Automotive Engineering ›› 2023, Vol. 45 ›› Issue (12): 2338-2347.doi: 10.19562/j.chinasae.qcgc.2023.12.016
Special Issue: 智能网联汽车技术专题-控制2023年
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Zhicheng He1,Leihao Du2,Enlin Zhou1(),Gaofeng Qin2,Jin Huang3
Received:
2022-04-19
Revised:
2022-06-12
Online:
2023-12-25
Published:
2023-12-21
Contact:
Enlin Zhou
E-mail:tenrey18@163.com
Zhicheng He,Leihao Du,Enlin Zhou,Gaofeng Qin,Jin Huang. CAN Bus Load Rate Optimization Based on Improved Continuous Hopfield Neural Network[J].Automotive Engineering, 2023, 45(12): 2338-2347.
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信号标识 | 长度/bit | 周期/ms | 信号标识 | 长度/bit | 周期/ms | 信号标识 | 长度/bit | 周期/ms | 信号标识 | 长度/bit | 周期/ms |
---|---|---|---|---|---|---|---|---|---|---|---|
S1 | 8 | 10 | S26 | 1 | 200 | S51 | 8 | 10 | S76 | 1 | 200 |
S2 | 8 | 10 | S27 | 2 | 200 | S52 | 8 | 10 | S77 | 2 | 200 |
S3 | 1 | 200 | S28 | 1 | 400 | S53 | 1 | 200 | S78 | 1 | 400 |
S4 | 1 | 10 | S29 | 2 | 400 | S54 | 1 | 10 | S79 | 2 | 400 |
S5 | 2 | 100 | S30 | 1 | 300 | S55 | 2 | 100 | S80 | 1 | 300 |
S6 | 1 | 100 | S31 | 16 | 100 | S56 | 1 | 100 | S81 | 16 | 100 |
S7 | 1 | 100 | S32 | 8 | 200 | S57 | 1 | 100 | S82 | 8 | 200 |
S8 | 1 | 100 | S33 | 2 | 300 | S58 | 1 | 100 | S83 | 2 | 300 |
S9 | 8 | 100 | S34 | 2 | 100 | S59 | 8 | 100 | S84 | 2 | 100 |
S10 | 1 | 10 | S35 | 10 | 300 | S60 | 1 | 10 | S85 | 10 | 300 |
S11 | 1 | 10 | S36 | 1 | 100 | S61 | 1 | 10 | S86 | 1 | 100 |
S12 | 2 | 10 | S37 | 1 | 100 | S62 | 2 | 10 | S87 | 1 | 100 |
S13 | 2 | 10 | S38 | 1 | 100 | S63 | 2 | 10 | S88 | 1 | 100 |
S14 | 2 | 100 | S39 | 10 | 100 | S64 | 2 | 100 | S89 | 10 | 100 |
S15 | 16 | 10 | S40 | 10 | 400 | S65 | 16 | 10 | S90 | 10 | 400 |
S16 | 16 | 10 | S41 | 8 | 400 | S66 | 16 | 10 | S91 | 8 | 400 |
S17 | 1 | 100 | S42 | 1 | 100 | S67 | 1 | 100 | S92 | 1 | 100 |
S18 | 1 | 100 | S43 | 1 | 400 | S68 | 1 | 100 | S93 | 1 | 400 |
S19 | 8 | 200 | S44 | 1 | 100 | S69 | 8 | 200 | S94 | 1 | 100 |
S20 | 8 | 200 | S45 | 1 | 100 | S70 | 8 | 200 | S95 | 1 | 100 |
S21 | 1 | 100 | S46 | 8 | 200 | S71 | 1 | 100 | S96 | 8 | 200 |
S22 | 3 | 100 | S47 | 8 | 300 | S72 | 3 | 100 | S97 | 8 | 300 |
S23 | 1 | 100 | S48 | 2 | 300 | S73 | 1 | 100 | S98 | 2 | 300 |
S24 | 1 | 100 | S49 | 2 | 300 | S74 | 1 | 100 | S99 | 2 | 300 |
S25 | 1 | 100 | S50 | 4 | 200 | S75 | 1 | 100 |
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算法 | 数据 | CAN总线负载率(Busload) | 均值 | 总计 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CHNN | A | 0.308 4 | 0.299 4 | 0.308 4 | 0.308 4 | 0.334 4 | 0.326 4 | 0.308 4 | 0.309 4 | 0.308 4 | 0.298 4 | 0.308 1 | 0.546 7 |
0.307 9 | 0.298 4 | 0.297 9 | 0.308 4 | 0.308 9 | 0.308 4 | 0.307 9 | 0.308 4 | 0.297 9 | 0.307 4 | ||||
0.025 7 | 0.025 4 | 0.025 9 | 0.025 7 | 0.025 4 | 0.025 4 | 0.025 7 | 0.025 7 | 0.025 2 | 0.025 4 | ||||
B | 0.229 3 | 0.239 3 | 0.239 3 | 0.239 3 | 0.239 3 | 0.229 3 | 0.239 3 | 0.239 3 | 0.239 3 | 0.247 3 | 0.238 6 | ||
0.239 3 | 0.239 3 | 0.239 3 | 0.239 3 | 0.247 3 | 0.229 3 | 0.239 3 | 0.239 3 | 0.239 3 | 0.239 3 | ||||
SA-CHNN | A | 0.298 4 | 0.298 4 | 0.298 4 | 0.297 9 | 0.298 4 | 0.297 9 | 0.297 9 | 0.298 4 | 0.297 9 | 0.298 4 | 0.298 2 | 0.528 0 |
