HEV Cruise Control Strategy on GPS ( Navigation ) Information

The main objectives of this paper are to demonstrate the development of the hybrid vehicle control system with the GPS (navigation) system of a vehicle travelling a pre-planned driving route. To verify the improvement in vehicle fuel economy, we developed the forward-facing simulator, which can be applied to the proposed HEVs control system. The proposed HEVs control system recognizes upcoming driving patterns because it has terrain (uphill, downhill) and speed information. The controller calculates the parameters related to pattern recognition during a sampling time to choose a comparable driving cycle and to classify into three driving modes (Urban/ Extra-urban/ Highway mode). Moreover, a dynamic programming approach is proposed to obtain the optimal fuel economy and the state of charge (SOC) trajectory. For this approach, we developed a rule-based controller to manage the battery SOC according to the target SOC range. The amount of the target SOC range depends on the driving pattern recognition during a specific time period. The conventional HEV control system sustains the battery SOC within a limited range. Compared with the conventional controller, the proposed control system, by using road slope and speed information, gives results that confirm improved fuel economy.


Introduction
As an alternative to conventional vehicles, Hybrid Electric Vehicles (HEVs) can achieve better fuel economy and reduce pollution emissions.HEVs can use gasoline (or diesel) engine energy to generate electric energy through a motor-generator system with a rechargeable energy storage system.Compared to similar conventional vehicles, Hybrids has more advantages: it can operate the combustion engine closer to the highest efficiency range; can capture the wasted energy from deceleration through the regenerative braking system and convert it into electrical power; and can use the electric power to charge the batteries, which in turn can propel the electric motor for hill climbing, acceleration, and high power demand conditions.
The conventional HEV control system commonly monitors and maintains the battery state of charge (SOC) at the upper (approximately 60%) and the lower (approximately 40%) limit range.The battery needs to provide propulsive power for unexpected hill climbing and/or acceleration conditions.The battery requires a sufficient charge storage space for regenerating mechanical potential and/or kinetic energy during downhill and/or deceleration conditions.When the battery SOC falls below the lower limits, in the conventional control system, the engine power will engage and provide electrical charging power to the battery immediately via the commands that increase engine torque and speed.However, this conventional control system is advantageous for obtaining adequate results when the vehicle operates on a flat terrain travelling route (without road slope information).When HEVs are operating at a relatively high altitude and/or high speed, more power may be required and the battery may be discharged faster than when HEVs are operating at a relatively low altitude and/or low speed.In such a case, the battery SOC control using GPS (navigation) information can be applied.
Arun Rajagopalan investigated an instantaneous, control strategy for a parallel HEV.This strategy continuously modifies itself based on future driving conditions, when speed or elevation changes.Traffic and elevation information from GPS is used in an adaptive fuzzy logic controller [3].Erik Hellstrom studied how information about future road slopes can be utilized in a heavy truck.A model predictive control scheme is used to control the longitudinal behavior of the vehicle.And computer simulations showed that fuel consumption can be reduced by 2.5% [2].Yoshitaka Deguchi proposed a charge/discharge control system, which uses fuel efficiency as the control parameter.The parameter is updated according to the magnitude of the difference between the predicted SOC and the actual SOC and whenever traffic information is updated.Fuel economy was improved by 3.5% on the test route, by 7.8% on the downhill route and 0.5% on a congested route [4].
Accordingly, we developed the forward-facing simulator,which can be applied to the proposed HEVs control system.The proposed HEVs control system recognizes future driving patterns based on information about the travelling terrain (uphill, downhill) and speed.This rule-based controller manages the battery SOC according to the incoming target range.The target SOC range depends on pattern recognition during a specific time period.Moreover, a dynamic programming approach is proposed to obtain the optimal fuel economy and the optimal SOC trajectory.The results of the proposed control system are presented and conclusions are described in comparison to those of the conventional control system.

Road slope and vehicle speed information
The Global Positioning System (GPS) is a common vehicle navigation system.It offers a signal that contains the longitudinal and latitudinal position information of the vehicle on a route.When the departure and destination points are specified, the driver drives the hybrid vehicle along the planned route and obtains information along this path from the GPS.Once the vehicle receives information on the future driving condition in advance, the control system can operate the vehicle to reduce fuel consumption.For example, Figure1 shows an illustration of the SOC control management scheme with or without slope information.When an HEV is to travel on an uphill in the near future at a relatively high speed and high altitude (c-d region), the motor/generator (MG) will require more power to satisfy the power demand and the battery SOC may drop.In the conventional control system, if the battery SOC falls below the threshold lower limit, the motor/generator (MG) will prevent the discharge of the battery.

Conclusions
This paper presents the hybrid vehicle control algorithm with GPS (navigation) system for vehicles travelling a pre-planned driving route.To verify fuel economy improvement, we developed the forward-facing simulator applicable to the proposed HEVs control system.
To predict the future state of a vehicle, the proposed HEV control system recognizes the future driving patterns from information on the terrain (uphill, downhill) and speed.The controller calculates the parameters during a sampled time in order to choose a driving cycle and to classify three driving modes (Urban/Extraurban/Highway mode) This rule-based controller commands manage the battery SOC according to an incoming target range.The target SOC range depends on pattern recognition during specific time period.Moreover, a dynamic programming approach was proposed to obtain the optimal fuel economy and the optimal?SOC trajectory.Dynamic programming is well-known for finding the best solution for optimal control of the HEV.In the simulation, ideal fuel efficiency can be expected to be approximately 4.84% enhancement.The optimal SOC trajectory at the transition of road section boundaries suggests rules of control strategy based on the ideas described.In addition, the backward-facing simulation (DP) and the forward-facing simulation results were compared to verify the reliability of the proposed forwardfacing control system and vehicle components model.Compared with the conventional controller, the proposed control system gives results that confirm improved fuel economy, using road slope and speed information in the simulation.

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