Computer modelling of the automotive energy requirements for internal combustion engine and battery electric-powered vehicles

Bumby, J.R.; Clarke, Peter; Forster, I.

doi:10.1049/ip-a-1.1985.0059

Cited by 39 publications

(18 citation statements)

References 14 publications

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“…However, when estimating SOC under dynamic conditions, when the battery is charging or discharging, there is a significant voltage drop due the internal impedance, whereas when export power is reduced to zero, there is no current flow in the battery circuit and no voltage drop. Charging/discharging rates [52], battery age, state of health (SOH) [53] and environmental conditions (e.g. ambient temperature) also has been shown to have significant impact on this internal impedance value [54].…”

Section: Trial Results and Validationmentioning

confidence: 99%

“…Coulomb counting calculates battery SOC by integrating the charging/discharging current over time with adjustments for the rate of charge or discharge of the battery [52,63]. It requires few battery specifications and is easy to be implemented.…”

Section: Trial Results and Validationmentioning

confidence: 99%

“…When using the internal impedance technique information on the battery state of health (SOH) [51], charging/discharging current [52] and ambient temperatures are required. If this technique is applied, a wide range of impedance experiments are needed to carry out SOC estimation.…”

Section: Trial Results and Validationmentioning

confidence: 99%

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Design and analysis of electrical energy storage demonstration projects on UK distribution networks

et al. 2015

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h i g h l i g h t sResults of an EES system demonstration project carried out in the UK. Approaches to the design of trials for EES and observation on their application. A formalised methodology for analysis of smart grids trials. Validated models of energy storage. Capability of EES to connect larger quantities of heat pumps and PV is evaluated. a b s t r a c tThe UK government's CO 2 emissions targets will require electrification of much of the country's infrastructure with low carbon technologies such as photovoltaic panels, electric vehicles and heat pumps. The large scale proliferation of these technologies will necessitate major changes to the planning and operation of distribution networks. Distribution network operators are trialling electrical energy storage (EES) across their networks to increase their understanding of the contribution that it can make to enable the expected paradigm shift in generation and consumption of electricity.In order to evaluate a range of applications for EES, including voltage control and power flow management, installations have taken place at various distribution network locations and voltage levels. This article reports on trial design approaches and their application to a UK trial of an EES system to ensure broad applicability of the results. Results from these trials of an EES system, low carbon technologies and trial distribution networks are used to develop validated power system models. These models are used to evaluate, using a formalised methodology, the impact that EES could have on the design and operation of future distribution networks.

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Section: Trial Results and Validationmentioning

confidence: 99%

Section: Trial Results and Validationmentioning

confidence: 99%

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Design and analysis of electrical energy storage demonstration projects on UK distribution networks

et al. 2015

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“…See for example Kang et al (1999); Kolmanovsky et al (2005); Guzzella and Amstutz (1999); Lin et al (2003); Sundström et al (2008). There are also tools for modeling and simulation of HEVs which support the evaluation of the size of design parameters, VEHLIB (Trigui et al, 2004), AMESim, Dymola, JANUS (Bumby et al, 1985), SIMPLEV (Cole, 1991), ADVISOR (Wipke et al, 1999), Markel et al (2002), QSS-TB (Guzzella and Amstutz, 1999), HYSDEL (Torrisi and Bemporad, 2004), CAPSim (Fredriksson et al, 2006), ADAMS/Car, CARSim and others (Butler et al, 1999;Mierlo and Maggetto, 2001;Hayat et al, 2003;Liu and Peng, 2008). Instead, the contribution of this paper is the automation of the configuration evaluation.…”

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confidence: 99%

A methodology and a tool for evaluating hybrid electric powertrain configurations

Murgovski

Sjöberg

Fredriksson

2011

IJEHV

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This paper describes a methodology for automatic optimization of hybrid electric powertrains. This methodology is developed and implemented in a tool, CAPSimO, and the paper is written in the form of describing the tool. Given the user inputs, which are dynamic vehicle model, driving cycle and optimization criterion, the tool first produces a simplified powertrain model in a form of static maps, before dynamic programming is used to find an optimal power split which minimizes the chosen criterion. The tool does not require that the vehicle model is transparent, which makes it possible to work on models hidden for intellectual property reasons. The paper presents two examples of powertrain evaluation, in terms of fuel consumption, for a parallel and a parallel-series powertrain.

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“…See for example Kang et al (1999), Kolmanovsky et al (2005), Guzzella and Amstutz (1999), Lin et al (2003), Sundström et al (2008). There are also tools for modeling and simulation of HEVs which support the evaluation of the size of the design parameters, JANUS by Bumby et al (1985), SIMPLEV by Cole (1991), ADVISOR by Wipke et al (1999), Markel et al (2002), QSS-TB by Guzzella and Amstutz (1999), HYSDEL by Torrisi and Bemporad (2004), CAPSim by Fredriksson et al (2006), ADAMS/Car, CARSim and others, Kolmanovsky et al (2005), Butler et al (1999), Mierlo and Maggetto (2001), Hayat et al (2003), Liu and Peng (2008). Instead, the contribution of this paper is the automation of the configuration evaluation.…”

Section: Introductionmentioning

confidence: 99%

A tool for generating optimal control laws for hybrid electric powertrains

Murgovski

Sjöberg

Fredriksson

2010

IFAC Proceedings Volumes

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This paper describes the development of a tool for automatic generation of optimal power management strategies. Given the user inputs, which are dynamic vehicle model, driving cycle and optimization criterion, the tool first produces a simplified powertrain model in the form of static maps, before dynamic programming is used to find an optimal torque split which minimizes the chosen criterion. The tool does not require a transparent vehicle model, which makes it possible to work on models hidden for intellectual property reasons.

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Computer modelling of the automotive energy requirements for internal combustion engine and battery electric-powered vehicles

Cited by 39 publications

References 14 publications

Design and analysis of electrical energy storage demonstration projects on UK distribution networks

Design and analysis of electrical energy storage demonstration projects on UK distribution networks

A methodology and a tool for evaluating hybrid electric powertrain configurations

A tool for generating optimal control laws for hybrid electric powertrains

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