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As the number of electrical devices in modern vehicles increases, the battery becomes more critical component for the operation of vehicles. To ensure the startability of the vehicle, battery conditions such as state of charge and state of health should be properly monitored and maintained. To reduce walk-home incidents due to no-start situation, appropriate warning should be issued to the driver to advise necessary actions such as replacing or re-charging the battery. For the last couple of years, General Motors has studied and developed several battery health monitoring methods based on different battery health signatures. Yet, it is found that relying on a single method may lead to false alarm or misdetection due to lack of information or uncertainty. This paper develops the algorithm for more robust and reliable battery health monitoring and prognosis, by applying Evidence Theory to fuse different battery health signatures.

Field reprogramming of electronic control units (ECU) via the serial communication bus for feature upgrades, software fixes is an area which has potential cost impacts to the OEMs. Due to increasing software complexity, feature content, up-integration considerations for ECUs in next generation vehicle platforms the expectation is for flash memory requirements to increase significantly in the future. A reduced reprogramming cycle that builds on top of the existing system framework would be of interest from a cost and timing aspects. Additionally deployment of the next generation of telematics based remote programming techniques would also benefit from shorter reprogramming time in ECUs. An approach to address field reprogramming time would be to migrate to high baud rate communication protocols like FlexRay or Ethernet from the traditional CAN based systems currently in use.

One of the motivations to adopt the FlexRay communication protocol is the increased need to integrate active safety, time-critical features into the automotive domain. A deterministic communication protocol can only provide time bounds to guarantee data availability at the network level. Exploration of the timing analysis & function allocation options for application features need to consider effects due to software behaviors such as task preemptions and interrupts, which could skew the response times during execution in ECUs. The data processing and reaction time to network data in each ECU needs to be adjusted during the development phase to achieve real-time closed loop control in adherence with the timing requirements of the application. This paper provides an innovative approach in using FlexRay to harness the time synchronized nature of the protocol and apply its attributes to support timing assessment of distributed application functions in ECUs.