IP Library › Granted Patent US 12,372,585
Granted Patent B2
US 12,372,585 · App. 18/118,231 · Granted Jul 29, 2025

Neural-network state-of-charge and state of health estimation

Inventors: Ephram Chemali (Ontario, CA); Matthias Preindl (New York, NY)
Assignees: The Trustees of Columbia University in the City of New York; McMaster University
G01R31/392G01R31/382H01M10/486H02J7/0048H02J7/005H02J7/007192G06N3/045G06N3/08G06N20/20
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,372,585
App. No.
18/118,231
Granted
Jul 29, 2025
Kind
B2
Abstract

An approach to control or monitoring of battery operation makes use of an artificial neural network (ANN), which receives one or more battery attributes for a Lithium ion (Li-ion) battery, and determines, based on the received one or more battery attributes, a state-of-charge (SOC) and/or a state-of-health (SOH) estimate for the Li-ion battery. The ANN includes at least one of a recurrent neural network (RNN) and a convolutional neural network (CNN), and the series of values of the battery attributes includes at one of battery voltage values, battery current values, and battery temperature values.

Claims (58)

1. A method comprising:

receiving, by a trained model, a time series of values of battery attributes for a battery that are determined based on measurements performed by one or more sensors monitoring the battery,

wherein the battery attributes include:

an ambient temperature, and

one or both of a battery voltage and a battery current, and

wherein a portion of the time series of values of battery attributes represents battery attributes of the battery during a charging cycle and another portion of the time series of values of battery attributes of the battery represents battery attributes of the battery during a discharging cycle; and

generating, by the trained model, a battery state for the battery based on the time series of values, the battery state including a state of health (SOH) of the battery,

wherein the trained model is a single, processor-based trained model that is trained to generate battery states for the battery during charging cycles of the battery and during discharging cycles of the battery.

2. The method of claim 1 , wherein the trained model is a convolutional neural network (CNN).

3. The method of claim 1 , wherein the battery attributes further include a state-of-charge (SOC) of the battery and wherein receiving, by the trained model, the time series of values of battery attributes includes receiving a time series of values of the SOC of the battery.

4. The method of claim 3 , further comprising:

determining, using a recurrent neural network (RNN), the time series of values of the SOC based on the battery voltage and the battery current; and

providing the time series of values of the SOC to the trained model as one of the time series of values of battery attributes received by the trained model.

5. The method of claim 1 , wherein the battery state generated by the trained model based on the time series of values further includes a state-of-charge (SOC) of the battery.

6. The method of claim 1 , wherein the SOH represents a capacity of the battery.

7. The method of claim 1 , further comprising:

providing the battery state for controlling operation of the battery or an electrical apparatus coupled to the battery.

8. The method of claim 1 , wherein the battery comprises a rechargeable battery, and wherein receiving the time series of values for the one or more battery attributes includes receiving a time series of values spanning multiple charging and discharging cycles of the rechargeable battery.

9. The method of claim 1 , wherein generating the battery state includes determining the battery state at a first time dependent on the time series of values of the battery attributes spanning greater than 30 minutes of operation of the battery.

10. The method of claim 1 , further comprising:

setting values of parameters of the trained model based on processing a plurality of time series of values of the battery attributes and corresponding battery states, the plurality of time series of values spanning a plurality of different state-of-charge profiles.

11. The method of claim 1 , wherein the trained model is a single neural network.

12. The method of claim 1 ,

wherein the generating, by the trained model, of battery state for the battery comprises: generating, by the trained model when the battery is discharging, based on the time series of values, the battery state for the battery when the battery is discharging, and wherein

the method further comprises:

receiving, by the trained model, a further time series of values of the battery attributes for the battery; and

generating, by the trained model when the battery is charging, based on the received further time series of values, a further battery state for the battery when the battery is charging.

13. A battery monitoring system comprising:

one or more sensors configured to monitor a battery; and

a processor coupled to the one or more sensors, the processor configured to:

receive the sensor data from the one or more sensors;

receive, at a trained model implemented by the processor, a time series of values of battery attributes for the battery that are determined based on measurements performed by the one or more sensors,

wherein the battery attributes include:

an ambient temperature, and

 one or both of a battery voltage and a battery current, and

wherein a portion of the time series of values of battery attributes represents battery attributes of the battery during a charging cycle and another portion of the time series of values of battery attributes of the battery represents battery attributes of the battery during a discharging cycle; and

generate, via the trained model, a battery state for the battery based on the time series of values, the battery state including a state of health (SOH) of the battery,

wherein the trained model is a single, processor-based trained model that is trained to generate battery states for the battery during charging cycles of the battery and during discharging cycles of the battery.

14. The system of claim 13 , wherein the trained model is a convolutional neural network (CNN) comprising a plurality of layers, including at least one pooling layer and at least one fully connected layer.

