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Frontiers of Mechanical Engineering

ISSN 2095-0233

ISSN 2095-0241(Online)

CN 11-5984/TH

Postal Subscription Code 80-975

2018 Impact Factor: 0.989

Front. Mech. Eng.    2023, Vol. 18 Issue (1) : 2    https://doi.org/10.1007/s11465-022-0718-y
RESEARCH ARTICLE
Lightweight design of an electric bus body structure with analytical target cascading
Puyi WANG1,2, Yingchun BAI1, Chuanliang FU1, Cheng LIN1
1. National Engineering Research Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
2. Northwest Institute of Mechanical and Electrical Engineering, Xianyang 712099, China
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Abstract

Lightweight designs of new-energy vehicles can reduce energy consumption, thereby improving driving mileage. In this study, a lightweight design of a newly developed multi-material electric bus body structure is examined in combination with analytical target cascading (ATC). By proposing an ATC-based two-level optimization strategy, the original lightweight design problem is decomposed into the system level and three subsystem levels. The system-level optimization model is related to mass minimization with all the structural modal frequency constraints, while each subsystem-level optimization model is related to the sub-structural performance objective with sub-structure mass constraints. To enhance the interaction between two-level systems, each subsystem-level objective is reformulated as a penalty-based function coordinated with the system-level objective. To guarantee the accuracy of the model-based analysis, a finite element model is validated through experimental modal test. A sequential quadratic programming algorithm is used to address the defined optimization problem for effective convergence. Compared with the initial design, the total mass is reduced by 49 kg, and the torsional stiffness is increased by 17.5%. In addition, the obtained design is also validated through strength analysis.

Keywords electric vehicle      body in white (BIW)      lightweight      analytical target cascading (ATC)     
Corresponding Author(s): Yingchun BAI   
About author: Changjian Wang and Zhiying Yang contributed equally to this work.
Just Accepted Date: 30 June 2022   Issue Date: 02 March 2023
 Cite this article:   
Puyi WANG,Yingchun BAI,Chuanliang FU, et al. Lightweight design of an electric bus body structure with analytical target cascading[J]. Front. Mech. Eng., 2023, 18(1): 2.
 URL:  
https://academic.hep.com.cn/fme/EN/10.1007/s11465-022-0718-y
https://academic.hep.com.cn/fme/EN/Y2023/V18/I1/2
Fig.1  Principle of analytical target cascading method.
Fig.2  Information interactions in the ith subsystem with penalty-based analytical target cascading method.
Fig.3  Composition and connections of electric bus body structure.
Fig.4  Constraints and load conditions under (a) bending case and (b) torsional case.
Fig.5  Experimental modal test set up: (a) free support, (b) accelerometer, and (c) data acquired system.
Fig.6  Comparisons between experimental and simulated modal shapes: (a) experimental first-order bending modal shape, (b) simulated first-order bending modal shape, (c) experimental first-order torsional modal shape, and (d) simulated first-order torsional modal shape.
Fig.7  Analytical target cascading-based two-level lightweight optimization formulation of electric bus body structure.
