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Real World Data Driven PHEV System Optimization Tool Based on Engine Cold Start Standard Disclosure Number: IPCOM000245146D
Publication Date: 2016-Feb-12
Document File: 2 page(s) / 171K

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The Prior Art Database

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Real World Data Driven PHEV System Optimization Tool Based on Engine Cold Start

Engine cold start emission is a big issue for PHEV models which pull up engine to assist motor for high power demand in EV mode driving. The cold start emission is multiple times of that warm start due to the inefficient catalysts under low temperature. For a PHEV powertrain pack, the power combination of engine and electric battery becomes critical for the PHEV design. Besides, real world driving patterns have great impact on engine cold start event performance. It is quite meaningful to design an optimization tool that can search the optimal system power combination based on real world data.


A vehicle simulation model is developed based on vehicle dynamics and power loss maps to calculate the power demand from the battery pack given the vehicle speed profiles. The model can calculate the average No. of cold starts of PHEV model/100 mi, assuming that T is the cool down period for the engine. The invention here is to use this simulation model to further develop a system optimization tool given the cold start target, and battery cost constraint. Real world customer driving data can be used as the main input to get customer driving pattern based estimation results, e.g. big data of European customers or NA customers. The schematic diagram is shown in Figure
1. Given the real world driving cycles, and pre-set cold start target, a minimum power Pmin can be found for the

PHEV battery power pack to make sure the designed model can have cold start performance better than target.

System Optimization for Electric Power Pack


Figure 1. System Optimization of PHEV based on Real World Driving Pattern

On one hand, increasing the battery power will cause higher cost of PHEV. However, higher battery power can lower the engine cold start frequency in EV mode driving. Thus, it is useful to find a trade-off between the two objectives. The optimization problem can be described as follows.




 (2)




where J is the total cost in the cost function, α and β are coefficients, fcostis the cost of battery pack, fcs is the cost of cold start frequency, Pbattery is the battery power capability, which should be within a low limit Plow and a high limit Phigh (battery pack search...