Zero emission freight trucks are needed to both improve public health and reduce global greenhouse gas emissions but at the same time are generally believed to be uneconomical.However, recent dramatic declines in battery prices and improvement in their energy density have created opportunities for battery-electric trucking today that were seldom anticipated just a few years ago. At the current global average battery pack price of $135 per kilowatt-hour (kWh) (realizable when procured at scale), a Class 8 electric truck with 375-mile range and operated 300 miles per day when compared to a diesel truck offers about 13% lower total cost of ownership (TCO) per mile, about 3-year payback and net present savings of about US $200,000 over a 15-year lifetime. This is achieved with only a 3% reduction in payload capacity. Even this small penalty can be reversed cost-effectively through light-weighting, in any case, only matters for a small fraction of trucks which regularly utilize their maximum payload. Electric trucks appear poised to also meet the performance demands for a large share of regional and long-haul trucking today. The estimated average distance traveled between 30minute driver breaks is 150 miles and 190 miles for regional-haul and long-haul trucks respectively in the US. Thirty minutes of charging using 500 kW or mega-Watt scale fastchargers would add sufficient range without impairing operations and economics of freight movement. However, as with almost any clean technology, higher upfront capital costs of both vehicles and charging infrastructure are major barriers when electric trucking is in its infancy. Without strong policy support, coordinated investments in both vehicle manufacturing and fuel infrastructure will not be forthcoming on the scale needed to harness the true potential of battery electric trucks.

Objective: Ensure that GEB technology, building performance, and customer cost-benefit data are easily accessible, and improve and standardize analytical methods.GEB field performance assessments and metrics are needed to enable grid operators to trust the ability of demand flexibility to reliably deliver grid services. This includes developing and evaluating the use of standard baseline M&V methods to measure demand flexibility, as well as and collecting field data on demand flexibility building performance. Also, building owners and operators are unwilling to invest in technology without a clear value proposition based on proven technology benefits. Demand flexibility benchmark data sets, load shapes, and metrics are needed across all building sectors to provide relevant, comprehensive data for GEB technology performance evaluation. To draw meaningful conclusions from the data that can be relied upon by grid operators, utilities, and customers, there is a need for statistically significant data sets at scale and across different dimensions of building type and time (e.g., hourly, daily, annually). Key implementation challenges include managing privacy and cybersecurity with widespread data accessibility.Users may have privacy or security concerns related to the transmission and storage of whole-building and specific end-use equipment and system data. Utilities, aggregators, technology providers, and DER service providers may also worry about liability related to sharing customer data.Additionally, providing granular data would require robust data storage systems. Technology providers must carefully balance these concerns with the need to provide easy access to data for customers, grid operators, aggregators, and performance evaluators. A challenge specifically related to analytical methods is establishing appropriate baselines, particularly with multiple programs and rate designs, and when demand flexibility is used routinely. Key ActionsDevelop standard metrics and methods for data collection, data analysis, and measurement and verification (M&V) of demand flexibility technologies and strategies. M&V methods for EE and DR have been developed for many years and are evolving toward increased use of automation and hourly meter data (e.g., "advanced M&V" or "M&V 2.0" with and without control groups). Similarly, hourly data, and in some cases sub-hourly data, and advanced telemetry are needed for demand flexibility market settlement. These metrics along with new and scalable evaluation methods must also be developed for the full complement of grid services that buildings can provide. Simplified approaches are needed for demand flexibility performance assessments at the whole building and system/equipment level and for multiple demand flexibility modes (e.g., shed and shift in combination). landmark    Expand EE benchmark dataset and benchmarking tools to incorporate demand flexibility. There is a long practice of collecting total energy use normalized by floor area to compare the energy performance of buildings...

We illustrate that when the benefits of recent dramatic declines in Lithium battery prices are fully realized, the total cost of ownership of urban (intra-city) electric buses is lower than that for diesel buses in India even without subsidies. Factoring in the air quality benefits, projected reductions in the cost of batteries and solar electricity, it becomes evident that transitioning to an all-electric bus fleet presents an enormous opportunity for India to reduce urban air pollution while improving the finances of urban bus transit agencies. Applying relevant lessons from the policy ecosystem that delivered substantial price reductions and large-scale rapid deployment of solar PV and LEDs could achieve similar outcomes for battery electric buses. Well-designed high volume auctions and clear long term ambitious targets could achieve rapid electrification with little net public subsidy in the long-run.

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