Abstract
The simplified process of the algorithm in the theoretical design may lead to deviations in practical application. As a result, it is very important to download the algorithm to the real BMS and evaluate it according to the relevant standards and indexes, which helps designers to find and solve some practical problems that are neglected in the theoretical derivation in time and optimize the algorithm. The traditional algorithm development and evaluation methods not only consume a lot of time, manpower cost, but also limited by safety issues. In addition, it is difficult to comprehensively and systematically evaluate some actual controlled objects. Fortunately, the āVā development process based on the rapid prototyping and hardware in the loop (HIL) test can find out the problems in the algorithm and make evaluation efficiently and accurately, which improves the development efficiency. This chapter mainly focuses on the development process of BMS for EVs, and illustrates the evaluation methods of rapid prototyping simulation, HIL test algorithm, and the experiments for vehicles [1].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sun H, Gu L, Tong X (2014) Development of a hardware-in-loop simulation test bench for electric drives. Drive Syst Tech 28(4):19ā26
Chin CS, Lum SH (2011) Rapid modeling and control systems prototyping of a marine robotic vehicle with model uncertainties using xPC Target system. Ocean Eng 38(17):2128ā2141
Zhang Z, Zhou M (2011) Experimental platform design on pure electric vehicle based on dSPACE. Equip Manuf Technol 12:115ā117
Xiong R, Duan Y (2018) Development and verification of the equilibrium strategy for batteries in electric vehicles. J Beijing Inst Technol 27(1):22ā28
Mu H (2016) Research on robustness state of charge estimation of lithium-ion power battery for electric vehicles. PhD Dissertation. Beijing Institute of Technology, Beijing
Wang C (2018) Modeling and energy management strategy for vehicular hybrid energy storage system. PhD Dissertation. Beijing Institute of Technology, Beijing
Cao J (2018) Research on configuration analysis, parameter matching and optimal control for hybrid power system in electric vehicles. MA thesis. Beijing Institute of Technology, Beijing
Chen C, Xiong R, Shen W (2018) A lithium-ion battery-in-the-loop approach to test and validate multi-scale dual h infinity filters for state of charge and capacity estimation. IEEE Trans Power Electron 33(1):332ā342
Wu C, Wei X, Dai H (2014) Battery management system fault diagnosis test based on hardware-in-loop. Meas Control Technol 33(3):25ā28
Wang J, Xiong R, Li L, Fang Y (2018) A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach. Appl Energy 229:648ā659
He H, Xiong R, Peng J (2016) Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS Ī¼COS-II platform. Appl Energy 162:1410ā1418
Xiong R, Cao J, Yu Q (2018) Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle. Appl Energy 211(5):538ā548
Xiong R, He H, Sun F, Zhao K (2012) Online estimation of peak power capability of Li-Ion batteries in electric vehicles by a hardware-in-loop approach. Energies 5(5):1455ā1469
Wang C, Xiong R, He H, Shen Zhang YWX (2019) Comparison of decomposition levels for wavelet transform based energy management in a plug-in hybrid electric vehicle. J Clean Prod 210:1085ā1097
Tian J, Xiong R, Shen W, Wang J (2019) Frequency and time domain modelling and online state of charge monitoring for ultra-capacitors. Energy 176:874ā887
Wang C, Huang B, Xu WN (2018) An integrated energy management strategy with parameter match method for plug-in hybrid electric vehicles. IEEE Access 6:62204ā62214
Wang C, Xiong R, He H, Ding X, Shen WX (2016) Efficiency analysis of a bidirectional DC/DC converter in a hybrid energy storage system for plug-in hybrid electric vehicles. Appl Energy 183:612ā622
Xiong R, Tian J, Shen W, Sun F (2018) A novel fractional order model for state of charge estimation in lithium ion batteries. IEEE Trans Veh Technol 68(5):4130ā4139
MPC5644A Microcontroller Datasheet, American:Freescale Semiconductor
LTC6804-1 datasheet, American: Linear Technology Corporation
Zhang Y (2018) State of health identification and remaining useful life prediction of lithium-ion batteries for electric vehicles. Ph.D. Dissertation. Beijing: Beijing Institute of Technology
Xiong R, Zhang Y, Wang J (2019) Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles. IEEE Trans Veh Technol 68(5):4110ā4121
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
Ā© 2020 China Machine Press
About this chapter
Cite this chapter
Xiong, R. (2020). Algorithm Development, Test, and Evaluation. In: Battery Management Algorithm for Electric Vehicles . Springer, Singapore. https://doi.org/10.1007/978-981-15-0248-4_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-0248-4_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0247-7
Online ISBN: 978-981-15-0248-4
eBook Packages: EnergyEnergy (R0)