PHEV energy management control with trip-oriented energy consumption preplanning

• US 9,409,563 B2
• Filed: 10/31/2013
• Issued: 08/09/2016
• Est. Priority Date: 10/31/2013
• Status: Expired due to Fees

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Yu, Hai, Wang, Qing, McGee, Ryan Abraham
• Primary Examiner(s): Shaawat, Mussa A
• Assistant Examiner(s): JHA, ABDHESH K
• Application Number:
  US14/068,726
• Publication Number:
  US 20150120107A1
• Time in Patent Office: 1,013 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: B60L 2240/622 by satellite navigation
• CPC Class Codes: B60L 2240/645 Type of road
• CPC Class Codes: B60L 2260/52 drive range estimation, e.g. of estimation of available travel distance
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 3/12 Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 53/14 Conductive energy transfer
• CPC Class Codes: B60W 20/00 Control systems specially adapted for hybrid vehicles hybrid vehicle design, B60K6/00; electric vehicles B60L
• CPC Class Codes: B60W 20/12 using control strategies taking into account route information estimation or calculation of non-directly measurable driving parameters B60W40/00
• CPC Class Codes: B60Y 2400/214 by power from domestic supply, e.g. plug in supplies
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 Electric energy management in electromobility
• CPC Class Codes: Y02T 90/14 Plug-in electric vehicles
• CPC Class Codes: Y02T 90/16 Information or communication technologies improving the operation of electric vehicles
• CPC Class Codes: Y10S 903/903 having energy storing means, e.g. battery, capacitor

Abstract

A driving pattern based plug-in hybrid electric vehicle (PHEV) energy consumption preplanning process enables a PHEV trip-oriented energy management control (TEMC) to utilize scalable levels of available trip foreknowledge in order to optimize the onboard energy (fuel and electricity) usage. The preplanning process generates an optimal battery state-of-charge (SOC) depletion profile for a given trip to be traveled by a PHEV. The preplanning process may generate the battery SOC profile using a driving pattern based dynamic programming (DP) algorithm. The TEMC controls the onboard energy usage in accordance with the battery SOC profile, which is optimized for the trip. The preplanning process makes use of spatial domain normalized drive power demand (SNDP) (or S-NDP) distributions in which each set of distributions is indicative of a respective driving pattern. The trip foreknowledge is used to select the driving pattern best representative of the driving process for the trip.

Claims

1. A system comprising:

a controller configured to control battery usage of a vehicle during a vehicle trip according to a battery state-of-charge (SOC) profile generated from dynamic programming of a fuel consumption rate and a battery electricity depletion rate based on a spatial domain normalized drive power (SNDP) distribution of a predefined driving pattern corresponding to the trip, the SNDP distribution being a drive power demand distribution of the driving pattern in a spatial domain with total propulsion energy for the trip is calculated by;

E_prop=∫

_O^TP_whldt=∫

_O^SP_sndpds,

wherein, P_whl is the time domain drive power demand,

T is the total trip time duration,

S is distance until charge,

P_sndp is the spatial domain normalized drive power.

2. The system of claim 1 wherein:

the driving pattern is representative of each of a plurality of driving processes having similar SNDP profiles.

3. The system of claim 1 wherein:

the controller is further configured to generate a library of driving patterns each having a corresponding SNDP distribution and each driving pattern associated with a driving process parameter.

4. The system of claim 3 wherein:

each driving pattern of the library of driving patterns is representative of a set of driving processes having similar SNDP profiles.

5. The system of claim 1 wherein:

the controller is further configured to select a driving pattern for each portion of the trip of the vehicle based on information associated with each portion of the trip, wherein each driving pattern has a corresponding SNDP distribution, and generate the battery SOC profile using dynamic programming with an energy consumption characteristic based on the SNDP distribution of each selected driving pattern and each portion of the trip associated with the selected driving pattern.