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Emerging heavy duty vehicle control systems increasingly rely on advance knowledge of the road topography, described by the longitudinal road grade. Highway road grade profiles are restricted by road design specifications to be piecewise affine. This characteristic is used herein to derive a method for road grade estimation based on standard on-vehicle sensors and optimal piecewise linear estimation through dynamic programming. The proposed method is demonstrated with on-road experiments. It is able to represent the road grade profile for two studied 15 km road sections, by 20 linear segments for each, with a root mean square error between 0.42 % and 0.55 % grade., QC 20120215

Emerging heavy duty vehicle control systems increasingly rely on advance knowledge of the road topography, described by the longitudinal road grade. Highway road grade profiles are restricted by road design specifications to be piecewise affine. This characteristic is used herein to derive a method for road grade estimation based on standard on-vehicle sensors and optimal piecewise linear estimation through dynamic programming. The proposed method is demonstrated with on-road experiments. It is able to represent the road grade profile for two studied 15 km road sections, by 20 linear segments for each, with a root mean square error between 0.42 % and 0.55 % grade., QC 20120215

Emerging heavy duty vehicle control systems increasingly rely on advance knowledge of the road topography, described by the longitudinal road grade. Highway road grade profiles are restricted by road design specifications to be piecewise affine. This characteristic is used herein to derive a method for road grade estimation based on standard on-vehicle sensors and optimal piecewise linear estimation through dynamic programming. The proposed method is demonstrated with on-road experiments. It is able to represent the road grade profile for two studied 15 km road sections, by 20 linear segments for each, with a root mean square error between 0.42 % and 0.55 % grade., QC 20120215

Emerging heavy duty vehicle control systems increasingly rely on advance knowledge of the road topography, described by the longitudinal road grade. Highway road grade profiles are restricted by road design specifications to be piecewise affine. This characteristic is used herein to derive a method for road grade estimation based on standard on-vehicle sensors and optimal piecewise linear estimation through dynamic programming. The proposed method is demonstrated with on-road experiments. It is able to represent the road grade profile for two studied 15 km road sections, by 20 linear segments for each, with a root mean square error between 0.42 % and 0.55 % grade., QC 20120215

Emerging heavy duty vehicle control systems increasingly rely on advance knowledge of the road topography, described by the longitudinal road grade. Highway road grade profiles are restricted by road design specifications to be piecewise affine. This characteristic is used herein to derive a method for road grade estimation based on standard on-vehicle sensors and optimal piecewise linear estimation through dynamic programming. The proposed method is demonstrated with on-road experiments. It is able to represent the road grade profile for two studied 15 km road sections, by 20 linear segments for each, with a root mean square error between 0.42 % and 0.55 % grade., QC 20120215

A model of a series HEV (Hybrid Electric Vehicle) including resistive losses in the energy storage and varying engine and generator efficiency is presented. The purpose is to study how system properties affect the optimal control of series HEVs. We show that there is an inherent trade-off in the control of such a system, based on the characteristics of the efficiency curve of the EGU (Engine and Generator Unit) together with the energy storage internal resistance, capacitance and energy level. The relation between these properties determines the optimal control of the EGU, and this is thus the mechanism behind all optimal control problems using a similar model setup. We illustrate our reasoning using deterministic dynamic programming where system properties are varied and the optimal control is studied., QC 20120104

A model of a series HEV (Hybrid Electric Vehicle) including resistive losses in the energy storage and varying engine and generator efficiency is presented. The purpose is to study how system properties affect the optimal control of series HEVs. We show that there is an inherent trade-off in the control of such a system, based on the characteristics of the efficiency curve of the EGU (Engine and Generator Unit) together with the energy storage internal resistance, capacitance and energy level. The relation between these properties determines the optimal control of the EGU, and this is thus the mechanism behind all optimal control problems using a similar model setup. We illustrate our reasoning using deterministic dynamic programming where system properties are varied and the optimal control is studied., QC 20120104

A model of a series HEV (Hybrid Electric Vehicle) including resistive losses in the energy storage and varying engine and generator efficiency is presented. The purpose is to study how system properties affect the optimal control of series HEVs. We show that there is an inherent trade-off in the control of such a system, based on the characteristics of the efficiency curve of the EGU (Engine and Generator Unit) together with the energy storage internal resistance, capacitance and energy level. The relation between these properties determines the optimal control of the EGU, and this is thus the mechanism behind all optimal control problems using a similar model setup. We illustrate our reasoning using deterministic dynamic programming where system properties are varied and the optimal control is studied., QC 20120104

A model of a series HEV (Hybrid Electric Vehicle) including resistive losses in the energy storage and varying engine and generator efficiency is presented. The purpose is to study how system properties affect the optimal control of series HEVs. We show that there is an inherent trade-off in the control of such a system, based on the characteristics of the efficiency curve of the EGU (Engine and Generator Unit) together with the energy storage internal resistance, capacitance and energy level. The relation between these properties determines the optimal control of the EGU, and this is thus the mechanism behind all optimal control problems using a similar model setup. We illustrate our reasoning using deterministic dynamic programming where system properties are varied and the optimal control is studied., QC 20120104

A model of a series HEV (Hybrid Electric Vehicle) including resistive losses in the energy storage and varying engine and generator efficiency is presented. The purpose is to study how system properties affect the optimal control of series HEVs. We show that there is an inherent trade-off in the control of such a system, based on the characteristics of the efficiency curve of the EGU (Engine and Generator Unit) together with the energy storage internal resistance, capacitance and energy level. The relation between these properties determines the optimal control of the EGU, and this is thus the mechanism behind all optimal control problems using a similar model setup. We illustrate our reasoning using deterministic dynamic programming where system properties are varied and the optimal control is studied., QC 20120104