15. The system of claim 13 , wherein the time series of values of the battery attributes received by the trained model further include a time series of values of the state of charge (SOC) of the battery.

16. The system of claim 15 , wherein the processor is further configured to:

determine, using a recurrent neural network (RNN) implemented by the processor, the time series of values of the SOC based on the battery voltage and the battery current; and

provide the time series of values of the SOC to the trained model as one of the time series of values of battery attributes received by the trained model.

17. The system of claim 13 , wherein the battery state generated by the trained model based on the time series of values further includes a state-of-charge (SOC) of the battery.

18. The system of claim 13 , wherein the battery comprises a rechargeable battery, and wherein the time series of values for the one or more battery attributes includes values spanning multiple charging and discharging cycles of the rechargeable battery.

19. A non-transitory computer readable media programmed with instructions, executable on one or more processors, to:

receive, by a processor-based trained model, a time series of values of battery attributes for a battery that are determined based on measurements performed by one or more sensors monitoring the battery,

wherein the battery attributes include:

an ambient temperature, and

one or both of a battery voltage and a battery current, and

wherein a portion of the time series of values of battery attributes represents battery attributes of the battery during a charging cycle and another portion of the time series of values of battery attributes of the battery represents battery attributes of the battery during a discharging cycle; and

generate, by the trained model, a battery state for the battery based on the time series of values, the battery state including a state of health (SOH) of the battery,

wherein the trained model is a single, processor-based trained model that is trained to generate battery states for the battery during charging cycles of the battery and during discharging cycles of the battery.

20. The non-transitory computer readable media of claim 19 , wherein the trained model is a convolutional neural network (CNN) comprising a plurality of layers, including at least one pooling layer and at least one fully connected layer.

21. The non-transitory computer readable media of claim 19 , wherein the time series of values of the battery attributes received by the trained model further include a time series of values of the state of charge (SOC) of the battery.

22. The non-transitory computer readable media of claim 21 , wherein the instructions, executable on the one or more processors, are further to:

determine, using a recurrent neural network (RNN), the time series of values of the SOC based on the battery voltage and the battery current; and