Fig.8  Specific section bars of the side structure.
Fig.9  Specific section bars of the roof structure.
Fig.10  Specific section bars of the chassis structure.
MethodTotal mass/kgBending stiffness/(N·mm?1)Torsional stiffness/(N·m·(° )?1)First-order bending frequency/HzFirst-order torsional frequency/Hz
Initial design2709277812120416.914.7
Results in Ref. [3]2642386412833416.914.8
Present study2660278514240617.014.9
Tab.1  Comparisons of optimization results with the initial design and Ref. [3]
Fig.11  Iteration curves of optimization history: (a) system-level optimization and (b) subsystem-level optimization.
Local variableValue/mmLocal variableValue/mmLocal variableValue/mm
X11.2X124.6X234.1
X23.3X134.9X243.6
X34.2X144.0X255.3
X43.1X154.1X263.8
X53.7X164.9X274.5
X64.1X173.6X282.7
X73.1X184.9X293.9
X84.3X194.1X304.3
X93.0X204.2X314.9
X103.1X214.1X323.7
X113.1X223.8X334.5
Tab.2  Results of local variables in subsystem levels
Fig.12  Loads on the present electric bus body structure.
Fig.13  Bending stress distributions of (a) the aluminum alloy structure and (b) the high strength steel structure.
Fig.14  Torsional stress distributions of (a) the aluminum alloy structure and (b) the high strength steel structure.
Structural materialMaximum validated stress/MPaYield stress/MPa
Bending caseTorsional case
6061T6108.5127.7160
QSTE700TM353.2318.4492
Tab.3  Comparisons of validated stresses and yield stresses
a1,a2,a3Scaled material properties of the side structure, roof structure, and chassis structure, respectively
AScaled material properties vector
cConsistency index
dcL,dcUMaximum displacements of the chassis structure, where “L” and “U” indicate that the performance is evaluated in the subsystem level and the system level, respectively
Ei,Eo (i = 1,2,3)Scaled Young’s modulus of each sub-structure and the base Young’s modulus, respectively
fijLocal objective function in ATC
fr,torsionL,fr,torsionUFirst-order torsional frequencies of the roof structure, where “L” and “U” indicate that the performance is evaluated in the subsystem level and the system level, respectively
fs,bendL,fs,bendUFirst-order bending frequencies of the side structure, where “L” and “U” indicate that the performance is evaluated in the subsystem level and the system level, respectively
ftorsionFirst-order torsional frequency of the entire structure
Fsys,Fsub,iEntire structural performance and sub-structural performance, respectively
gij,gInequality constraint
hij,hEquality constraint
M,Msub,iMass of the entire structure and the sub-structure, respectively
Mc,Mr,MsMass of the chassis structure, roof structure, and side structure, respectively
PijSubsystem-level problem in ATC
rijResponse fed back to the superior level in ATC
tijLocal objective assigned from the superior level in ATC
vLagrangian multiplier vector
wQuadratic punishment weight vector
Xc,Xr,XsLocal design variable vector of the chassis structure, roof structure, and side structure, respectively
XijLocal design variable vector in ATC
XsysGlobal design variable vector of the entire electric bus body structure
X1?X9Thicknesses of specific section bars in the side structure
X10?X20Thicknesses of specific section bars in the roof structure
X21?X33Thicknesses of specific section bars in the chassis structure
βRelaxation factor
εConvergence threshold
ρi,ρo (i = 1,2,3)Scaled density of each sub-structure and the base density, respectively
?(c) Penalty-based coordinated function
  
