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Power Management Strategy for the 48 V Mild Hybrid Electric Vehicle Based on the Charge-Sustaining Control

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Abstract

To enhance the 48 V mild hybrid electric vehicle performance using a smaller capacity and lower voltage battery than the full hybrid electric vehicle, a novel power management strategy needs to be established that considers the characteristics and limitations of the components. This paper proposes a charge-sustaining control strategy as a ground principle of the 48 V hybrid electric vehicle control for managing the battery state-of-charge (SOC) to stay near the most efficient regime. The base efficiency characteristics of the component models including engine, motor/generator, and battery are determined in the form of efficiency maps using the powertrain analysis tool. Then the control strategy is formulated as a nonlinear optimal regulation problem that meets two conflicting control objectives, such as fuel efficiency improvement and state-of-charge maintenance. The optimal regulation problem implements a discrete-time Hamilton-Jacobi-Bellman approach. The proposed strategy is evaluated by comparing with the reference strategy applying the Dynamic Programming (DP), i.e. a global optimal result, under urban dynamometer driving schedule and worldwide harmonized light duty test cycle. Through the evaluation, the fuel efficiency of the proposed strategy with three different electrical loads is slightly deteriorated at most by 5.03 % from the DP results with staying within a desirable SOC. This suggests that the proposed strategy is operating very closely to global optimal performances.

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Abbreviations

BSFC:

engine, brake specific fuel consumption, g/kWh

f bat,int.c :

battery, function for internal charge resistance

f bat,int.d :

battery, function for internal discharge resistance

f bat,oc :

battery, function for open circuit voltage

f eng,fuel :

engine, function for fuel efficiency

f mge :

M/G, function for electric power efficiency

f mgm :

M/G, function for mechanical power efficiency

H(·):

Hamiltonian function

I bat,int,c :

battery, internal charge current, A

I bat,int,d :

battery, internal discharge current, A

I bat,t :

battery, internal temperature, A

J(·):

cost function

mfuel :

engine, fuel consumption rate, kg/sec

n bat,cell :

battery, the number of cell

p :

lyapunov coefficient

P bat12 :

battery, power 12 V, W

P bat48 :

battery, power 48 V, W

P bat,max :

battery, maximum power, W

P dc12 :

DC/DC converter, outlet power (12 V power-net), W

P dc48 :

DC/DC converter, inlet power (48 V power-net), W

P dt :

drivetrain, power, W

P el :

electric loads, consumed power, W

P el12 :

electric loads, consumed power (12 V power-net), W

P el48 :

electric loads, consumed power (48 V power-net), W

P eng :

engine, power, W

P ma :

mechanical accessary, power, W

P mg :

M/G, power, W

P mgm :

M/G, mechanical power, W

P mge :

M/G, electrical power, W

Q bat :

battery, cell capacity, Ah

R bat,int,c :

battery, internal charge resistance, Ω

R bat,int,d :

battery, internal discharge resistance, Ω

SOC :

SOC

SOC 0 :

battery, SOC initial value

SOC e :

battery, SOC error between current and target SOC

SOC init :

battery, initial SOC

SOC tgt :

battery target SOC

T s :

calculation interval, sec

t bat,int :

battery, internal temperature, °C

u τ :

control input = τmg,dmd / τdrv,dmd

V(·):

value function in Hamilton-Jacobi-Bellman equation

V bat,oc :

battery, open circuit voltage, V

V bat,t :

battery, internal temperature, V

V dc :

DC/DC converter target voltage, V

V veh :

vehicle speed, km/h

ηbat,c :

battery, charge efficiency, %

ηbat,d :

battery, discharge efficiency, %

τdrv,dmd :

driver, torque demand, Nm

τeng :

engine, torque, Nm

τeng,dmd :

engine, torque demand, Nm

τeng,max :

engine, maximum permissible torque, Nm

τeng,min :

engine, minimum permissible torque, Nm

τmg :

M/G, torque, Nm

τmg,dmd :

M/G, torque demand, Nm

τmg,max :

M/G, maximum permissible torque, Nm

τmg,min :

M/G, minimum permissible torque, Nm

ωeng :

engine, rotational speed, rad/s

ωmg :

M/G, rotational speed, rad/s

References

  • Abu-Khalaf, M. and Lewis, F. L. (2005). Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach. Automatica 415, 779–791.

    Article  MathSciNet  MATH  Google Scholar 

  • Al-Tamimi, A., Lewis, F. L. and Abu-Khalaf, M. (2008). Discrete-time nonlinear HJB solution using approximate dynamic programming: Convergence proof. IEEE Trans. Systems, Man, and Cybernetics, Part B (Cybernetics) 38, 4, 943–949.

    Article  Google Scholar 

  • Anderman, M. (2013). Assessing the Future of Hybrid and Electric Vehicles: The 2014 xEV Industry Insider Report Based on Private Onsite Interviews with Leading Technologists and Executives. 2014 Edition.

    Google Scholar 

  • Argonne (2009). Autonomie v1210. UChicago Argonne, LLC.

    Google Scholar 

  • Argonne (2007). PSAT v6.2, Argonne National Laboratory.

