Energy Management Strategy for the Hybrid Energy Storage System of Pure Electric Vehicle Considering Traffic Information
Abstract
:1. Introduction
2. Parameters and Model
3. Adaptive Rule Based Controller
3.1. Offline Optimization of Dynamic Programming
3.2. Formulation of Adaptive Rule Based Controller
4. Comprehensive Controller Considering Traffic Information
4.1. Access to the Traffic Condition and the Road Grade
4.2. Future Trend Prediction of Vehicle Speed
- ①
- The combined driving cycle, including MANHATTAN, NYCC, UDDS, NEDC, WVUINTER, and HWFET, is selected as the sample driving cycle, as shown in Figure 6. These six driving cycles cover urban, suburban, and expressway condition.
- ②
- The sampling period is 1 s and the driving cycle between the two sampling points is a driving cycle block. The average accelerated speed, the standard deviation of the vehicle speed, and the difference between the initial speed and the last speed of a driving cycle block are selected as the characteristic parameters of the driving cycles [32]. The characteristic parameters of 10 s are calculated at each sampling point.
- ③
- ④
- After obtaining the cluster centers, the distance, , from various characteristic parameters to the th cluster center can be computed by Equation (11). If & , the state of the vehicle speed is the type of speed descending, and the state of the vehicle speed belongs to the speed stabling when & . If & , the vehicle speed is considered to be a speed rising.
4.3. Optimization of Instantaneous Power Allocation
5. Results
6. Conclusions
- (1)
- A general law exists in the optimal power allocation of HESS under various types of driving cycles, and the controllers based on the law can achieve good economic performance of HESS.
- (2)
- Considering traffic information in a controller is beneficial to the performance promotion of HESS.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter | Value |
---|---|
Total weight/kg | 1900 |
Curb weight/kg | 1500 |
Front section/m2 | 2.3 |
Aerodynamic drag factor | 0.29 |
Rolling resistance | 0.012 |
Wheel radius/m | 0.307 |
Motor rated power/kW | 80 |
Motor peak power/kW | 105 |
Motor voltage class/V | ≤360 |
Index | Value |
---|---|
Nominal capacity/Ah | 20 |
Nominal voltage/V | 3.2 |
Internal resistance/mΩ | ≤6 |
Weight/g | 514 ± 10 |
Charge voltage/V | 3.65 ± 0.05 |
Discharge termination voltage/V | 2.0 |
Operating temperature/°C | −20 ~ 60 |
Driving Cycle | Average Positive Power/kW | Threshold in Region 2/kW | Fitting Curves in Region 3 |
---|---|---|---|
UKBUS6 | 3.360 | 3.291 | f(x) = 0.7647x − 3.040 |
MANHATTAN | 6.172 | 5.132 | f(x) = 0.7850x − 4.366 |
NYCC | 7.121 | 6.673 | f(x) = 0.7855x − 5.905 |
UDDS | 10.202 | 6.486 | f(x) = 0.7772x − 6.187 |
New York Bus | 11.000 | 8.070 | f(x) = 0.7767x − 7.065 |
INDIAHWY | 11.669 | 10.85 | f(x) = 0.7892x − 8.444 |
EUDC_LOW | 11.770 | 13.68 | f(x) = 0.7872x − 11.15 |
HWFET | 14.251 | 18.67 | f(x) = 0.7628x − 14.53 |
Operating Condition | Switch Condition | Power Allocation |
---|---|---|
Driving: battery and UC | Pmin < Pdem and SOCuc,min < SOCuc | Puc = aPdem + b Pbat = Pdem − Puc |
Driving: battery | Pmin ≤ Pdem and SOCuc ≤ SOCuc,min | Pbat = Pdem Puc = 0 |
Driving: battery | 0 ≤ Pdem ≤ Pmin | Pbat = Pdem Puc = 0 |
Braking: UC | Pdem < 0 and SOCuc < SOCuc,max | Puc = Pdem Pbat = 0 |
Braking: neither of them | Pdem < 0 and SOCuc,max ≤ SOCuc | Pbat = 0 Puc = 0 |
Cluster Centers | |||
---|---|---|---|
Average accelerated speed | −0.65394301496 | −0.00551733775 | 0.51745111141 |
Standard deviation of vehicle speed | 7.78720463753 | 1.10825192133 | 6.05417011930 |
Speed difference | −22.1134815365 | −0.1531105304 | 17.3566038619 |
Input and Output | Actual Domain | Fuzzy Domain | Membership Function | Fuzzy Subset Levels |
---|---|---|---|---|
0 ~ 70 | 0 ~ 1 | Gauss type/Bilateral Gauss | 3 | |
−3 ~ 3 | −3 ~ 3 | Triangle | 7 | |
−10 ~ 10 | −1 ~ 1 | Triangle | 7 | |
−0.2 ~ 0.2 | −0.2 ~ 0.2 | Gauss type/Bilateral Gauss | 7 |
Operating Conditions | Switch Requirement | Power Allocation |
---|---|---|
Battery discharges alone | Pmin ≤ Pdem & SOCuc ≤ SOCuc,min | Pbat = Pdem, Puc = 0 |
Battery discharges alone | 0 ≤ Pdem < Pmin & SOCuc,tag ≤ SOCuc | Pbat = Pdem, Puc = 0 |
Battery and UC discharge | Pmin ≤ Pdem & SOCuc,min < SOCuc | Pbat = Pmin, Puc = Pdem-Pmin |
Battery discharges and UC charges | 0 ≤ Pdem < Pmin & SOCuc < SOCuc,tag | Pbat = Pdem + Pch, Puc = −Pch |
UC absorbs braking energy | Pdem < 0 & SOCuc < SOCuc,max | Pbat = 0, Puc = Pdem |
Neither of them absorbs the braking energy | Pdem < 0 & SOCuc,max ≤ SOCuc | Pbat = 0, Puc = 0 |
Controllers\Battery Index | Battery Life Loss | ||
---|---|---|---|
TRBC 3 | 0.9 | 0.7788 | 1.4028 × 10−4 |
ARBC 4 | 0.9 | 0.7831 | 1.3501 × 10−4 |
CC 5 | 0.9 | 0.7853 | 1.3106 × 10−4 |
Battery index\Controllers | TRBC | ARBC | CC |
---|---|---|---|
Battery using times | 13,074 | 10,933 | 9564 |
Average power of battery/kW | 9.31 | 9.05 | 8.87 |
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Hu, J.; Jiang, X.; Jia, M.; Zheng, Y. Energy Management Strategy for the Hybrid Energy Storage System of Pure Electric Vehicle Considering Traffic Information. Appl. Sci. 2018, 8, 1266. https://doi.org/10.3390/app8081266
Hu J, Jiang X, Jia M, Zheng Y. Energy Management Strategy for the Hybrid Energy Storage System of Pure Electric Vehicle Considering Traffic Information. Applied Sciences. 2018; 8(8):1266. https://doi.org/10.3390/app8081266
Chicago/Turabian StyleHu, Jianjun, Xingyue Jiang, Meixia Jia, and Yong Zheng. 2018. "Energy Management Strategy for the Hybrid Energy Storage System of Pure Electric Vehicle Considering Traffic Information" Applied Sciences 8, no. 8: 1266. https://doi.org/10.3390/app8081266