1 A C Olivera , J M García-Nieto , E Alba . Reducing vehicle emissions and fuel consumption in the city by using particle swarm optimization. Applied Intelligence, 2015, 42(3): 389–405
https://doi.org/10.1007/s10489-014-0604-3
2 L Li , X Y Wang , J Song . Fuel consumption optimization for smart hybrid electric vehicle during a car following process. Mechanical Systems and Signal Processing, 2017, 87(1): 17–29
https://doi.org/10.1016/j.ymssp.2016.03.002
3 C L Fu , Y C Bai , C Lin , W W Wang . Design optimization of a newly developed aluminum steel multi-material electric bus body structure. Structural and Multidisciplinary Optimization, 2019, 60(5): 2177–2187
https://doi.org/10.1007/s00158-019-02292-w
4 L B Duan , H B Jiang , H H Li , N C Xiao . Crashworthiness optimization of VRB thin-walled structures under manufacturing constraints by the eHCA-VRB algorithm. Applied Mathematical Modelling, 2020, 80: 126–150
https://doi.org/10.1016/j.apm.2019.11.030
5 J Sobieszczanski-Sobieski , S Kodiyalam , R Y Yang . Optimization of car body under constraints of noise, vibration, and harshness (NVH), and crash. Structural and Multidisciplinary Optimization, 2001, 22(4): 295–306
https://doi.org/10.1007/s00158-001-0150-6
6 T Ide , M Otomori , J P Leiva , B C Watson . Structural optimization methods and techniques to design light and efficient automatic transmission of vehicles with low radiated noise. Structural and Multidisciplinary Optimization, 2014, 50(6): 1137–1150
https://doi.org/10.1007/s00158-014-1143-6
7 H Y Wang , H Xie . Multi-objective optimization of crashworthiness of vehicle front longitudinal beam. Structural and Multidisciplinary Optimization, 2020, 61(5): 2111–2123
https://doi.org/10.1007/s00158-019-02459-5
8 F Xiong , X H Zou , Z G Zhang , X H Shi . A systematic approach for multi-objective lightweight and stiffness optimization of a car body. Structural and Multidisciplinary Optimization, 2020, 62(6): 3229–3248
https://doi.org/10.1007/s00158-020-02674-5
9 M Kiani , A R Yildiz . A comparative study of non-traditional methods for vehicle crashworthiness and NVH optimization. Archives of Computational Methods in Engineering, 2016, 23(4): 723–734
https://doi.org/10.1007/s11831-015-9155-y
10 N Aulig , E Nutwell , S Menzel , D Detwiler . Preference-based topology optimization for vehicle concept design with concurrent static and crash load cases. Structural and Multidisciplinary Optimization, 2018, 57(1): 251–266
https://doi.org/10.1007/s00158-017-1751-z
11 D F Wang , C Xie , Y C Liu , W C Xu , Q Chen . Multi-objective collaborative optimization for the lightweight design of an electric bus body frame. Automotive Innovation, 2020, 3(3): 250–259
https://doi.org/10.1007/s42154-020-00105-1
12 Y Jung , S Lim , J Kim , S Min . Lightweight design of electric bus roof structure using multi-material topology optimization. Structural and Multidisciplinary Optimization, 2020, 61(3): 1273–1285
https://doi.org/10.1007/s00158-019-02410-8
13 H G Ou , X D Tang , J P Xiao , Y B Wang , Z M Ma . Lightweight body-in-white design driven by optimization technology. Automotive Innovation, 2018, 1(3): 255–262
https://doi.org/10.1007/s42154-018-0032-x
14 C Li , I Y Kim , J Jeswiet . Conceptual and detailed design of an automotive engine cradle by using topology, shape, and size optimization. Structural and Multidisciplinary Optimization, 2015, 51(2): 547–564
https://doi.org/10.1007/s00158-014-1151-6
15 H Qin , Z J Liu , H L Zhong , Y Liu , C Lv . Two-level multiple cross-sectional shape optimization of automotive body frame with exact static and dynamic stiffness constraints. Structural and Multidisciplinary Optimization, 2018, 58(5): 2309–2323
https://doi.org/10.1007/s00158-018-2025-0
16 C Xie , D F Wang . Multi-objective cross-sectional shape and size optimization of S-rail using hybrid multi-criteria decision-making method. Structural and Multidisciplinary Optimization, 2020, 62(6): 3477–3492
https://doi.org/10.1007/s00158-020-02651-y
17 W J Zuo . Bi-level optimization for the cross-sectional shape of a thin-walled car body frame with static stiffness and dynamic frequency stiffness constraints. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2015, 229(8): 1046–1059
https://doi.org/10.1177/0954407014551585
18 S De , K Singh , J Seo , R K Kapania , E Ostergaard , N Angelini , R Aguero . Lightweight chassis design of hybrid trucks considering multiple road conditions and constraints. World Electric Vehicle Journal, 2021, 12(1): 3
https://doi.org/10.3390/wevj12010003