0.298 4 | 0.298 4 | 0.297 9 | 0.298 4 | 0.298 4 | 0.297 9 | 0.298 4 | 0.297 9 | 0.297 9 | 0.298 4 | ||||
0.025 4 | 0.025 2 | 0.025 2 | 0.025 2 | 0.025 4 | 0.024 9 | 0.025 2 | 0.024 9 | 0.025 2 | 0.025 2 | ||||
B | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 8 | ||
0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.229 3 | 0.239 3 | 0.229 3 |
1 | SPECIFICATION C A N. Bosch[J]. Robert Bosch GmbH, Postfach, 1991, 50. |
2 | DI NATALE M, ZENG H, GIUSTO P, et al. Understanding and using the controller area network communication protocol: theory and practice[M]. Springer Science & Business Media, 2012. |
3 | SHUAI Z, ZHANG H, WANG J, et al. Combined AFS and DYC control of four-wheel-independent-drive electric vehicles over CAN network with time-varying delays[J]. IEEE Transactions on Vehicular Technology, 2013, 63(2): 591-602. |
4 | TAUBE J, HARTWICH F, BEIKIRCH H. Comparison of CAN gateway modules for automotive and industrial control applications[C].Proceedings of the 10th international CAN Conference (iCC2005), 2005. |
5 | SOMMER J, BLIND R. Optimized resource dimensioning in an embedded CAN-CAN gateway[C]. 2007 International Symposium on Industrial Embedded Systems. IEEE, 2007: 55-62. |
6 | SOJKA M, PÍŠA P, ŠPINKA O, et al. Measurement automation and result processing in timing analysis of a Linux-based CAN-to-CAN gateway[C]. Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems. IEEE, 2011, 2: 963-968. |
7 | AZKETA E, GUTIÉRREZ J J, PALENCIA J C, et al. Schedulability analysis of multi-packet messages in segmented CAN[C].Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012). IEEE, 2012: 1-8. |
8 | LIU J, ZHANG S, SUN W, et al. In-vehicle network attacks and countermeasures: challenges and future directions[J]. IEEE Network, 2017, 31(5): 50-58. |
9 | 刘洪鹏, 唐国强. CAN 总线负载率算法及优化设计[J]. 客车技术与研究, 2019, 5. |
LIU H P, TANG G Q. Algorithm and optimization design of CAN bus load rate[J]. Bus & Coach Technology and Research, 2019, 5. | |
10 | COOK J A, FREUDENBERG J S. Controller area network (CAN)[J]. EECS, 2007, 461: 1-5. |
11 | NOLTE T, HANSSON H, NORSTROM C. Minimizing CAN response-time jitter by message manipulation[C].Proceedings. Eighth IEEE Real-Time and Embedded Technology and Applications Symposium. IEEE, 2002: 197-206. |
12 | NAN J R, CUI S C, CHAI Z. Research on CAN bus of pure electric vehicles based on SAEJ1939[C]. Advanced Materials Research. Trans Tech Publications Ltd, 2012, 512: 2650-2656. |
13 | POLZLBAUER F, BATE I, BRENNER E. Optimized frame packing for embedded systems[J]. IEEE Embedded Systems Letters, 2012, 4(3): 65-68. |
14 | 赵公旗,冯宝存,赵红玉,等.浅谈车载总线负载对网络传输影响及优化[J].汽车电器,2015(12):22-24. |
ZHAO G Q, FENG B C, ZHAO H Y, et al. Influence of vehicle busload on network transmission and its optimization[J]. Automotive Appliances, 2015(12):22-24. | |
15 | 巢宽宏. 基于神经网络的CAN总线负载率优化的研究[D].长沙: 湖南大学,2020. |
CHAO K H. Research on the load rate optimization of CAN bus based on neural network[D]. Changsha: Hunan University, 2020. | |
16 | 刘青.汽车 CAN 总线实时性优化研究[D].长春: 吉林大学, 2013. |
LIU Q. Optimization research on real time performance of CAN bus in automobile[D].Changchun: Jilin University, 2013. |
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