Minimizing battery stress during hybrid electric vehicle control design: Real world considerations for model-based control development

Christopher Vagg, C J Brace, S Akehurst, Lloyd Ash

Research output: Contribution to conferencePaper

  • 4 Citations

Abstract

In a mild hybrid electric vehicle (HEV) aggressive use of the electrical powertrain is desired to maximize the benefits from hybridizing the vehicle, however this has negative consequences for battery management, battery state of health, and motor temperature. In this paper a control strategy cost function is presented which can minimize these negative effects without significantly affecting the achievable reduction in fuel consumption, and without requiring a detailed battery model or a motor thermal model. This concept is demonstrated on a retrofit HEV unit developed by Ashwoods Automotive, with a model validated using chassis dynamometer test data. Dynamic Programming (DP) is used to optimize the controller, and some limitations of DP which are not often recognized are discussed.

Conference

ConferenceVPPC 2013: The 9th IEEE Vehicle Power and Propulsion Conference
CountryChina
CityBeijing
Period15/10/1318/10/13

Fingerprint

Hybrid vehicles
Dynamic programming
Powertrains
Dynamometers
Chassis
Fuel consumption
Cost functions
Health
Controllers
Temperature

Keywords

  • control
  • battery health
  • battery management system (BMS)
  • battery stress
  • dynamic programming
  • hybrid electric vehicle (HEV)
  • real world
  • retrofit
  • simulation

Cite this

Vagg, C., Brace, C. J., Akehurst, S., & Ash, L. (2013). Minimizing battery stress during hybrid electric vehicle control design: Real world considerations for model-based control development. 329-334. Paper presented at VPPC 2013: The 9th IEEE Vehicle Power and Propulsion Conference, Beijing, China. https://doi.org/10.1109/VPPC.2013.6671713

Minimizing battery stress during hybrid electric vehicle control design : Real world considerations for model-based control development. / Vagg, Christopher; Brace, C J; Akehurst, S; Ash, Lloyd.

2013. 329-334 Paper presented at VPPC 2013: The 9th IEEE Vehicle Power and Propulsion Conference, Beijing, China.

Research output: Contribution to conferencePaper

Vagg, C, Brace, CJ, Akehurst, S & Ash, L 2013, 'Minimizing battery stress during hybrid electric vehicle control design: Real world considerations for model-based control development' Paper presented at VPPC 2013: The 9th IEEE Vehicle Power and Propulsion Conference, Beijing, China, 15/10/13 - 18/10/13, pp. 329-334. https://doi.org/10.1109/VPPC.2013.6671713
Vagg, Christopher ; Brace, C J ; Akehurst, S ; Ash, Lloyd. / Minimizing battery stress during hybrid electric vehicle control design : Real world considerations for model-based control development. Paper presented at VPPC 2013: The 9th IEEE Vehicle Power and Propulsion Conference, Beijing, China.6 p.
@conference{1b5d4c016df640ef8c67afa312264a9b,
title = "Minimizing battery stress during hybrid electric vehicle control design: Real world considerations for model-based control development",
abstract = "In a mild hybrid electric vehicle (HEV) aggressive use of the electrical powertrain is desired to maximize the benefits from hybridizing the vehicle, however this has negative consequences for battery management, battery state of health, and motor temperature. In this paper a control strategy cost function is presented which can minimize these negative effects without significantly affecting the achievable reduction in fuel consumption, and without requiring a detailed battery model or a motor thermal model. This concept is demonstrated on a retrofit HEV unit developed by Ashwoods Automotive, with a model validated using chassis dynamometer test data. Dynamic Programming (DP) is used to optimize the controller, and some limitations of DP which are not often recognized are discussed.",
keywords = "control, battery health, battery management system (BMS), battery stress, dynamic programming, hybrid electric vehicle (HEV), real world, retrofit, simulation",
author = "Christopher Vagg and Brace, {C J} and S Akehurst and Lloyd Ash",
year = "2013",
doi = "10.1109/VPPC.2013.6671713",
language = "English",
pages = "329--334",
note = "VPPC 2013: The 9th IEEE Vehicle Power and Propulsion Conference ; Conference date: 15-10-2013 Through 18-10-2013",

}

TY - CONF

T1 - Minimizing battery stress during hybrid electric vehicle control design

T2 - Real world considerations for model-based control development

AU - Vagg, Christopher

AU - Brace, C J

AU - Akehurst, S

AU - Ash, Lloyd

PY - 2013

Y1 - 2013

N2 - In a mild hybrid electric vehicle (HEV) aggressive use of the electrical powertrain is desired to maximize the benefits from hybridizing the vehicle, however this has negative consequences for battery management, battery state of health, and motor temperature. In this paper a control strategy cost function is presented which can minimize these negative effects without significantly affecting the achievable reduction in fuel consumption, and without requiring a detailed battery model or a motor thermal model. This concept is demonstrated on a retrofit HEV unit developed by Ashwoods Automotive, with a model validated using chassis dynamometer test data. Dynamic Programming (DP) is used to optimize the controller, and some limitations of DP which are not often recognized are discussed.

AB - In a mild hybrid electric vehicle (HEV) aggressive use of the electrical powertrain is desired to maximize the benefits from hybridizing the vehicle, however this has negative consequences for battery management, battery state of health, and motor temperature. In this paper a control strategy cost function is presented which can minimize these negative effects without significantly affecting the achievable reduction in fuel consumption, and without requiring a detailed battery model or a motor thermal model. This concept is demonstrated on a retrofit HEV unit developed by Ashwoods Automotive, with a model validated using chassis dynamometer test data. Dynamic Programming (DP) is used to optimize the controller, and some limitations of DP which are not often recognized are discussed.

KW - control

KW - battery health

KW - battery management system (BMS)

KW - battery stress

KW - dynamic programming

KW - hybrid electric vehicle (HEV)

KW - real world

KW - retrofit

KW - simulation

UR - http://www.vppc.org/

UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6671713

UR - http://dx.doi.org/10.1109/VPPC.2013.6671713

U2 - 10.1109/VPPC.2013.6671713

DO - 10.1109/VPPC.2013.6671713

M3 - Paper

SP - 329

EP - 334

ER -