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key words：
| terminal voltage | 外接电压 |
|---|---|
| capacitance | 电容 |
| resistance | 电阻 |
| electrolyte | 电解质 |
| electrice current | 电流 |
| solid-electrolyte interphase | 固体电解质 |
Strong influences on terminal voltage
- Temperature
- SOC
- Precondition
- Age (calendric/ cyclic)
- HAVC Usage (Heating, ventilation, aircondition)
- DOD, depth of charge
historical data, road profile (urban, intercity),
traffic congestion level, driver behaviour (i.e. aggressiveness),
environmental conditions (i.e. weather conditions), dynamic
vehicle parameters (i.e. SOC, SOH) and accessory loads (i.e. heating,
cooling
Many different cell
NMC\LFP\LTO
NN/ stochastic model/ Fuzzy-Logic
Pro： Little Knowledge about BMS, computational performance
Con: non-explainable, not for all batteries/ comprehensive measurement
Modeling approach
pro: computational performance, sufficient precisions, partly physical meaning
con: leaner model (U and I )
1. H.A. Yavasoglu
- DT for road type
- ANN for range estimation (Use ann because the update ability)
conventional multiple linear regression method
gradient boosting decision tree algorithm
- residual usable energy (RUE)
SOC(state of charge) , SOH (state of health), temperature,
future discharge voltage
capacity values
Implementation of machine learning-based real-time range estimation method without destination knowledge for BEVs
| phrase | exp |
|---|---|
| Range Anxiety | 里程焦虑 |
| velocity | speed |
| jerk | change of acceleration |
2. Compare different algorithms Delnevo*
3 State-of-Charge Estimation Methods for Li-ion Batteries in Electric Vehicles
To estimate SOC, there are three methods
All about the SOC
