator, an electronic measurement of the expected travel distance under remote control to total travel distance for the predetermined period of time compared to the recorded travel distance under remote control to total travel distance for the predetermined period of time is generated.

Abstract: Charging initiation methods for flow batteries, controllers for flow batteries and flow battery systems are disclosed. A charging initiation method for a flow battery which allows charging of the flow battery from an AC power supply with a power converter having a minimum operational DC voltage is described. Details of the flow battery construction are provided. The charging initiation method so described includes a pre-charging initiation condition that varies depending on a DC voltage threshold or a current threshold.

Abstract: A process for implementing a materials handling vehicle feature monitor includes receiving wirelessly, from a fleet of materials handling vehicles, electronic vehicle records. Each electronic vehicle record comprises travel-related data recorded by an associated materials handling vehicle and an operator identification of the corresponding operator of the vehicle. The vehicle records are parsed for each operator to extract dashboard data, which can include a travel distance that the materials handling vehicle has traveled. An expected travel distance under remote control to total travel distance for the predetermined period of time is established. For each operator, an electronic measurement of the expected travel distance under remote control to total travel distance for the predetermined period of time compared to the recorded travel distance under remote control to total travel distance for the predetermined period of time is generated.

Abstract: A materials handling vehicle technology monitor receives wirelessly, from a fleet of materials handling vehicles, electronic vehicle records. Each electronic vehicle record includes technology feature data recorded by a controller on an associated materials handling vehicle. Typically, the electronic vehicle record is generated in response to a corresponding technology feature on the materials handling vehicle being operated in a work environment. Moreover, each electronic vehicle record includes an operator identification of an operator of the materials handling vehicle at the time the technology feature data is recorded. The monitor also generates for each operator, an electronic measurement based upon a comparison of expected technology feature usage, e.g., a threshold, compared to the technology feature data in the received electronic vehicle records, which are associated with the corresponding operator.

Abstract: Flow battery systems and control methods are disclosed. The flow battery includes components such as positive electrolyte tank, a negative electrolyte tank, a primary stack having a positive porous electrode and a negative porous electrode separated by a membrane, a power bus linked to the electrodes, an electrolyte pump and an auxiliary stack, etc. The power load on a power bus is monitored and as required pumps are activated if the detected power load on the power bus is greater than a first threshold power load. One of the pumps are deactivated if the detected power load on the power bus is less than a second threshold power load which is lower than the first threshold power load.

Abstract: Systems and methods for generating regressors based on data sparsity using a machine learning (ML) model are described. A system is configured to provide a plurality of time series datasets to a recurrent neural network (RNN) of a machine learning (ML) model. The RNN generates one or more outputs associated with one or more time series datasets, and the system provides a first portion and a second portion of the one or more outputs to a regressor layer and a classification layer of the ML model, respectively. The regressor layer generates one or more regressors for the one or more time series datasets, and the classification layer generates one or more classifications associated with the one or more regressors (with each indicating whether an associated regressor is valid). Whether a classification indicates a regressor is valid may be based on time series data sparsity.

Abstract: A materials handling vehicle technology monitor receives wirelessly, from a fleet of materials handling vehicles, electronic vehicle records. Each electronic vehicle record includes technology feature data recorded by a controller on an associated materials handling vehicle. Typically, the electronic vehicle record is generated in response to a corresponding technology feature on the materials handling vehicle being operated in a work environment. Moreover, each electronic vehicle record includes an operator identification of an operator of the materials handling vehicle at the time the technology feature data is recorded. The monitor also generates for each operator, an electronic measurement based upon a comparison of expected technology feature usage, e.g., a threshold, compared to the technology feature data in the received electronic vehicle records, which are associated with the corresponding operator.