Bid Document and Quotation Preparation for Battery PACK and Energy Storage System Integration: Forms and Interactions

Data sources for this category include cell grading data, MES systems, welding equipment, torque tools, BMS software version records, and EOL test

What data for this category looks like

Data sources for this category include cell grading data, MES systems, welding equipment, torque tools, BMS software version records, and EOL test bench test data. Update frequency is once per battery pack or energy storage cabinet, with synchronization whenever the BMS software version changes. Daily documents include grouping rules, BOM lists, welding and torque records, BMS flashing records, insulation and voltage withstand reports, EOL test reports, and system configuration lists. Fields include cell capacity (Ah), welding torque (N·m), insulation resistance (MΩ), BMS version number, test voltage (V), and others, with units fixed per parameter type.

What constraints these characteristics impose on the forms and interactions workflow

Scattered data sources linked to multiple devices and test stages require forms to support bulk import of multi-source data and field mapping, to avoid manual entry errors. High update frequency with each update tied to a single device requires forms to support creating entries per pack or per cabinet, to meet small-batch, high-frequency update needs. Complex document structures and fields with fixed units require forms to pre-set unit options, to reduce input errors. Bid quotations must be linked to grouping rules and test data, so the interaction flow must support field linkage. For example, automatically match the corresponding grouping logic when cell capacity changes, to reduce verification costs.

How to set the configuration

Configuration ItemRecommended SetupRationale
formInputBind fields such as cell capacity (Ah) and welding torque (N·m), and pre-set unit optionsAdapt to the fixed unit requirements of fields for this category, reducing manual input errors
userSelectProvide drop-down options for grouping rules and BMS version number, with no more than 20 options totalMatch the fixed rules and version entries in documents, avoiding selection difficulties caused by too many options
Bulk Import SettingsSupport CSV format imports, with a single import limit of 50 entriesAdapt to bulk entry needs for multi-source test data, reducing manual entry costs
Field Linkage ConfigurationAutomatically load the corresponding grouping logic when cell capacity changesLink grouping rules and test data, reducing manual verification steps
Timeout Threshold60 secondsAdapt to the processing duration of multi-field verification and data mapping, avoiding submission failures

This configuration is based on FastGPT v4.17.0.

Three common configuration errors

  • Phenomenon: After configuring formInput to bind fields, field values are not correctly carried into the bid document template when the form is submitted. Cause: Field and template mapping configuration was not enabled, so the binding relationship did not take effect.
  • Phenomenon: After configuring a third-party data interface, the form's drop-down options do not display the fields from the corresponding data source. Cause: The data source association rules for userSelect were not set correctly, leading to a mismatch between options and configuration.
  • Phenomenon: When importing EOL test data, some numeric fields do not automatically adapt to units. Cause: Unit binding rules for fields were not pre-configured, leading to data format mismatches.

How to confirm the configuration is correct

  • Create a new single-pack form, fill in the preset fields and submit, then check if the document template automatically populates the corresponding content.
  • After configuring a third-party data source, open the form's drop-down options and confirm that the displayed fields match the data source configuration.
  • Import test-format CSV data and check that fields are correctly mapped and not missing.
  • Modify a field value and check that the linkage logic triggers the corresponding rules.

The values provided here are common starting points and should be evaluated against the user’s own samples.