Search citation statements

Paper Sections

Select...

2

2

1

Citation Types

0

6

0

Year Published

2018

2018

Publication Types

Select...

2

Relationship

0

2

Authors

Journals

(6 citation statements)

0

6

0

“…As shown in Figure 5, a comprehensive driving cycle which is originated from the six typical driving cycles shown in Table 1 is employed as the test driving cycle, 18 namely, US06_HWY + MANHATTAN + WVUSUB + HWFET + CSHVR + NYCC. The I-LVQ and LVQ algorithm 21 are used to train the DCR model to verify the effectiveness of the model training algorithm. Each 200-s time length of the comprehensive driving cycle is considered the recognition target.…”

confidence: 99%

“…As shown in Figure 5, a comprehensive driving cycle which is originated from the six typical driving cycles shown in Table 1 is employed as the test driving cycle, 18 namely, US06_HWY + MANHATTAN + WVUSUB + HWFET + CSHVR + NYCC. The I-LVQ and LVQ algorithm 21 are used to train the DCR model to verify the effectiveness of the model training algorithm. Each 200-s time length of the comprehensive driving cycle is considered the recognition target.…”

confidence: 99%

“…The LVQ neural network is a class of algorithms for pattern recognition and has been widely used in DCR. 21 However, the LVQ neural network cannot easily obtain ideal recognition accuracy and low computational complexity when the sample space is relatively large. 24 Therefore, an I-LVQ neural network is considered and applied to recognize driving cycle online.…”

confidence: 99%

“…The current energy management strategies of HEV are mainly divided into rule-based energy management strategy, 2,3 instantaneous optimized energy management strategy, 4,5 global optimization energy management strategy [6][7][8] and adaptive driving condition energy management strategy. 9,10 The first three energy strategies distribute power between the engine and the motor only based on the current vehicle operating conditions or analysis of driving conditions. Daniel et al 2 proposed a rule-based energy management strategy combining neural network and fuzzy control, which determine the start point and the duration of the engine to drive the generator for charging according to the engine power and battery SOC, and the charging current, respectively.…”

confidence: 99%