    Google Scholar 

  • Bellman, R. E. and Dreyfus, S. E. (1962). Applied Dynamic Programming. Princeton University Press. Princeton, New Jersey, USA.

    Book  MATH  Google Scholar 

  • Benchetrite, D. (2013). Hybrid4all: A low voltage, low cost, mass-market hybrid solution. Proc. Int. Conf. Automotive 48 V Power Supply Systems, Berlin, Germany.

    Google Scholar 

  • Buchmann, I. (2014). Lithium-based Batteries. Battery University; Cadex Electronics Inc. http://batteryuniversity.com/learn/article/lithium_based_batteries

    Google Scholar 

  • Chen, Z. and Jagannathan, S. (2008). Generalized Hamilton-Jacobi-Bellman formulation -based neural network control of affine nonlinear discrete-time systems. IEEE Trans. Neural Networks 19, 1, 90–106.

    Article  Google Scholar 

  • Haddad, W. M., Chellaboina, V.-S., Fausz, J. L. and Abdallah, C. (1998). Optimal discrete-time control for non-linear cascade systems. J. Franklin Institute 335, 5, 827–839.

    Article  MathSciNet  MATH  Google Scholar 

  • Hou, C., Ouyang, M., Xu, L. and Wang, H. (2014). Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles. Applied Energy, 115, 174–189.

    Article  Google Scholar 

  • Kim, C. S., Park, K., Kim, H., Lee, G., Lee, K., Yang, H. J., Cho, H., Song, M. and Son, Y. (2013). 48 V power assist recuperation system (PARS) with a permanent magnet motor, inverter and DC-DC converter. Proc. IEEE Int. Future Energy Electronics Conf. (IFEEC), Tainan, Taiwan.

    Google Scholar 

  • Kim, N., Cha, S. and Peng, H. (2011). Optimal control of hybrid electric vehicles based on pontryagin's minimum principle. IEEE Trans. Control Systems Technology 19, 5, 1279–1287.

    Article  Google Scholar 

  • Kim, N., Cha, S. and Peng, H. (2012). Optimal equivalent fuel consumption for hybrid electric vehicles. IEEE Trans. Control Systems Technology 20, 3, 817–825.

    Article  Google Scholar 

  • Kirk, D. E. (2004). Optimal Control Theory: An Introduction. Dover Publications. Mineola, New York, USA.

    Google Scholar 

  • Lewis, F. L., Vrabie, D. L. and Syrmos, V. L. (2012). Optimal Control. 3rd edn. John Wiley & Sons. Hoboken, New Jersey, USA.

    Book  MATH  Google Scholar 

  • Liu, W. (2013). Introduction to Hybrid Vehicle System Modeling and Control. John Wiley & Sons. Hoboken, New Jersey, USA.

    Book  Google Scholar 

  • Mate, J.-L. (2013). 48 V eco-hybrid systems. Proc. European Conf. Nanoelectronics and Embedded Systems for Electric Mobility Toulouse, Toulouse, France.

    Google Scholar 

  • Naidu, D. S. (2002). Optimal Control Systems. Taylor & Francis. Boca Raton, Florida, USA.

    Book  Google Scholar 

  • Ornelas, F., Sanchez, E. N. and Loukianov, A. G. (2011). Discrete-time nonlinear systems inverse optimal control: A control Lyapunov function approach. Proc. IEEE Int. Conf. Control Applications (CCA), Denver, Colorado, USA.

    Google Scholar 

  • Pang, S., Farrell, J., Jie, D. and Barth, M. (2001). Battery state-of-charge estimation. Proc. IEEE American Control Conf., Arlington, Virginia, USA.

    Google Scholar 

  • Robert Bosch GmbH (2007). Bosch Automotive Handbook. John Wiley & Sons. Hoboken, New Jersey, USA.

    Google Scholar 

  • SAE International (2014). Hybrid Electric Vehicle (HEV) and Electric Vehicle (EV) Terminology. Surface Vehicle Information Report. J1715_201410.

    Google Scholar 

  • Sampathnarayanan, B., Onori, S. and Yurkovich, S. (2012). An optimal regulation strategy for energy management of hybrid electric vehicles. Proc. IEEE Conf. Decision and Control (CDC), Maui, Hawaii, USA.

    Google Scholar 

  • Saridis, G. N. and Lee, C.-S. G. (1979). An approximation theory of optimal control for trainable manipulators. IEEE Trans. Systems, Man, and Cybernetics 9, 3, 152–159.

    Article  MathSciNet  MATH  Google Scholar 

  • Sohn, J. (2015). Electric Power Management Strategy for 48V Mild Hybrid Electric Vehicles Based on a Chargesustaining Control Applying Hamilton-Jacobi-Bellman Approach. Ph. D. Dissertation. Hanyang University. Seoul, Korea.

    Google Scholar 

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Correspondence to Manbae Han.

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Sohn, J., Sunwoo, M., Min, K. et al. Power Management Strategy for the 48 V Mild Hybrid Electric Vehicle Based on the Charge-Sustaining Control. Int.J Automot. Technol. 20, 37–49 (2019). https://doi.org/10.1007/s12239-019-0004-0

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