Cps Technology Holdings LLC

Johnson Controls Technology Company

Johnson Controls Technology LLC

Johnson Controls Advanced Power Solutions Gmbh

Cps Technology Services LLC

Clarios Advanced Solutions Gmbh

Zhihong H Jin

Zhenli Zhang

Perry M Wyatt

Timur L Aliyev

Brian Christopher Sisk

Mark R Johnson

Steven G Rinaldo

Miaomiao Ma

Kem M Obasih

Christian Kuper

Ronald J Dulle

Qingzhi Guo

Michael R Blemberg

Matthew D Elberson

Douglas G Grimsley

Tom M Watson

Diego Hernan Diaz Martinez

Steven J Wood

David R Boone

Kathryn Marie Ciurlik

Jason David Searl

Michael E Iverson

Deepan Chakkaravarthi Bose

Raul Jacinto Arredondo Cardenas

Uday S Kasavajjila

Renate Witt

Austin Bellile

Logan Crain

Michael Thomas Ballo

Mark Johnson

Diego HD Martinez

Matthew Elberson

A system and method for selecting a battery (e.g., a battery selector) is disclosed. The example battery selector includes a plurality of battery factors, a plurality of electrical load factors, a plurality of cycling or crank data, and an output. The output includes a battery selection based on the plurality of battery factors, plurality of vehicle loads, and the plurality of cycling or crank data.

System and method for battery selection

Disclosed is a vehicle comprising a vehicle system having a system having a number of loads defining a load profile; a validated battery comprising one or more batteries which can fulfill the load profile; an integrated battery selected from the validated battery, the integrated battery selected for longevity relative to other batteries; wherein the validated battery is provided within the vehicle. Further disclosed is a battery longevity predictor comprising a plurality of battery factors; a plurality of electrical load factors; a plurality of cycling or crank data; an output; wherein the output comprises a battery longevity predictor based on the plurality of battery factors, plurality of vehicle loads, and the plurality of cycling or crank data.

A battery system includes a lithium ion battery that couples to an electrical system. The battery system also includes a battery management system that electrically couples to the lithium ion battery and controls one or more recharge parameters of the lithium ion battery. Additionally, the battery management system monitors one or more parameters of the lithium ion battery. Further, the battery management system controls the recharge parameters of the lithium ion battery based on at least one lithium plating model and the monitored parameters. Furthermore, the at least one lithium plating model indicates a relationship between the one or more parameters of the lithium ion battery and a likelihood of lithium plating occurring in the lithium ion battery.

Battery module lithium plating reduction

A battery system includes a lithium ion battery configured to couple to an electrical system, and a battery management system configured to electrically couple to the lithium ion battery and to control one or more recharge parameters of the lithium ion battery. The battery management system is programmed with an electrochemical model, and the battery management system is configured to monitor parameters of the lithium ion battery, and to control the one or more recharge parameters of the lithium ion battery based on the electrochemical model and the one or more monitored parameters. The electrochemical model determines lithium plating reaction kinetics at an anode of the lithium ion battery, determines a quantity of plated lithium at the anode of the lithium ion battery, or both, and indicates a relationship between the one or more monitored parameters and the lithium plating reaction kinetics, the quantity of plated lithium, or both.

State of charge dependent plating estimation and prevention

Systems and methods for improving operation of an automotive battery system including an automotive electrical system comprising a battery system that uses operational parameters, predicted internal resistance of a battery expected over a prediction horizon, and real-time internal resistance of a battery to increase performance and reliability. The battery system includes a battery electrically coupled to electrical devices in the automotive system, sensors coupled to the battery that determine terminal voltage of battery, and a battery control system communicatively coupled to sensors. The battery control system determines a charging power limit used to control supply of electrical power to the battery when charging the battery, based on predicted internal resistance when measured terminal voltage of the battery is not greater than a lower voltage threshold and based on a real-time internal resistance of the battery when the measured terminal voltage of the battery is greater than the lower voltage threshold.

Model predictive battery power limit estimation systems and methods

One embodiment of the present disclose describes systems and methods responsible for reducing errors in a battery model used in the operation of a battery control system. The battery control system may operate based on a modeled response of the battery derived from the battery model. If the battery model is not calibrated/validated, errors in the battery model may propagate through the modeled response of the battery to the operation of the battery control system. A calibration current pulse may result in a different measured response of the battery than the modeled response of the battery to the same calibration current pulse. A validation technique, which uses a difference between the modeled response and the measured response of the battery to the calibration current pulse as a method to calibrate the battery model, may protect the battery control system from the contribution of errors from an uncalibrated battery model.

Battery model and control application calibration systems and methods

Embodiments describe a battery system that includes a first battery module coupled to a regenerative braking system and a control module that controls operation of the battery system by: determining a predicted driving pattern over a prediction horizon using a driving pattern recognition model based in part on a battery current and a previous driving pattern; determining a predicted battery resistance of the first battery module over the prediction horizon using a recursive battery model based in part on the predicted driving pattern, the battery current, a present bus voltage, and a previous bus voltage; determining a target trajectory of a battery temperature of the first battery module over a control horizon using an objective function; and controlling magnitude and duration of electrical power supplied from the regenerative such that a predicted trajectory of the battery temperature is guided toward the target trajectory of the battery temperature during the control horizon.

Cooling strategy for battery systems

An electrical system having a battery system and a control system. The battery system may include a first switching device, a first battery electrically coupled in series with the first switching device, a second battery electrically coupled in parallel with the first switching device and the first battery when the first switching device is in a closed position, and a second switching device coupled in series with a load. The control system may perform diagnostics on the battery system.

Battery electromechanical switching device diagnostics system and methods

Systems and methods for improving operation of an automotive battery system including an automotive electrical system comprising a battery system that uses operational parameters. predicted internal resistance of a battery expected over a prediction horizon, and real-time internal resistance of a battery to increase performance and reliability. The battery system includes a battery electrically coupled to electrical devices in the automotive system, sensors coupled to the battery that determine terminal voltage of battery, and a battery control system communicatively coupled to sensors. The battery control system determines a charging power limit used to control supply of electrical power to the battery when charging the battery, based on predicted internal resistance when measured terminal voltage of the battery is not greater than a lower voltage threshold and based on a real-time internal resistance of the battery when the measured terminal voltage of the battery is greater than the lower voltage threshold.

Method predictive battery power limit estimation systems and methods

A module-based framework evaluates designs of advanced start stop systems, particularly 12 V advanced start stop systems. The framework separates vehicle and battery analysis and uses a power profile to evaluate different designs of the vehicles and batteries. Particularly, the framework can evaluate different battery solutions and compare performances as a function of drive cycles, motor size, and electrical loads. In addition to modeling, actual batteries are tested for the same power inputs for validating performance differences. This framework identifies performance limiting components for determination of the vehicle system component optimization.

Battery simulation module, and battery analysis tool for determining a battery solution using a battery simulation module

An electrical system comprising a battery system and a control system is disclosed. The battery system may comprise a first switching device, a first battery electrically coupled in series with the first switching device, a second battery electrically coupled in parallel with the first switching device and the first battery when the first switching device is in a closed position, and a second switching device coupled in series with a load. The control system may be configured to perform diagnostics on the battery system.

Growing community of inventors

Pewaukee, WI, United States of America

Zhihong H Jin