Sf Motors, Inc.

Chongqing Jinkang Powertrain New Energy Co., Ltd.

Other

Chongqing Jinkang New Energy Automobile Co., Ltd.

Chongqing Jinkang Energy Vehicle Co., Ltd.

Saeed Khaleghi Rahimian

Ying Liu

Yifan Tang

Sangwoo Han

Mehdi Forouzan

Wenke Zhang

Jun Hou

Junzhe Shi

Brennan Campbell

Chen-Yen Yu

Min Tian

Joseph Tolentino

Embodiments described herein generally relate to the modification of State of Charge (SoC) calculations within electric vehicles (EVs). A database of data points may be generated based on characteristics of a battery cell at various measured SoCs within a controlled environment. Subsequently, during the operation of an EV, a battery management system (BMS) within the EV may collect various operating data points. The collected operating data points may be utilized to reference similar data points stored in the database in order to determine an SoC value. The SoC value may be utilized to modify or alter the SoC calculations by the BMS for an EV in operation.

State of charge error estimation of battery cells background

A physics-based calendar life model for determining the state of health of a lithium ion battery cell. The model accounts for parasitic reactions on anode and cathode particles to accurately determine degradation of a battery. By incorporating electrolyte decomposition in a cathode, the present calendar model can predict capacity retention as well as the substantial rise of cell resistance at high state of charge (SOC) and temperatures. The present calendar model is a simple algorithm, utilizing only three parameters, and determines the capacity retention and resistance rise based on temperature, SOC and time.

Control-oriented physics-based calendar life model for lithium ion cells

An automatically generated and customized fast charging process results in reduced degradation in the battery cell. An algorithm for a particular battery cell profile is automatically generated and customized to minimize degradation due to fast charging for that particular batch. To generate the custom algorithm, battery cell information is retrieved for a profile of a battery, wherein each battery profile may have a particular manufacturer, model, type, electrode batch, and potentially other specific identification information. Each battery cell is charged from a particular SOC level and at a selected C-rate, and then discharged. During discharge, the battery cell is monitored for detection of lithium plating or other undesirable effects. A lookup table is automatically generated from the battery cell information, and can be provided to devices and/or battery management systems. The BMS then uses the lookup table to apply a charging process that is customized to the on-board battery.

Serial SOC testing for improved fast-charge algorithm

Systems and methods to manage battery cell degradation are provided. The system determines anode stoichiometry bounds in full-cell and cathode stoichiometry bounds in full-cell. The system measures a cell open circuit voltage subsequent to a rest duration. The system identifies, via a data fitting technique and the cell open circuit voltage, a change in the anode stoichiometry bounds in full-cell and a change in the cathode stoichiometry bounds in full-cell. The system determines a total cyclable lithium ion for the battery cell, an amount of anode active material and an amount of anode cathode material. The system determines a primary capacity fade mechanism for the battery cell. The system generates a battery health indicator, selects a command, and provides the command to manage power consumption from the battery cell.

Systems and methods of managing battery cell degradation

A multiple particle reduced order model to adjust charging applied to a load based on accurately predicting lithium plating potential in real time during the life of a lithium battery cell. In the current multi-particle reduced order modeling system, the current density and the potential distributions are solved iteratively. Once the current distribution is solved, lithium concentration distribution is solved without involving any iterative process. By solving the lithium concentration distribution as a separate step after the iteratively determined current density and potential distributions, the computation time required by the model to generate an output is dramatically reduced by avoiding solving multiple partial derivative equations iteratively. Based on the potential distribution information provided by the output of the model, lithium plating potential can be determined, and actions can be taken, such as modified charging techniques and rates, to minimize future lithium plating.

Continuous derating fast charging method based on multiple particle reduced order model

Apparatuses, systems, and methods of storing electrical energy for electric vehicles are provided. A battery pack can be disposed in an electric vehicle to power the electric vehicle. A battery cell can be arranged in the battery pack. The battery cell can include a housing. The housing can define a cavity within the housing. The cavity of the battery cell can include a separator having a first side and a second side, a cathode disposed along the first side of the separator, and an anode disposed along the second side of the separator. The anode can include a first portion adjacent to the second side of the separator, and a second portion adjacent to the first portion and separated from the separator by the first portion. A porosity of the first portion of the anode can be greater than a porosity of the second portion of the anode.

Electric vehicle lithium batteries having non-uniform anode layer

Various arrangements for determining the state of health (SOH) of a battery of an electric vehicle are presented. A first OCV of the battery of the electric vehicle may be determined. Coulomb counting on the battery of the electric vehicle may be performed to determine an accumulated capacity value following the first OCV being determined. A second OCV of the battery of the electric vehicle may be determined after performing the coulomb counting. A change in OCV value may be calculated using the determined first OCV and the determined second OCV. A slope value may be determined by dividing the accumulated capacity value by the change in OCV value. The SOH of the battery may then be determined using the slope value.

Battery state of health estimation using open circuit voltage slope

Battery health is determined by estimating a battery anode capacity, battery cathode capacity, and cyclable lithium capacity. Determining electrode and lithium capacity may include determining anode and cathode open cell voltages (OCV) at the beginning of battery life, and estimating the full cell OCV from the individual (half-cell) OCV values. Once the beginning of battery life information is known, the state of charge (SOC) for a battery is measured during battery life. The SOC measurements may be captured at a plurality of different levels. The cathode capacity, anode capacity, and cycle mobile lithium capacity are then determined from the beginning of life OCV data and plurality of SOC data. The capacities can be used to detect degradation, and a battery management system can take steps to reduce further degradation based on the capacity values.

Battery state of health estimation by tracking electrode and cyclable lithium capacities

A method for controlling battery temperature during a charge cycle includes representing a battery cell as a plurality of individual battery cells such that each representation of the individual battery cells can be simulated individually; for each of the individual battery cells, calculating one or more simulated responses during a charge cycle based at least in part on one or more values indicative of a charge rate; determining an optimal input temperature profile for the charge rate that minimizes differences between simulated responses for each of the plurality of individual battery cells; and providing the optimal input temperature profile for control of a temperature management system that monitors and controls a temperature of the battery cell.

Optimizing a temperature profile in a thermal management system of an electric vehicle

A battery management system that uses a multiple particle reduced order model to manage battery performance of a vehicle, such as an electric vehicle or hybrid electric vehicle is provided. The system can receive a value of a current output and determine, via a multi-particle model, a local current distribution that converges. The system can determine, via the multi-particle model and subsequent to determination of the local current distribution that converges, a concentration distribution. The system can determine, based on the local current distribution, a value of a voltage of the battery. The system can determine, based on the value of the voltage of the battery and the concentration distribution, a temperature of the battery. The system can generate, based on the value of the voltage of the battery or the temperature of the battery, a command to manage a performance of the battery.

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Santa Clara, CA, United States of America

Saeed Khaleghi Rahimian