The efficient transportation system can improve the flow of goods and diminish the amount of energy used. In this regard, electric vehicles (EVs) have gained attention owing to their effectiveness in reducing oil demands and gas emissions especially in the area of fork-lifts. These fork-lifts are not only energy-efficient but are also safer for the drivers as electrically powered forklifts produce no fumes, vibrate less and are quieter than the combustion engine powered fork-lifts. Of the electric components of an EV, the battery is considered as the major bottleneck. The lithium-ion batteries are widely employed to power EVs among all types of batteries. The battery state of health (SoH) estimation is used for monitoring and control of EV batteries in order to ensure their safe application. Further, a forecasting battery state-of-health and a battery charge/discharge management strategy needs to be developed to maximize the battery availability for fork-lift operations. 

Voltages available through state-of-the-art single cells are not sufficient for supporting an electric driveline. Therefore, many cells have to be combined in series and in parallel to build up battery packs that are then used as energy sources in EVs. Cells in a battery pack age differently than they would in an isolated environment which affects the service life of the battery packs, and as a result, EVs. This research focuses on EV battery packs in contrast to major studies related to individual battery cells aging. 

Three-year lifetime dataset of lithium-ion batteries from 30 EV fork-lifts is used for sampling information. The dataset consists of basic signals of a battery pack: voltage, current, battery state of charge (SOC) and fork-lift operating temperature over time. We have defined the SoH value for the battery as the ratio of present to unseasoned parameter values that we have selected for the calulation of SoH. The unsesoned battery’s baseline values are estimated from the set of first month’s values. This period of one month has been chosen due to almost continuous driving of these fork-lifts with little rest-time. Regression tools were used for making short-term and long-term SoH forecasts based on our initial calculations.  

If the usage behavior remains unchanged, the designed model estimates the truck's battery pack to complete 3000 cycles in a ten-year service period. Estimated cycle life corresponds to the published cycle life for commercial Nickel-Cobalt-Manganese (NMC) cells. Details of the SOH forecasting model and the key predictions are presented in this manuscript.


A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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