Abstract

The application of a 48-V mild hybrid powertrain system is one of the eco-friendly technologies for global CO2 reduction in the present sector of hybrid electric vehicles (HEVs). It is well known that the energy management strategy is crucial for improving the fuel economy of the HEVs. Therefore, the strengths and the limitations of dynamic programming (DP) global optimization strategy and adaptive equivalent consumption minimization strategy (ECMS) for this kind of powertrain system under new European driving cycle (NEDC) and federal test procedure (FTP75) driving cycles are discussed in this paper. The results of the DP global optimization solution have also been adopted as a reference for evaluating the degree of optimality of real-time controllers. The reference start of charge (SOC) was found to be a very important parameter for the real-time ECMS approach. Thus, an adaptive ECMS approach using the SOC obtained from DP global optimization algorithm as a reference SOC in real-time ECMS control was studied. The study results in this paper may provide some theoretical support for future energy management optimization of a 48-V mild hybrid parallel powertrain system.

References

1.
Liu
,
Z.
,
Ivanco
,
A.
, and
Filipi
,
Z. S.
,
2016
, “
Impacts of Real-World Driving and Driver Aggressiveness on Fuel Consumption of 48 V Mild Hybrid Vehicle
,”
SAE Int. J. Altern. Powertrains
,
5
(
2
),
249
258
10.4271/2016-01-1166.
2.
Bao
,
R.
,
Avila
,
V.
, and
Baxter
,
J.
,
2017
, “
Effect of 48 V Mild Hybrid System Layout on Powertrain System Efficiency and Its Potential of Fuel Economy Improvement
,”
SAE 2017-01-1175
.
3.
Vallur
,
A. R.
,
Khairate
,
Y.
, and
Awate
,
C.
,
2015
, “
Prescriptive Modeling, Simulation and Performance Analysis of Mild Hybrid Vehicle and Component Optimization
,”
SAE 2015-26-0010
.
4.
Abdellahi
,
A.
,
Rahimian
,
S. K.
,
Blizanac
,
B.
, and
Sisk
,
B.
,
2017
, “
Exploring the Opportunity Space for High-Power Li-Ion Batteries in Next-Generation 48 V Mild Hybrid Electric Vehicles
,”
SAE World Congress Experience
,
Detroit, MI
,
SAE 2017-01-1197
.
5.
Himelic
,
J. B.
, and
Kreith
,
F.
,
2011
, “
Potential Benefits of Plug-In Hybrid Electric Vehicles for Consumers and Electric Power Utilities
,”
ASME J. Energy Resour. Technol.
,
133
(
3
), p.
031001
. 10.1115/1.4004151
6.
Naidu
,
A.
,
Brittle
,
P.
,
Ma
,
X.
, and
Rutter
,
B.
,
2017
, “
Integrated Systems Engineering Approach for Incremental 48Volt Hybrid Technology Introduction
,”
SAE 2017-01-1603
.
7.
Kelly
,
J.
,
Scanes
,
P.
, and
Bloore
,
P.
,
2014
, “
Specification and Design of a Switched Reluctance 48 V Belt Integrated Starter Generator (B-ISG) for Mild Hybrid Passenger Car Applications
,”
SAE 2014-01-1890
.
8.
Xie
,
S.
,
Hu
,
X.
,
Qi
,
S.
, and
Lang
,
K.
,
2018
, “
An Artificial Neural Network-Enhanced Energy Management Strategy for Plug-In Hybrid Electric Vehicles
,”
Energy
,
163
, pp.
837
848
. 10.1016/j.energy.2018.08.139
9.
Sabri
,
M. F. M.
,
Danapalasingam
,
K. A.
, and
Rahmat
,
M. F.
,
2016
, “
A Review on Hybrid Electric Vehicles Architecture and Energy Management Strategies
,”
Renew. Sustainable Energy Rev.
,
53
, pp.
1433
1442
. 10.1016/j.rser.2015.09.036
10.
Sun
,
C.
,
Sun
,
F.
, and
He
,
H.
,
2017
, “
Investigating Adaptive-ECMS With Velocity Forecast Ability for Hybrid Electric Vehicles
,”
Appl. Energy
,
185
, pp.
1644
1653
. 10.1016/j.apenergy.2016.02.026
11.
Enang
,
W.
, and
Bannister
,
C.
,
2017
, “
Modelling and Control of Hybrid Electric Vehicles (A Comprehensive Review)
,”
Renew. Sustainable Energy Rev.
,
74
, pp.
1210
1239
. 10.1016/j.rser.2017.01.075
12.
Peng
,
J.
,
He
,
H.
, and
Xiong
,
R.
,
2017
, “
Rule Based Energy Management Strategy for a Series–Parallel Plug-In Hybrid Electric Bus Optimized by Dynamic Programming
,”
Appl. Energy
,
185
, pp.
1633
1643
. 10.1016/j.apenergy.2015.12.031
13.
Han
,
J.
,
Park
,
Y.
, and
Kum
,
D.
,
2014
, “
Optimal Adaptation of Equivalent Factor of Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Under Active State Inequality Constraints
,”
J. Power Sources
,
267
, pp.
491
502
. 10.1016/j.jpowsour.2014.05.067
14.
Chen
,
S. Y.
,
Hung
,
Y. H.
,
Wu
,
C. H.
, and
Huang
,
S. T.
,
2015
, “
Optimal Energy Management of a Hybrid Electric Powertrain System Using Improved Particle Swarm Optimization
,”
Appl. Energy
,
160
, pp.
132
145
. 10.1016/j.apenergy.2015.09.047
15.
Kessels
,
J. T. B. A.
,
Koot
,
M. W. T.
,
van den Bosch
,
P. P. J.
, and
Kok
,
D. B.
,
2008
, “
Online Energy Management for Hybrid Electric Vehicles
,”
IEEE Trans. Veh. Technol.
,
57
(
6
), pp.
3428
3440
. 10.1109/TVT.2008.919988
16.
Chasse
,
A.
,
Corde
,
G.
,
Del Mastro
,
A.
, and
Perez
,
F.
,
2010
, “
Online Optimal Control of a Parallel Hybrid With After-Treatment Constraint Integration
,”
IEEE Vehicle Power and Propulsion Conference
,
Lille, France
,
Sept. 1–3
.
You do not currently have access to this content.