Recording frequency optimization for massive battery data storage in battery management systems

Massive data storage is an advanced function in a fully functional battery management system (BMS). Reducing the recording signal length undoubtedly saves the precious memory space for BMS. And it also reduces the network and computation loads. However, it leads to a side effect that the trend of signal distortion is enhanced. The optimal recording frequency in practice should be as low as possible on the condition that little signal distortion happens. This paper presents a novel method which uses a multi-frequency recording technology that cooperates two approaches according to the signal dynamics. A flexible recording frequency method is applied for stationary signals which only records signals when their values are changed. While for dynamic signals, the most dynamic period is found using discrete wavelet transformation (DWT) and further analyzed by fast Fourier transformation (FFT). By comparing two recording signal indicators for four different recording frequencies, we conclude that recording at 1Hz is not qualified for the cell voltage and current during the dynamic period in our system due to the high dynamic performance of the vehicle. In the demonstrated vehicle, only by increasing the recording frequency to at least 2Hz, can the accuracy of the recorded cell voltage achieve the level the same as the measurement accuracy in engineering. And we also verify that when the recording frequency is reduced to the optimal frequency compared to the high frequency recorded original signals, the accuracy of the SOC estimation is not influenced.

[1]  Nigel P. Brandon,et al.  Module design and fault diagnosis in electric vehicle batteries , 2012 .

[2]  Jianqiu Li,et al.  Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model , 2013 .

[3]  Jianqiu Li,et al.  Online management of lithium-ion battery based on time-triggered controller area network for fuel-cell hybrid vehicle applications , 2010 .

[4]  Unai Viscarret,et al.  Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter , 2015 .

[5]  Ken Darcovich,et al.  Modelling the impact of variations in electrode manufacturing on lithium-ion battery modules , 2012 .

[6]  Jianqiu Li,et al.  A review on the key issues for lithium-ion battery management in electric vehicles , 2013 .

[7]  R. Mahamud,et al.  Reciprocating air flow for Li-ion battery thermal management to improve temperature uniformity , 2011 .

[8]  Enrique Romero-Cadaval,et al.  A novel active battery equalization control with on-line unhealthy cell detection and cell change decision , 2015 .

[9]  Xuning Feng,et al.  Online internal short circuit detection for a large format lithium ion battery , 2016 .

[10]  Gae-won You,et al.  Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach , 2016 .

[11]  Jemal H. Abawajy,et al.  Intelligent battery energy management and control for vehicle-to-grid via cloud computing network , 2013 .

[12]  Chenbin Zhang,et al.  A novel active equalization method for lithium-ion batteries in electric vehicles , 2015 .

[13]  Simon F. Schuster,et al.  Lithium-ion cell-to-cell variation during battery electric vehicle operation , 2015 .

[14]  Dirk Uwe Sauer,et al.  Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination , 2013 .

[15]  Christian Fleischer,et al.  Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .

[16]  Jianqiu Li,et al.  Understanding aging mechanisms in lithium-ion battery packs: From cell capacity loss to pack capacity evolution , 2015 .

[17]  Jianqiu Li,et al.  Lithium ion battery pack power fade fault identification based on Shannon entropy in electric vehicles , 2013 .

[18]  Bo-Hyung Cho,et al.  An innovative approach for characteristic analysis and state-of-health diagnosis for a Li-ion cell based on the discrete wavelet transform , 2014 .

[19]  Asok Ray,et al.  Dynamic data-driven identification of battery state-of-charge via symbolic analysis of input–output pairs , 2015 .

[20]  Hongwen He,et al.  A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique , 2016 .

[21]  Hewu Wang,et al.  Optimal decentralized valley-filling charging strategy for electric vehicles , 2014 .

[22]  Puqiang Zhang,et al.  Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery , 2014 .

[23]  Jianqiu Li,et al.  LiFePO4 battery pack capacity estimation for electric vehicles based on charging cell voltage curve transformation , 2013 .

[24]  Dong Hui,et al.  Battery Energy Storage Station (BESS)-Based Smoothing Control of Photovoltaic (PV) and Wind Power Generation Fluctuations , 2013, IEEE Transactions on Sustainable Energy.

[25]  Jianqiu Li,et al.  On-line equalization for lithium-ion battery packs based on charging cell voltages: Part 1. Equalization based on remaining charging capacity estimation , 2014 .

[26]  Matthieu Dubarry,et al.  State-of-charge estimation and uncertainty for lithium-ion battery strings , 2014 .

[27]  F. A. Amoroso,et al.  Impact of charging efficiency variations on the effectiveness of variable-rate-based charging strate , 2011 .

[28]  Jiahao Li,et al.  A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems , 2015 .

[29]  Matthew B. Pinson,et al.  Internal resistance matching for parallel-connected lithium-ion cells and impacts on battery pack cycle life , 2014 .

[30]  Hongwen He,et al.  A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles , 2014 .