provide the time series of values of the SOC to the trained model as one of the time series of values of battery attributes received by the trained model.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 13, 2023
From: PREINDL, MATTHIAS
To: THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK
Reel/Frame 065861/0405 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 13, 2023
From: CHEMALI, EPHRAM
To: MCMASTER UNIVERSITY
Reel/Frame 065861/0514 →
Continuity (5)
Continuation 16688260 · Nov 19, 2019
Continuation In Part 16194596 · Nov 19, 2018
Provisional Application 62769039 · Nov 19, 2018
Provisional Application 62588510 · Nov 20, 2017
Related Publication 20230280410A1 · Sep 7, 2023
References Cited (81)
US 8680815B2 · Wang et al. · 2014 [cited by applicant]
US 8719195B2 · Frisch et al. · 2014 [cited by applicant]
US 11171498B2 · Chemali et al. · 2021 [cited by applicant]
US 11637331B2 · Chemali · 2023 [cited by examiner]
US 20080234956A1 · Mizuno · 2008 [cited by examiner]
US 20090326841A1 · Zhang et al. · 2009 [cited by applicant]
US 20100324848A1 · Cho · 2010 [cited by examiner]
US 20120271578A1 · Nareid · 2012 [cited by examiner]
US 20130119921A1 · Choe · 2013 [cited by examiner]
US 20140092375A1 · Raghavan · 2014 [cited by examiner]
US 20160209472A1 · Chow · 2016 [cited by examiner]
US 20170023649A1 · You et al. · 2017 [cited by applicant]
US 20180129893A1 · Son · 2018 [cited by examiner]
US 20180136285A1 · You · 2018 [cited by examiner]
US 20190157891A1 · Chemali et al. · 2019 [cited by applicant]
US 20200081070A1 · Chemali et al. · 2020 [cited by applicant]
CN 104881001A · 2015 [cited by applicant]
EP 2762907A1 · 2014 [cited by applicant]
WO 2014119123A1 · 2014 [cited by applicant]
Roscher et al., “Dynamic electric behavior and open-circuit-voltage modeling of LifePO4-based lithium ion secondary batteries,” Journal of Power Sources, vol. 196, pp. 331-336, 2011. [cited by applicant]
Sepasi et al., “Improved extended Kalman filter for state of charge estimation of battery pack,” Journal of Power Sources, vol. 255, pp. 368-376, Jun. 2014. [cited by applicant]
Sun et al., “Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles,” Energy, vol. 36, No. 5, pp. 3531-3540, May 2011. [cited by applicant]
Sutskever, “Training Recurrent Neural Networks,” Ph.D. dissertation, University of Toronto, 2013. [cited by applicant]
Tong, “Battery state of charge estimation using a load-classifying neural network,” Journal of Energy Storage, vol. 7, pp. 236-243, Aug. 2016. [cited by applicant]
United States Environmental Protection Agency, Vehicle and Fuel Emissions Testing, https://epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-driveschedules#vehicleDDS, (2017). [cited by applicant]
Waag et al., “Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles,” Journal of Power Sources, vol. 258, pp. 321-339, 2014. [cited by applicant]
Wang et al., “Comparison of Kalman Filter-based state of charge estimation strategies for Li-Ion batteries,” 2016 IEEE Transporation Electrification Conference and Expo (ITEC), pp. 1-6, 2016. [cited by applicant]
Wang et al., “Deep Learning for Identifying Metastatic Breast Cancer,” ArXiv Preprint, 1-6arXiv:1591239, 2016. [cited by applicant]
Weng et al., “State-of-health monitoring of lithium-ion battery models and packs via incremental capacity peak tracking,” Applied Energy, vol. 180, pp. 360-368, Oct. 2016. [cited by applicant]
World Energy Outlook Special Report: Energy and Air Pollution, International Energy Agency, pp. 1-6, Jun. 2016. [cited by applicant]
Zaremba et al., “Recurrent neural network reularization,” arXiv preprint arXiv: 1409.2329, Feb. 2015. [cited by applicant]
Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,” pp. 1-19, Nov. 2015. [cited by applicant]
Ahmed et al., “Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries Part 1: Parameterization Model Development for Healthy Batteries,” IEEE Journal of Em… [cited by applicant]
Ahmed et al., “Reduced-Order Electrochemical Model Parameters Identification and State of Charge Estimation for Healthy and Aged Li-Ion Batteries-Part II: Aged Battery Model and State of Charge Estimation,” in IEEE Jour… [cited by applicant]
Amodei et al., “Deep Speech 2: End-to-End Speech Recognition in English and Mandarin,” ArXiv e-prints, Dec. 2015. [cited by applicant]
Andre et al., “Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electricvehicals,” in Engeenering Applications of Artifi… [cited by applicant]
Anton et al., “Battery state-of-charge estimator using the SVM technique,” Applied Mathematical Modelling, vol. 37, No. 9, pp. 6244-6253, May 2013. [cited by applicant]
Anton et al., “Support Vector Machines Used to Estimate the Battery State of Charge,” IEEE Transactions on Power Electronics, vol. 28, No. 12, pp. 5919-5926, Dec. 2013. [cited by applicant]
Chang, Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA,: International Journal of Electrical Power & Energy Systems, vol. 53, pp. 60… [cited by applicant]
Charkhgard et al., “State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF,” in IEEE Transactions on Industrial Electronics, vol. 57, No. 12, pp. 4178-4187, Dec. 2010. [cited by applicant]
Chemali et al., “Electrochemical an delectrostatic energy storage and managment systems for electric drive vehicles: State-of-the-art review and future trends,” IEEE Journal of Emerging and Selected Topics in Power Elec… [cited by applicant]
Chen et al., “A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles,” Journal of Power Sources, vol. 246, pp. 667-678, 2014. [cited by applicant]
Ciresan et al., “Multi-Column Deep Neural Network for Traffic Sign Classification,” Neural Network, vol. 32, pp. 333-338, 2012. [cited by applicant]
Dahl et al., “Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, No. 1, pp. 30-42, Jan. 2012. [cited by applicant]
Deng et al., “Online available capacity predition and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery,” Energy, vol. 112, pp. 469-480, Oct. 2016. [cited by applicant]
