Advanced battery management technologies for electric vehicles
The first chapter introduces the basic knowledge of electric vehicle and the requirements for battery system of electric vehicle, and introduces the structure and function of BMS.
Chapter 2 introduces the classification of battery modeling technology. Based on the equivalent circuit model, the off-line parameter identification method and on-line parameter identification method are introduced, and the application of each method is illustrated by examples.
In Chapter 3-5, state of charge estimation, health state estimation and actual power state estimation are introduced respectively. Matlab code and Simulink model are used in the case study to implement these estimation methods and provide them to users.
Chapter 6 reviews the different charging methods. Then, the latest development of charging method is introduced from two aspects.
Chapter 7 introduces several battery balancing technologies, including battery classification, battery passive balancing and battery active balancing. Then, this chapter focuses on the active balance of battery.
The eighth chapter introduces the basic function and structure of the battery management system.
The eighth chapter introduces the basic function and structure of the battery management system. In addition, key insights for future generations of BMS are discussed, including self heating and safety management, as well as the application of cloud computing and big data in health state estimation and battery life prediction.
Ref：Rui Xiong, Weixiang Shen. Advanced battery management technologies for electric vehicles.John Wiley & Sons, 2019. (Wiley)
Battery Management Algorithm for Electric Vehicles
Chapter 1 analyzes the development plan of China's new energy vehicles in the 13th five year plan and the technical indicators of the power battery management system, and systematically expounds the design and implementation points of the power battery system and its management；
In Chapter 2, the construction, experimental design and characteristic analysis of power battery test platform are described. The working characteristics of power battery under different aging, temperature and charge discharge rate are analyzed systematically, which provides direction guidance for the development of core algorithm of power battery management system；
From Chapter 3 to Chapter 7, the basic theory, algorithm construction and implementation details of the core algorithms of power battery management system, such as power battery system modeling, state of charge and health collaborative estimation, peak power prediction, residual life prediction, low temperature rapid heating and optimal charging, are discussed systematically and deeply. Finally, the "V" development process of power battery management system algorithm is discussed from the aspects of hardware and software in the loop simulation verification, bench test and real vehicle verification of the core algorithm.
Ref: Rui Xiong. Battery Management Algorithm for Electric Vehicles. Springer, 2020. (Springer)
Available resources List
1. Model: Equivalent Circuit Model (Thevenin model.zip); Electrochemical Model (Electrochemical model.zip); Fractional-order Model (Fractional order model.zip);
2. SOX Algorithm: EKF-SOC Algorithm Model (program01_EKF.zip); SOH Algorithm Model (SOH.zip, 56.9kB); SOP Algorithm Model (SOP.zip);
3. Optimized Charging Algorithm: (optimal_charge.zip);
4. Life Prediction Model: (runBoxCox.zip);
5. Test Data: Battery cell data, Battery pack data, Real vehicle operation data (Click to see);
Notes: Some resources are large, you will receive a download link, maybe the link for a third-party platform, or directly Zip File in 48 hours once you submit the application form.
1. R. Xiong, S. Ma, H. Li, F. Sun and J.Li, “Towards a Safer Battery Management System: A Critical Review on Diagnosis and Prognosis of Battery Short Circuit”, iScience, vol. 23, no. 4, pp. 101010, April 2020. (Download)
2. R. Xiong, Q. Yu, W. Shen, C.Lin and F. Sun, "A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles", IEEE Transactions on Power Electronics, 2019, vol. 34, no. 10, pp. 9709-9718, OCT 2019. (Download)
3. R. Xiong, Y. Zhang, H. He, X. Zhou, Michael Pecht, “A double-scale, particle-filtering, energy state prediction algorithm for lithium-ion batteries,” IEEE Transactions on Industrial Electronics, vol.65, no.2, pp.1526-1538, Feb 2018. (Download)
4. R. Xiong, JP Tian, H Mu, C. Wang, “A systematic model-based degradation behavior recognition and health monitor method of lithium-ion batteries,” Appl Energy, vol. 207, pp. 367-378, DEC 2017. (Download)
5. R. Xiong, Q.Q Yu, LY Wang, C Lin, “A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter,” Appl Energy, vol. 207, pp. 341-348, DEC 2017. (Download)
6. F. Sun; R. Xiong and H. He, “Estimation of state-of-charge and state-of-power capability of lithium-ion battery considering varying health conditions,” J. Power Sources, vol.259, pp.166–176, Aug. 2014. (Download)
7. R. Xiong; F. Sun; X. Gong and C. Gao, “A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles,” Appl Energy, vol. 113, pp. 1421–1433, Jan. 2014. (Download)
8. R. Xiong; F. Sun; Z. Chen and H. He, “A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles,” Appl Energy, vol. 113, pp. 463-476, Jan. 2014. (Download)
9. R. Xiong; F. Sun; H. He and T. Nguyen, “A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles,” Energy, vol. 63, pp. 295–308, Dec. 2013. (Download)
10. R. Xiong; F. Sun; X. Gong and H. He, “Adaptive state of charge estimator for lithium-ion cells series battery pack in electric vehicles,” J. Power Sources, vol. 242, pp. 699–713, Nov., 2013. (Download)
11. R. Xiong; X. Gong and C. C. Mi, “A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter,” J. Power Sources, vol. 243, pp. 805–816, Jun. 2013. (Download)
12. R. Xiong; H. He; F. Sun; X. Liu and Z.Liu, “Model-based State of Charge and peak power capability joint estimation of Lithium-Ion battery in plug-in hybrid electric vehicles,” J. Power Sources, vol. 229, pp. 159–169, May 2012. (Download)
13. R. Xiong; H. He; F. Sun and K. Zhao, “Evaluation on State of Charge Estimation of Batteries with Adaptive Extended Kalman Filter by Experiment Approach,” IEEE T VEH TECHNOL. Vol. 62, no.1, pp. 108–117, Jan. 2013. (Download)
14. R. Xiong; F. Sun and H. He, “Data-driven State-of-charge Estimator for Electric Vehicles Battery using Robust Extended Kalman Filter,” INT J AUTOMOT TECHN., vol. 15, no. 1, pp. 89–96, Feb. 2014. (Download)
15. R. Xiong; H. He; F. Sun and K. Zhao, “Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach,” Energies, vol. 5, no. 5, pp. 1455-1469, May 2012. (Download)
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