19 S B Lu , H G Ma , L Xin , W J Zuo . Lightweight design of bus frames from multi-material topology optimization to cross-sectional size optimization. Engineering Optimization, 2019, 51(6): 961–977
https://doi.org/10.1080/0305215X.2018.1506770
20 J T Bai , Y W Li , W J Zuo . Cross-sectional shape optimization for thin-walled beam crashworthiness with stamping constraints using genetic algorithm. International Journal of Vehicle Design, 2017, 73(1–3): 76–95
https://doi.org/10.1504/IJVD.2017.082582
21 W Zhong , R Y Su , L J Gui , Z J Fan . Multi-objective topology and sizing optimization of bus body frame. Structural and Multidisciplinary Optimization, 2016, 54(3): 701–714
https://doi.org/10.1007/s00158-016-1431-4
22 J Chen , Y J Wu , L M Zhang , X L He , S J Dong . Dynamic optimization design of the suspension parameters of car body-mounted equipment via analytical target cascading. Journal of Mechanical Science and Technology, 2020, 34(5): 1957–1969
https://doi.org/10.1007/s12206-020-0417-8
23 Z G Guo , Y F Zhang , X B Zhao , X Y Song . CPS-based self-adaptive collaborative control for smart production-logistics systems. IEEE Transactions on Cybernetics, 2021, 51(1): 188–198
https://doi.org/10.1109/TCYB.2020.2964301
24 P Guarneri , M Gobbi , P Y Papalambros . Efficient multilevel design optimization using analytical target cascading and sequential quadratic programming. Structural and Multidisciplinary Optimization, 2011, 44(3): 351–362
https://doi.org/10.1007/s00158-011-0630-2
25 S DorMohammadi , M Rais-Rohani . Exponential penalty function formulation for multilevel optimization using the analytical target cascading framework. Structural and Multidisciplinary Optimization, 2013, 47(4): 599–612
https://doi.org/10.1007/s00158-012-0861-x
26 H M Kim , W Chen , M M Wiecek . Lagrangian coordination for enhancing the convergence of analytical target cascading. AIAA Journal, 2006, 44(10): 2197–2207
https://doi.org/10.2514/1.15326
27 L F Etman , M Kokkolaras , A T Hofkamp , P Y Papalambros , J E Rooda . Coordination specification in distributed optimal design of multilevel systems using the χ language. Structural and Multidisciplinary Optimization, 2005, 29(3): 198–212
https://doi.org/10.1007/s00158-004-0467-z
28 T Qu , G Q Huang , V D Cung , F Mangione . Optimal configuration of assembly supply chains using analytical target cascading. International Journal of Production Research, 2010, 48(23): 6883–6907
https://doi.org/10.1080/00207540903307631
29 K Ramakrishnan , G Mastinu , M Gobbi . Multidisciplinary design of electric vehicles based on hierarchical multi-objective optimization. Journal of Mechanical Design, 2019, 141(9): 091404
https://doi.org/10.1115/1.4043840
30 H M Kim , D G Rideout , P Y Papalambros , J L Stein . Analytical target cascading in automotive vehicle design. Journal of Mechanical Design, 2003, 125(3): 481–489
https://doi.org/10.1115/1.1586308
31 T W Cui , W Z Zhao , C Y Wang , Y H Guo , H Y Zheng . Design optimization of a steering and suspension integrated system based on dynamic constraint analytical target cascading method. Structural and Multidisciplinary Optimization, 2020, 62(1): 419–437
https://doi.org/10.1007/s00158-019-02472-8
32 V Y Blouin , G M Fadel , I Q Haque , J R Wagner , H B Samuels . Continuously variable transmission design for optimum vehicle performance by analytical target cascading. International Journal of Heavy Vehicle Systems, 2004, 11(3–4): 327–348
https://doi.org/10.1504/IJHVS.2004.005454
33 N Kang , M Kokkolaras , P Y Papalambros , S Yoo , W Na , J Park , D Featherman . Optimal design of commercial vehicle systems using analytical target cascading. Structural and Multidisciplinary Optimization, 2014, 50(6): 1103–1114
https://doi.org/10.1007/s00158-014-1097-8
34 H M Kim , N F Michelena , P Y Papalambros , T Jiang . Target cascading in optimal system design. Journal of Mechanical Design, 2003, 125(3): 474–480
https://doi.org/10.1115/1.1582501
35 R Choudhary , A Malkawi , P Y Papalambros . Analytical target cascading in simulation-based building design. Automation in Construction, 2005, 14(4): 551–568
https://doi.org/10.1016/j.autcon.2004.11.004
36 Z J Li , M Kokkolaras , P Y Papalambros , S J Hu . Product and process tolerance allocation in compliant multi-station assembly using analytical target cascading. Journal of Mechanical Design, 2008, 130(9): 091701
https://doi.org/10.1115/1.2943296
37 S Tosserams , L F P Etmanl , P Y Papalambros , J E Rooda . An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers. Structural and Multidisciplinary Optimization, 2006, 31(3): 176–189
https://doi.org/10.1007/s00158-005-0579-0
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