Deng, “Deep Learning: Methods and Applications,” Foundations and Trends in Signal Processing, vol. 7, No. 3-4, pp. 197-387, 2014. [cited by applicant]
Dong et al., “Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter,” Journal of Power Sources, vol. 271, pp. 114-123, Dec. 2014. [cited by applicant]
Du et al., “State of charge estimation for Li-ion battery based on model from extreme learning machine,” Control Engineering Practice, vol. 26, pp. 11-19, May 2014. [cited by applicant]
Dubarry et al., “State of health battery estimator enabling degradation diagnosis: Model and algorithm description,” Journal of Power Sources, vol. 360, pp. 56-69, Aug. 2017. [cited by applicant]
Emadi, “Advanced Electric Drive Vehicles,” New York: CRC Press, 2015. [cited by applicant]
Ganguli et al., “Embedded fiber-optic sensing for accurate internal monitoring of cell state in advanced battery management systems part 2: Internal cells signals and utility for state estimation,” Journal of Power Sour… [cited by applicant]
Gholizadeh et al., “Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model,” IEEE Transactions on Industrial Electronics, vol. 61,… [cited by applicant]
Graves et al., “Speech Recognition with Deep Recurrent Neural Networks,” 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), No. 3, pp. 6645-6649, 2013. [cited by applicant]
Hansen et al., “Support vector based battery state of charge estimator,” Journal of Power Sources, vol. 141, No. 2, pp. 351-358, 2005. [cited by applicant]
He et al., “Deep Residual Learning for Image Recognition,” Arxiv.Org, pp. 171-180, 2015. [cited by applicant]
Hinton et al., “Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,” in IEEE Signal Processing Magazine, vol. 29, No. 6, pp. 82-97, Nov. 2012. [cited by applicant]
Hochreiter et al., “Long short-term memory,” Neural Computation, vol. 9, No. 8, pp. 1735-1780, 1997. [cited by applicant]
Hochreither, “The vanishing gradient problem during learning recurrent neural nets and problem solutions,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 6, No. 2, pp. 107-116, Apr. 19… [cited by applicant]
Hu et al., “A multiscale framework with extended kalman filter for lithium-ion battery {SOC} and capacity estimation,” Applied Energy, vol. 92, pp. 694-704, 2012. [cited by applicant]
Hu et al., “Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles,” in IEEE Transactions on Transportation Electrification, vol. 2, No. 2, pp. 140-149, Jun. 2016. [cited by applicant]
Hu et al., “Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model,” in IEEE/ASME Transactions on Mechatronics, vol. 23, No. 1, pp. 167-178, Feb. 201… [cited by applicant]
Hu et al., “Fuzzy Clustering Based Multi-model Support Vector Regression State of Charge Estimator for Lithium-ion Battery of Electric Vehicle,” 2009 International Conference on Intelligent Human-Machine Systems and cyb… [cited by applicant]
Hu et al., “Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs,” in IEEE Power and Energy Magazine, vol. 15, No. 5, pp. 20-31, Sep.-Oct. 2017. [cited by applicant]
Kingma et al., “Adam: A method for stochastic optimization,” CoRR, vol. abs/1412.6980, 2014. [cited by applicant]
Kollmeyer et al., “Design of an electric powertrain for a Ford F150 crew cab truck utilizing a lithium battery pack and an interior PM synchronous machine drive,” 2012 IEEE Transporation Electrification Conference and E… [cited by applicant]
Kollmeyer, “Development and Implementation of a Battery-Electric Light-Duty Class 2a Truck including Hybrid Energy Storage,” Ph.D. dissertation, The University of Wisconsin—Madison, 2015. [cited by applicant]
Kollmeyer, “Panasonic 18650PF Li-ion Battery Data,” Mendeley Data, v1, (2018) https://doi.org/10.17632/wykht8y7tg.1. [cited by applicant]
Krizhevsky et al., “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, pp. 1-9, 2012. [cited by applicant]
LeCun et al., “Deep learning,” Nature, vol. 521, No. 7553, pp. 436-444, May 2015. [cited by applicant]
Lee et al., “Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String Systems,” in IEEE Transactions on Industrial Electronics, vol. 55, No. 1, pp. 229-239, Jan. 2008. [cited by applicant]
Li et al., “A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles,” Journla of Power Sources, vol. 230, pp. 244-250, 2013. [cited by applicant]
Li et al., “On state-of-charge determination for lithium-ion batteries,” Journal of Power Sources, vol. 348, pp. 281-301, Apr. 2017. [cited by applicant]
Liu et al., “Integrated System Identification and State-of-Charge Estimation of Battery Systems,” in IEEE Transactions on Energy Conversion, vol. 28, No. 1, pp. 12-23, Mar. 2013. [cited by applicant]
Liu et al., “Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression,” vol. 53, No. 6, pp. 832-839, Jun. 2013. [cited by applicant]
Ma et al., “Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships,” Journal of Chemical Information and Modeling, vol. 55, No. 2, pp. 263-274, Jan. 2015. [cited by applicant]
Malysz et al., “State-of-charge and state-of-health estimation with state constraints and current sensor bias correction for electrified powertrain vehicle batteries,” IET Electrical Systems in Transportation, vol. 6, N… [cited by applicant]
McCurlie et al., “Fast Model Predictive Control for Redistributive Lithium-Ion Battery Balancing,” in IEEE Transactions on Industrial Electronics, vol. 64, No. 2, pp. 1350-1357, Feb. 2017. [cited by applicant]
Meng et al., “Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine,” IEEE Transactions on Power Electronics, vol. 31, No. 3, pp. 2226-2238, Mar. 2016. [cited by applicant]
Panasonic, “Panasonic NCR18650PF Lithium-Ion Battery Datasheet (Jun. 2016),” 2016. [cited by applicant]
Panasonic,“Introduction of NCR18650PF, Panasonic,” 2013. [cited by applicant]
Pascanu et al., “On the difficulty of training recurrent neural networks.” 30th International Conference on Machine Learning (ICML), vol. 28, pp. 1310-1318, 2013. [cited by applicant]