Sorry, you need to enable JavaScript to visit this website.

Embedded Fiber Optic Sensing System for Battery Packs

Palo Alto Research Center (PARC)
ARPA-E Award: 
Palo Alto, CA
Project Term: 
10/01/2012 to 03/06/2017
Project Status: 
Image of PARC's technology
Critical Need: 

Today's electric vehicle batteries are expensive and prone to unexpected failure. Batteries are complex systems, and developing techniques to cost-effectively monitor and manage important performance measures while predicting battery cell degradation and failure remains a key technological challenge. There is a critical need for breakthrough technologies that can be practically deployed for superior management of both electric vehicle batteries and renewable energy storage systems.

Project Innovation + Advantages: 

Palo Alto Research Center (PARC) is developing new fiber optic sensors that would be embedded into batteries to monitor and measure key internal parameters during charge and discharge cycles. Two significant problems with today's best batteries are their lack of internal monitoring capabilities and their design oversizing. The lack of monitoring interferes with the ability to identify and manage performance or safety issues as they arise, which are presently managed by very conservative design oversizing and protection approaches that result in cost inefficiencies. PARC's design combines low-cost, embedded optical battery sensors and smart algorithms to overcome challenges faced by today's best battery management systems. These advanced fiber optic sensing technologies have the potential to dramatically improve the safety, performance, and life-time of energy storage systems.

Potential Impact: 

If successful, PARC's compact fiber optic sensing system would actively assess the battery's state and health with high accuracy while in use to avoid degradation and/or failure during use. Additionally, the new system could help to reduce design oversizing by more than 25%, resulting in a significant reduction in the price of electrical energy storage systems.


Advances in energy storage management could reduce the cost and increase the adoption of electric vehicles and renewable energy storage technologies, which in turn would reduce our nation's dependence on foreign sources of energy.


Improving the reliability and safety of electric vehicles and renewable energy storage facilities would enable more widespread use of these technologies, resulting in a substantial reduction in carbon dioxide emissions.


Enabling alternatives to conventional sources of energy could insulate consumers, businesses, and utilities from unexpected price swings.

Innovation Update: 
(As of May 2016) 
Since the project’s inception in October 2012, the PARC team has developed a Battery Management System (BMS) using the innovative and advantageous approach of embedding fiber-optics (FO). PARC’s optical sensor technology allows for downsizing the battery pack, saving hundreds of dollars and reducing the cost of the BMS itself. As of October 2015, the team led by PARC initiated testing and evaluation of the Smart Embedded Network of Sensors with Optical Readout (SENSOR) system’s application to a plug-in hybrid electric vehicle module. The PARC BMS prototype has successfully demonstrated reduced pack weight by 5 percent and reduced cost by 10 percent. The module will continue to undergo a number of experiments to continue to prove the system’s worth for future hybrid electric and electric vehicles. 
The PARC team’s transformational approach to BMS was largely enabled by the team’s newly developed low-cost, high-accuracy FO wavelength shift detector. The shift detector based system has a multitude of technical benefits and has the potential to reduce overall system cost by 10-15 percent. PARC embedded the FO sensors and completed an initial demonstration to measure the battery state, meaning state-of-charge, state-of-health, and state-of-power, generally referred to as SOX. The system combines embedded FO sensors and smart algorithms to monitor internal cell parameters, estimate SOX information, and predict remaining battery energy. The sensor network utilizes FO sensors attached to and embedded within the battery cell itself and advanced battery algorithms enabling 2.5 percent or better accuracy in SOX estimation. 
For a detailed assessment of the PARC team's project and impact, please click here.

ARPA-E Program Director: 
Dr. Patrick McGrath
Project Contact: 
Ajay Raghavan
LG Chem Power, Inc.
Release Date: